Resources

Strategic analysis of data lakehouse platform updates: serverless compute economics, attribute-based access control, and cross-cloud data sharing for enterprise competitive advantage.

November 2025 marked a pivotal shift in data lakehouse architecture. Seven platform updates fundamentally changed how enterprises approach data infrastructure economics, governance at scale, and AI-powered analytics.

The latest Databricks lakehouse platform updates reveal a fundamental shift in how enterprises must think about data infrastructure cost optimization. This isn’t about new features, it’s about three strategic imperatives converging:

serverless compute economics;
data governance strategy at scale;
and AI-powered self-service that actually works.

Why this matters now: Enterprises are simultaneously under pressure to reduce infrastructure costs, accelerate AI adoption, and maintain regulatory compliance. These updates address all three, but only if you understand what they enable strategically, not just technically.

7 Strategic Updates That Redefine Data Lakehouse Economics

1. Serverless Compute Expansion: Production-Grade JAR Support Transforms Cost Structure

Strategic Principle:Serverless data platform economics only work when they cover your actual production workloads, not just experimental notebooks.

The Enterprise Reality: Most data engineering teams run mission-critical pipelines in Scala or Java JARs. Until now, those workloads required provisioned clusters, meaning you paid for capacity, not consumption. November’s update extends serverless to JAR tasks, fundamentally enabling Databricks cost optimization across production data pipelines.

Decision Framework: If your data engineering team runs Scala/Java pipelines that aren’t latency-critical, serverless JAR tasks can reduce data infrastructure cost by 30-40% while eliminating cluster management overhead. If your pipelines require sub-second response times or have specialized compute requirements, dedicated clusters still make sense.

Key Takeaway: This bridges the gap between “serverless for data science” and “serverless for production engineering” enabling true pay-per-use economics across your entire data lakehouse architecture.

Contrast: Legacy approach required separate infrastructure strategies for development vs. production, creating cost inefficiency and operational complexity.

2. Attribute-Based Access Control: Governance Architecture That Scales to Enterprise Reality

Strategic Principle:Attribute-based access control at enterprise scale requires automation, not manual permission management.

The Enterprise Reality: Traditional table-level permissions break down when you have 10,000+ tables and need different access rules based on user attributes (department, role, location, clearance level). ABAC in Unity Catalog (now public preview) automates access control by defining policies once and applying them dynamically, a cornerstone of modern data governance strategy.

Decision Framework: If you’re managing data access across multiple business units, geographies, or regulatory jurisdictions, or if your data governance team spends more time managing permissions than defining strategy, ABAC represents a fundamental operational improvement aligned with data governance standards.

Key Takeaway: ABAC shifts governance from reactive permission management to proactive policy definition. The fact that ABAC-secured assets can now be shared like standard tables means governance no longer creates friction in data collaboration, addressing key benefits of data governance without operational overhead.

Contrast: Manual permission management doesn’t scale beyond hundreds of users and dozens of tables. ABAC scales to thousands of users and tens of thousands of data assets.

3. Research Agent: Business Intelligence Architecture That Actually Reasons

Strategic Principle: Self-service analytics fails when users can’t ask complex, multi-step questions. AI that reasons changes the data management strategy equation.

The Enterprise Reality: Most BI tools give you answers to questions you already know how to ask. Genie’s Research Agent uses multi-step reasoning to tackle complex business questions that require hypothesis testing and progressive analysis, the kind of questions that typically require a data analyst. This aligns with emerging AI business intelligence capabilities.

Decision Framework: If your business users regularly escalate “simple” questions to data teams because those questions require multiple joins, time-series analysis, or comparative logic, Research Agent can deflect 40-60% of those requests while maintaining analytical rigor.

Key Takeaway: This isn’t chatbot BI. It’s reasoning-powered analytics that can handle the complexity business users actually face. The starter questions and auto-suggested benchmarks reduce the “blank page” problem that kills most self-service initiatives.

Contrast: Traditional BI requires users to know SQL, understand data models, and frame questions perfectly. Research Agent handles the analytical complexity while users focus on business questions.

4. Cross-Cloud Data Sharing: Monetization Architecture Without Migration Overhead

Strategic Principle: Data products generate revenue when you can deliver them where customers are, not where you are.

The Enterprise Reality: SAP BDC Connector now supports cross-cloud sharing, meaning you can maintain your Databricks Lakehouse on AWS while sharing governed datasets with partners on Azure or GCP. This eliminates the “migrate to monetize” trap that has limited data product strategies.

Decision Framework: If you’re exploring data monetization, ecosystem partnerships, or multi-cloud customer delivery, cross-cloud sharing reduces your go-to-market friction by 6-12 months compared to replicating data across clouds, a critical capability for data management strategy roadmap execution.

Key Takeaway: Data products succeed or fail based on distribution friction. Cross-cloud sharing removes the biggest distribution barrier, forcing customers to adopt your cloud platform.

Contrast: Legacy data sharing required recipients to be on the same cloud provider, limiting addressable market and forcing expensive data replication.

5. Real-Time Collaboration: The Productivity Multiplier in Data Infrastructure

Strategic Principle: Collaboration latency compounds. Real-time collaboration in notebooks, SQL editors, and files eliminates the tax.

The Enterprise Reality: When data teams can’t edit the same notebook simultaneously, they work serially, create version conflicts, or maintain duplicate copies. Real-time collaboration across notebooks, files, and SQL editor eliminates these friction points, supporting the operational efficiency goals in your data governance strategy.

Decision Framework: Calculate the time your data teams spend managing merge conflicts, waiting for file locks, or synchronizing changes. For teams larger than 5 people, real-time collaboration typically saves 5-10 hours per team member per month.

Key Takeaway: This isn’t video conferencing for code. It’s eliminating the asynchronous overhead that makes data projects take twice as long as they should.

Contrast: Without real-time collaboration, teams work around the tools instead of with them, creating shadow processes and productivity drags.

6. Revamped SQL Alerts: Proactive Operations vs. Reactive Fire-Drills

Strategic Principle: Data-driven organizations monitor metrics, not dashboards. Alert infrastructure determines whether you’re proactive or reactive,a critical component of Databricks Lakehouse monitoring.

The Enterprise Reality: The revamped SQL Alerts UI streamlines how you define queries, conditions, schedules, and notifications. This sounds tactical, but alert architecture determines whether your organization catches problems early or discovers them in quarterly reviews.

Decision Framework: If your executive team discovers issues through customer complaints or quarterly reviews rather than automated monitoring, your alert infrastructure is the constraint. Modernizing alerts is a prerequisite for proactive data operations.

Key Takeaway: Alert sophistication separates data-informed from data-reactive organizations. The UI overhaul removes friction that prevented teams from building comprehensive monitoring.

Contrast: Legacy alerting required complex workarounds or third-party tools. Native alerting with modern UI enables alert-driven operations at scale.

7. Foundation Model Expansion: Optionality in AI Infrastructure Architecture

Strategic Principle: AI strategy requires model flexibility as capabilities and economics evolve rapidly.

The Enterprise Reality: OpenAI GPT-5.1 and Google Gemini Pro 3 are now available as hosted models through Foundation Model APIs. This expands your AI capability options without infrastructure changes, allowing you to optimize for cost, capability, or compliance per use case, supporting your broader AI business intelligence strategy.

Decision Framework: If you’re building AI applications with diverse requirements (some need reasoning depth, some need cost efficiency, some need specific compliance profiles), model optionality prevents architectural lock-in as the AI landscape evolves.

Key Takeaway: The fastest-moving area in technology requires the most architectural flexibility. Multi-model support is insurance against obsolescence and vendor concentration risk.

Contrast: Single-model architectures force you to rebuild when model capabilities or economics shift. Multi-model platforms let you adapt without refactoring.

Conclusion

Which of these Databricks Lakehouse architecture updates addresses your organization’s most immediate data infrastructure constraint? More importantly, which one do your competitors understand better than you do?

We’ve seen too many enterprises treat platform updates as IT bulletins rather than strategic opportunities. The organizations winning with data lakehouse infrastructure aren’t the ones with the newest features. Yhey’re the ones who understand what those features enable competitively.

What’s your organization’s approach to evaluating data lakehouse platform updates for strategic advantage?

Resource

Engineering Velocity as Competitive Advantage: Inside Databricks’ New IDE for Data Engineering

Category

Databricks

Tags

Databricks, Data Engineer

Last Updated

03 Dec 2025

Summary

Remember waiting four minutes to download a single song on iTunes? You’d click purchase, watch the progress bar crawl forward, and hope your internet connection

Remember waiting four minutes to download a single song on iTunes? You’d click purchase, watch the progress bar crawl forward, and hope your internet connection didn’t drop mid-download. Only after the entire file landed on your hard drive could you actually hear if it was the right track. That friction between intent and validation defined the iTunes era.

Then Spotify arrived. Instant playback. No downloads. No waiting. The shift wasn’t just about convenience; it fundamentally changed how people discovered, consumed, and thought about music.

We’ve watched data engineering teams operate in their own “iTunes era” for years, even on modern lakehouse platforms. Engineers write pipeline logic in Databricks notebooks, configure Delta Live Tables orchestration, wait for full execution cycles, and only then discover if their transformation logic works correctly. A single syntax error discovered after a 45-minute pipeline run compounds across every iteration, multiplying time-to-insight and delaying the business decisions waiting downstream.

Databricks just delivered their Spotify moment. And did it with a bang!

From Sequential Deploys to Continuous Validation

In traditional Databricks pipeline development, validation happens at the end. You write SQL transformations in notebooks, configure Delta Live Tables orchestration logic, deploy the entire pipeline to a development workspace, trigger a run, and wait. If something breaks (a join condition that doesn’t account for Unity Catalog’s data governance rules, a data quality expectation that’s too restrictive, a dependency that wasn’t properly sequenced), you discover it only after full execution completes against your Delta tables.

Every iteration cycle that takes hours instead of seconds doesn’t just delay one engineer. It cascades. Downstream teams wait for validated Delta tables. Analytics projects built on Databricks SQL stall. Machine learning engineers can’t access the feature tables they need for model training in MLflow. Business stakeholders make decisions with stale dashboards.

The new IDE for Data Engineering fundamentally changes this equation. Built directly into the Databricks workspace for Lakeflow Spark Declarative Pipelines, it provides instant execution feedback as you declare your datasets. You write a transformation that creates a new Delta table, and the IDE immediately validates your syntax, shows you the dependency graph, and lets you execute just that specific dataset to preview results. A misconfigured join to a Unity Catalog table gets caught in seconds.

We’ve observed enterprises reduce Databricks pipeline development time by 60-70% when they adopt the IDE. You declare your desired state (what Delta tables should exist, what quality expectations must hold) and the IDE continuously validates that logic as you write it. When your team can test and validate pipeline logic in minutes instead of hours, you compress the entire cycle from data request to business insight on the lakehouse platform.

Unified Development Surface Eliminates Context Switching

Here’s the pre-IDE workflow in practice: Your data engineer opens a Databricks notebook to write transformation logic. They switch to the Delta Live Tables UI to configure the pipeline DAG. They navigate to the Jobs tab to check if the last scheduled run completed. They open another browser tab to query Delta tables in the Data Explorer. They context-switch to Repos to commit changes to Git. They move back to pipeline configuration to adjust compute clusters. Each transition creates cognitive overhead that accumulates across dozens of daily iterations.

The Databricks IDE for Data Engineering collapses this fragmentation. Code, the automatically-generated dependency graph of your Delta tables, results preview with data profiling, Unity Catalog schema references, quality expectation configuration, and debugging tools exist in a unified surface. Engineers see their pipeline’s DAG update in real-time as they declare new datasets. They preview Delta table outputs directly alongside the transformation logic that generates them. They configure Lakeflow Jobs scheduling without leaving the IDE. Git integration through Databricks Repos happens inline.

When you eliminate the overhead of switching between six different Databricks workspace surfaces just to validate a single Delta Live Tables change, you fundamentally change organizational velocity. Teams that operate in the unified IDE consistently deliver production pipelines to Delta Lake 40-50% faster than teams working across fragmented Databricks notebook workflows.

Selective Execution Transforms Iteration Speed

Traditional Delta Live Tables orchestration operates on an all-or-nothing premise. When you modify transformation logic in one part of your pipeline, you typically redeploy and rerun the entire workflow. The IDE for Data Engineering enables selective execution of individual datasets and their dependencies. Engineers modify a single transformation that creates a specific Delta table, click to rerun just that dataset and its downstream dependencies, and validate the change in seconds while the rest of the pipeline continues operating normally.

Consider fixing a data quality issue in a production Delta table. In the notebook-based approach, you identify the problem, modify the logic, redeploy the full Delta Live Tables pipeline, wait for complete re-execution across all your Delta tables, and hope nothing else broke. That cycle might take hours and consume significant Databricks compute resources. In the IDE, you modify the specific transformation with the quality expectation issue, rerun just that dataset and its dependencies, validate the fix in minutes with minimal compute cost, and push the change to production.

Enterprises we work with routinely manage Databricks pipelines with hundreds of individual Delta table transformations, all governed by Unity Catalog, serving dozens of downstream Databricks SQL analytics and MLflow model training applications. The ability to iterate on specific datasets without triggering full pipeline reruns is the difference between a data platform that enables rapid experimentation and one that becomes an organizational bottleneck.

The Strategic Question

The technology shift from notebook-based to IDE-native pipeline development in Databricks isn’t coming. It’s already here. The IDE for Data Engineering launched in Public Preview and represents Databricks’ opinionated direction for how declarative pipelines should be built on the lakehouse platform.

Your organization will make this transition eventually. The question facing data leaders today isn’t whether to modernize Databricks pipeline development workflows. It’s whether you’ll lead that modernization proactively (capturing the competitive advantage from higher engineering velocity on the lakehouse) or whether you’ll adopt it reactively, after watching competitors iterate faster on their Delta Lake data products quarter after quarter.

Consider your current state: How many hours per week does your Databricks data engineering team spend waiting for Delta Live Tables pipeline validation? How many potential experiments with Unity Catalog data models never happen because the notebook-based iteration cost is too high? How many business opportunities arrive and depart while your team works through deployment cycles that should take minutes in the IDE but stretch into days?

The music industry didn’t go back to iTunes after experiencing Spotify. Your Databricks data engineering team won’t go back to notebook-based development after experiencing the IDE’s continuous validation.

Resource

From 40 Hours to Zero: How Leading Enterprises Automate Revenue Recognition at Scale

Category

Databricks

Tags

Databricks, Analytics, Cost Optimization

Last Updated

28 Nov 2025

Summary

Enterprise finance teams face a paradox. They’re expected to deliver faster closes, provide real-time visibility into revenue performance, and guide strategic decisions with data-driven insights.

Enterprise finance teams face a paradox. They’re expected to deliver faster closes, provide real-time visibility into revenue performance, and guide strategic decisions with data-driven insights. Yet according to industry research, organizations spend 30 to 40% of their finance team’s time on manual reconciliation tasks. A 2023 Gartner survey of nearly 500 accounting professionals revealed that 18% make financial errors daily, with mounting workload pressure cited as the primary driver.

This isn’t a staffing problem. It’s an architectural one.

The organizations we work with describe the same pattern: time tracking systems capture operational reality, billing rates live in spreadsheets with fragmented version control, contract terms exist in disconnected repositories, and someone must manually reconcile everything to calculate revenue. Senior financial analysts spend their last week of each month in what one CFO described as “spreadsheet gymnastics,” validating calculations that should happen automatically.

The cost transcends wasted hours. When finance teams receive revenue updates two weeks after month-end, strategic opportunities have already passed. When billing disputes arise from manual calculation errors, client relationships suffer. When leadership questions whether financial numbers are final, organizational trust erodes. Manual revenue recognition isn’t just operationally inefficient. It’s a competitive liability in markets where decision velocity determines winners.

The enterprises breaking through this constraint aren’t hiring more analysts or working longer hours. They’re eliminating the constraint entirely through automation architecture that converts reconciliation work into strategic capacity. So how do leading enterprises address this?

The Strategic Architecture of Automated Revenue Recognition

The transformation begins with a fundamental shift in how enterprises architect their financial data foundation. Traditional approaches treat revenue calculation as a monthly event requiring heroic effort from finance teams. Modern approaches recognize that revenue recognition is a continuous process that should execute automatically as operational data flows through the organization.

Professional services firms, consulting organizations, and project-based businesses face particular complexity in revenue recognition. Billable hours tracked in time management systems must be matched with billing rates that vary by client, engagement, and role. Contract terms specify caps, milestones, and recognition timing. Project codes connect activity to revenue streams. Historically, finance teams manually knit these elements together each month.

The architectural solution consolidates these disconnected elements into a unified data foundation. Time entries flow automatically from operational systems into governed data lakehouse tables through API-based ingestion. Rate structures move from spreadsheet files into structured, versioned data tables with clear ownership and audit trails. Validation logic that previously lived in analyst institutional knowledge becomes codified business rules that execute continuously. Revenue calculations happen automatically as new data arrives, not on demand when finance teams request reports.

Mahindra & Mahindra demonstrated this principle at enterprise scale. They developed a GenAI bot for financial analysts on the Databricks platform that led to a 70% reduction in time spent on routine tasks, enabling teams to focus on higher-value strategic initiatives. The transformation wasn’t about making spreadsheets faster. It was about eliminating spreadsheets from the critical path of financial insight.

Consider the contrast with legacy approaches. Traditional financial operations treat each month-end as an isolated event. Analysts extract data from time tracking systems, match it against rate sheets, validate contract compliance, calculate revenue, and prepare reports. The workflow is sequential and manual. If an analyst leaves mid-month, institutional knowledge walks out with them. Automated architectures flip this model. Workflows execute on schedule, not on request. Business logic lives in version-controlled code, not in spreadsheet formulas scattered across shared drives.

From Data Silos to Strategic Capacity

The second strategic element addresses what happens when automation frees finance teams from reconciliation work. We implemented this approach for a professional services organization with revenue spread across 200+ client engagements. Their finance team pulled time data from Clockify, cross-referenced rates in Google Sheets maintained by practice leads, validated against project codes in their CRM, then manually calculated billable revenue. The process consumed 40+ hours monthly. Errors were inevitable. Leadership received financial visibility two weeks after close.

The transformation architecture consolidated these disparate elements into Delta Lake as the unified foundation. Bronze layer tables ingest raw time entries from Clockify API with complete historical fidelity. Silver layer workflows enrich time data with billing rates from structured rate tables, replacing spreadsheet lookups with governed data access. Automated validation logic flags discrepancies between logged hours and contract terms immediately, not at month-end. Gold layer aggregations calculate revenue by client, project, and practice area with full audit trails showing exactly how every number derived.

The impact extended beyond efficiency. Month-end reconciliation dropped from 40 hours to zero. Finance redeployed from data validation to margin analysis, discovering underutilized capacity worth significant annual revenue. Leadership shifted from monthly financial reviews to daily revenue dashboard checks. Billing accuracy improved measurably, reducing client disputes.

Block achieved similar outcomes at greater scale. Using Databricks, they realized a 12x reduction in computing costs while redefining financial services infrastructure for real-time payment processing and revenue recognition across their global platform.

The critical difference from legacy architectures is governance embedded in the platform rather than enforced through process. Unity Catalog provides fine-grained access control across the data lakehouse. Finance sees revenue calculations. Operations sees project metrics. Leadership sees aggregated performance. All querying identical gold tables, ensuring consistency. Data lineage is automatic, not reconstructed manually during audits

The Implementation Framework

Enterprises considering this transformation consistently ask about implementation complexity and organizational change management. Revenue recognition offers ideal characteristics for automation: repetitive monthly workflows, clear business logic, measurable outcomes, and high organizational visibility. Success here builds credibility for broader financial automation initiatives.

The technical pattern follows medallion architecture principles. Bronze layer ingests operational data from source systems with API connectivity where available and batch extraction where necessary. Silver layer applies business rules, validation logic, and enrichment workflows to transform raw data into business entities. Gold layer aggregates data into analytical structures optimized for executive consumption. Scheduled workflows execute transformations automatically, eliminating manual intervention.

The organizational pattern requires finance leadership to shift from hands-on calculation to workflow oversight. Instead of validating every revenue calculation manually, finance teams define business rules that validate calculations automatically. Exceptions surface for review, not entire datasets. This transition from “check everything” to “manage exceptions” requires trust in the platform and clarity in business logic codification.

Unity Catalog provides the governance foundation. Define data domains with clear ownership. Implement access controls that reflect organizational hierarchy. Establish audit trails for all transformations. Create data quality metrics that surface automatically. Treat data governance as architectural requirement, not operational afterthought.

Conclusion

Enterprise finance automation represents more than operational improvement. It’s a strategic imperative for organizations competing in markets where decision velocity determines winners. When finance teams spend 30 to 40% of their time on manual reconciliation, they’re not available for strategic work that drives competitive advantage.

The transformation from manual reconciliation to automated revenue recognition follows clear architectural principles. Consolidate disconnected data sources into unified lakehouse foundations. Codify business logic that currently lives in spreadsheets and institutional knowledge. Build continuous workflows that execute automatically rather than manual processes that happen monthly. Embed governance in platform architecture rather than enforcing it through process overhead.

The enterprises leading this transformation don’t view automation as replacing finance teams. They recognize automation as redirecting finance talent from mechanical validation to strategic insight. When analysts stop reconciling spreadsheets, they start identifying margin opportunities, pricing inefficiencies, and capacity utilization gaps that drive real business value.

The competitive gap between organizations with real-time financial intelligence and those waiting weeks for month-end reports will only widen. Markets reward velocity. Automation enables velocity. The strategic question facing enterprise finance leaders isn’t whether to automate revenue recognition. It’s whether they can afford the competitive disadvantage of continuing manual processes while competitors automate.

What percentage of your finance team’s strategic capacity is currently locked in spreadsheet reconciliation? And what competitive opportunities could that capacity unlock if redirected to strategic work?

Resource

From Permission Bottlenecks to Scaling Data Governance: The Shift to Attribute-Based Access Control

Category

Databricks

Tags

Databricks, Data Governance, Unity Catalog

Last Updated

17 Nov 2025

Summary

We’ve watched too many enterprise data initiatives stall not because of technology limitations, but because of a governance paradox: the more successful you become at

We’ve watched too many enterprise data initiatives stall not because of technology limitations, but because of a governance paradox: the more successful you become at democratizing data access, the more unmanageable your security controls become.

Gartner’s research reveals that by 2027, 60% of organizations will fail to realize the anticipated value of their AI use cases due to incohesive data governance frameworks. This isn’t a technology problem; it’s an architecture problem. Traditional row-level and column-level security approaches, applied asset-by-asset, simply don’t scale when you’re managing hundreds of thousands of tables across multiple business units, each with evolving compliance requirements and thousands of users requiring least-privilege access.

We witnessed this firsthand while working with enterprises that were migrating from legacy data warehouses. Teams would spend weeks configuring granular permissions, only to discover that their carefully constructed access controls became obsolete the moment new data sources came online or organizational structures shifted. The governance team became the bottleneck to data democratization – precisely the opposite of what leadership wanted.

According to Gartner, 77% of Chief Data and Analytics Officers put AI-ready data initiatives in their top 5 investment priorities, yet fewer than half can connect these investments to tangible business outcomes using business-driven metrics. The disconnect isn’t in data platform capabilities, it’s in how we architect access control at scale.

So how do leading enterprises solve this? They shift from managing individual permissions to orchestrating policy-driven governance that adapts automatically as data assets evolve.

The Cost of Governance Fragmentation

Most organizations we work with have fallen into what we call the “permission trap.” Each data asset gets its own set of access rules. Every new table requires manual configuration. Every organizational change triggers a cascade of permission updates across potentially thousands of objects.

The math is brutal. If you have 10,000 tables, each requiring separate row and column filters for different user groups, you’re managing potentially hundreds of thousands of individual permission configurations. When a new compliance regulation arrives, say, expanded PII protection requirements, you’re facing weeks of manual remediation work to identify and update every affected table.

This fragmentation creates three critical failure modes.

First, inconsistent protection across similar data assets because manual processes inevitably introduce variation.
Second, administrative overhead that scales linearly with data growth, forcing teams to choose between governance rigor and data democratization speed.
Third, the inability to respond quickly to regulatory changes or security incidents because you lack centralized visibility and control.

The cost is quantifiable: Gartner estimates that poor data governance costs organizations an average of $12.9 million annually in wasted resources, failed projects, and reputational damage.

Elevating Access Control to Policy Intelligence

The architectural shift that is currently being introduced is moving from object-level permissions to attribute-based policy enforcement. Think of it as graduating from tactical access control to strategic governance intelligence.

Attribute-Based Access Control enables governance teams to define tag-driven policies at the catalog or schema level, where they’re automatically inherited by all current and future tables and views. This approach transforms how enterprises think about data protection. Instead of securing individual tables, you’re defining principles that automatically apply across entire data domains.

Here’s how this works in practice. Rather than manually configuring row filters on every customer table, you define a governance policy once: “Users can only see records where the regional attribute matches their assigned geography.” Tag your data assets with region classifications, and this policy automatically applies to every current and future table in that catalog, whether it contains customer data, transaction records, or operational metrics.

The power isn’t just in simplification – it’s in adaptive, intelligent enforcement. When Data Classification automatically identifies and labels sensitive columns like email addresses or phone numbers, an ABAC policy can automatically mask those fields for all users except authorized teams. Your governance posture strengthens automatically as new data arrives, without manual intervention.

We’ve watched this approach transform organizations. As one enterprise technology leader noted: “Databricks ABAC with column masking unblocked a major workflow for us by enabling dynamic masking of sensitive datasets at scale. The centralized hierarchical policy design brings simplicity and flexibility to policy management and enforcement”.

From Reactive Compliance to Proactive Protection

The real strategic value emerges when you shift from reactive governance, responding to compliance requirements after they arrive, to proactive, principle-based protection that adapts automatically.

Consider a realistic enterprise scenario: your organization acquires another company. Suddenly, you need to integrate their customer data while maintaining strict compliance boundaries during the integration period. With traditional row-level security, you’re looking at weeks of manual work mapping permissions across potentially thousands of tables.

With attribute-based governance, you define the integration policy once: “Users in acquiring company can access all customer records except those tagged as requiring special consent.” Tag the appropriate data assets, and the policy applies automatically. As the integration progresses and restrictions lift, you modify the policy once rather than updating thousands of individual permissions.

This approach scales across the most complex enterprise scenarios.

Multi-national organizations can define region-specific data residency policies that automatically enforce as data moves across geographic boundaries.
Financial services firms can implement dynamic masking that adapts based on user role, data sensitivity classification, and regulatory context, all without manual per-table configuration.

The measurable outcomes include 60-80% reduction in governance administration time, 90%+ improvement in policy consistency across similar data assets, and the ability to respond to new compliance requirements in days rather than months.

The Competitive Stakes

We opened by describing a governance paradox, but we think the real challenge facing enterprises today is a governance threshold. Below a certain scale, perhaps a few hundred tables, a few dozen analysts, traditional permission management is adequate, if inefficient.

But once you cross that threshold, when you’re trying to democratize data access across thousands of users, tens of thousands of tables, and multiple regulatory jurisdictions, fragmented, object-level governance becomes an existential constraint on your ability to operationalize AI and analytics at competitive speed.

As Gartner’s research emphasizes, without unified governance, organizations risk data silos, compliance failures, and untrustworthy AI. A strong governance foundation accelerates time to insight, improves data trust, and allows teams to innovate confidently at scale.

The enterprises winning this transition aren’t necessarily those with the most sophisticated data science teams or the largest technology budgets. They’re the ones who’ve recognized that data governance architecture is strategic infrastructure and have invested in approaches that scale governance capabilities as fast as data democratization requirements.

So here’s our question for you: Is your governance architecture designed to scale with your ambitions, or is it the hidden constraint limiting what your organization can achieve with data and AI?

Resource

From Attendee to Sponsor: What We Learned at Databricks Data + AI Amsterdam

Category

Company News

Tags

Databricks

Last Updated

12 Nov 2025

Summary

Three thousand data practitioners packed into RAI Amsterdam this week for the Databricks Data + AI World Tour, and for the first time, Cosmos Thrace

Three thousand data practitioners packed into RAI Amsterdam this week for the Databricks Data + AI World Tour, and for the first time, Cosmos Thrace wasn’t just in the audience – we were on the floor as an Explorer sponsor with our own booth.

The difference? Instead of taking notes from the back of the room, we were having direct conversations about what keeps data leaders up at night.

The Real Conversations Happening at Booth 12.19

The polished keynotes gave us the “what’s next” in data and AI. But our booth gave us something more valuable: unfiltered insight into what companies are actually struggling with right now.

The pattern was clear. Company after company arrived with the same fundamental challenge: they know Databricks is where they need to be, but they’re stuck in legacy systems that feel impossible to migrate without disrupting operations.

One conversation stands out. A data engineering lead from a major European retailer spent 20 minutes walking us through their current architecture – a patchwork of on-premise warehouses, cloud data lakes, and reporting tools that barely talk to each other. “We know the lakehouse model makes sense,” he said. “We just don’t know how to get there without breaking everything.”

That’s the gap we’re built to bridge.

Three Technical Shifts Worth Watching

While we were heads-down at the booth, three keynote sessions highlighted where Databricks is pushing the platform:

Unity Catalog is becoming non-negotiable Governance used to be the thing you’d tackle “eventually.” Now it’s table stakes. Unity Catalog’s new capabilities around access control, lineage tracking, and multi-cloud management mean enterprises can finally govern data and AI workloads without creating bottlenecks. If you’re planning a Databricks implementation in 2025, Unity Catalog needs to be in scope from day one.
Agent Bricks tackles the 80% AI failure rate Most AI projects fail before production because teams can’t guarantee accuracy at scale. Agent Bricks uses synthetic data generation and automated benchmarking to optimize AI agents for both cost and quality before deployment. This matters because it removes the guesswork from building multi-agent systems that need to operate reliably in high-stakes environments.
Streaming data now flows directly into governed tables Confluent’s Tableflow integration with Unity Catalog eliminates the custom pipeline engineering that traditionally sits between Kafka streams and Delta tables. Real-time data can now materialize as governed, analytics-ready tables automatically, cutting both complexity and total cost of ownership by up to 60%.

What Databricks Data + AI Amsterdam Did for Us

Brand positioning: We’re no longer the boutique consultancy that “also does Databricks.” We’re now visibly part of the Databricks partner ecosystem, standing alongside the platform’s most active implementation partners.

Pipeline development: We walked away with qualified conversations – not vague “let’s stay in touch” exchanges, but specific migration challenges with timelines and budgets attached.

Validation: The problems we’ve been solving for clients aren’t edge cases. Migration complexity, governance uncertainty, and AI production readiness are universal pain points. Our specialization matters.

Next Stop: Databricks Data + AI Stockholm

We’re doing this again on December 10 at the Stockholm leg of the Data + AI World Tour. If you’re attending and want to talk about Databricks migrations, Unity Catalog rollouts, or getting AI agents into production, find us there.

And if you couldn’t make Amsterdam but have questions about navigating your own Databricks journey, whether that’s platform migration, lakehouse architecture, or AI implementation—reach out. We’re here for exactly these conversations.

Resource

Beyond Compliance: Reimagining Data Governance as Your Enterprise AI Accelerator

Category

Data Strategy

Tags

Databricks, Data Governance, AI Ethics, CTO

Last Updated

01 Nov 2025

Summary

In 5 years advising medium and large enterprises’ leadership teams on digital transformation initiatives, we’ve witnessed a persistent misconception that continues to undermine enterprise AI

In 5 years advising medium and large enterprises’ leadership teams on digital transformation initiatives, we’ve witnessed a persistent misconception that continues to undermine enterprise AI ambitions: the belief that data governance and AI innovation exist in fundamental opposition. This couldn’t be further from the truth.

The organizations struggling most with AI adoption today aren’t those lacking technical talent or computational resources – they’re the ones trapped in governance paradigms designed for a bygone era. Traditional governance frameworks emerged when data was primarily structured, centrally managed, and accessed by a small pool of specialized analysts. But today’s enterprise operates in an environment where data volumes have exploded, sources have multiplied, and the potential users span every function.

McKinsey research indicates that companies with sophisticated data governance are 2.5 times more likely to report strong AI adoption and twice as likely to report significant value from these investments. Yet only 31% of enterprises report having governance capabilities robust enough to support their AI ambitions.

The fundamental challenge isn’t whether to prioritize governance over innovation (or vice versa), but rather how to reconceptualize governance itself, transforming it from a defensive control mechanism into a strategic enabler that accelerates responsible AI development. So how do leading enterprises address this governance paradox to fuel AI-powered transformation?

The Evolution from Defensive to Enabling Governance

Traditional data governance programs were designed with a singular focus: risk mitigation. They emerged from regulatory pressures and privacy concerns, resulting in frameworks optimized for control rather than value creation. In practice, this meant centralized data teams serving as gatekeepers, extensive approval workflows, and limited self-service capabilities.

While this approach offered a sense of security, it created significant bottlenecks. At a global financial services firm, their legacy governance model required an average of 22 days for data scientists to access needed datasets – effectively crippling their ability to iterate on AI models at competitive speeds.

Modern governance frameworks serve a fundamentally different purpose. Rather than simply restricting access, they create trusted data foundations that enable broader, safer usage. This requires unified visibility across data assets, automated policy enforcement, and fine-grained access controls that work at scale.

The contrast becomes clear when examining governance capabilities within Databricks’ lakehouse architecture. Rather than manually tracking permissions across fragmented systems, Unity Catalog provides a single governance layer spanning structured, semi-structured, and unstructured data, allowing organizations to implement consistent controls while dramatically expanding safe access.

Decentralizing Governance for AI Scale

Another critical evolution involves shifting from centralized to distributed governance models. In traditional approaches, a small team of data stewards shoulders the entire governance burden, creating inevitable bottlenecks as AI initiatives scale.

Forward-thinking enterprises are adopting domain-oriented governance models where accountability is distributed across business units while maintaining centralized visibility. This allows organizations to scale governance efforts proportionally with their data estates.

A company we worked with implemented this approach by establishing clear data domains aligned to business functions, with designated domain owners responsible for quality, metadata, and access policies within their purview. The central data governance team transitioned from gatekeeping to enablement, establishing standards, providing tooling, and monitoring compliance.

This distributed model proved transformative for their AI initiatives. Rather than waiting weeks for governance approvals, data scientists could access pre-approved, business-context-rich datasets through self-service catalogs. The organization accelerated AI development cycles by 70% while simultaneously strengthening its compliance posture.

Similar capabilities exist within modern data platforms like Databricks, where data can be organized into discrete catalogs with delegated administration while maintaining enterprise-wide visibility and policy enforcement through Unity Catalog.

From Static Rules to Dynamic, Context-Aware Governance

Perhaps the most significant shift is from static, rule-based governance to dynamic, context-aware approaches. Traditional governance applied one-size-fits-all policies regardless of the user, workload, or sensitivity level, essentially treating all data and use cases equally.

Leading organizations now implement dynamic governance that adapts based on context. This includes:

– Differential privacy controls that automatically adjust based on dataset sensitivity – Purpose-based access that considers not just who you are, but what you’re doing – Automated lineage tracking that provides end-to-end visibility of data flows

A global retailer implemented this approach using Databricks’ fine-grained access controls and lineage capabilities. Their data scientists received broader access to customer data for model development, but with automatic PII masking and usage limitations enforced by the platform rather than manual processes. When models moved to production, the governance controls followed the data through its lifecycle, ensuring consistent protection without manual handoffs.

The result? Their data science teams increased productivity by 40% while reducing governance-related incidents by 60% – a dual outcome impossible under traditional approaches.

Conclusion: Governance as AI Accelerator

The evidence is clear: organizations that reimagine governance as a strategic enabler rather than a necessary burden gain significant competitive advantages in their AI initiatives. By shifting from defensive to enabling models, centralizing policy while decentralizing execution, and implementing context-aware controls, enterprises can simultaneously strengthen protection and accelerate innovation.

The path forward requires more than technology – it demands a fundamental shift in how organizations conceptualize governance itself. Rather than asking “how do we control our data?” the more valuable question becomes “how do we enable safe, efficient use of our data at scale?”

As AI becomes increasingly central to competitive strategy, this governance transformation grows more urgent. Those clinging to outdated governance paradigms will find themselves unable to unlock the value of their data while watching more agile competitors race ahead.

We encourage you to reflect: Is your governance framework designed primarily to prevent problems, or does it actively enable your AI ambitions? The answer may reveal whether your organization is positioned to lead or follow in the AI-driven future.

Resource

Lowering TCO and Future-Proofing Data Strategy: The Hidden Advantages of Databricks SQL

Category

Databricks

Tags

Databricks, SQL, Cost Optimization

Last Updated

13 Oct 2025

Summary

Discover how the Databricks SQL platform lowers TCO, simplifies data architecture, and future-proofs your enterprise analytics and AI strategy.

Introduction

If you’re leading enterprise technology today, you’ve probably had this thought more than once: “We’re spending too much on data infrastructure and still moving too slowly.”

Data warehouses, data lakes, integration tools, pipelines, governance layers, visualization platforms… it all adds up. Every component brings cost, complexity, and lock-in risk. The result? A high total cost of ownership (TCO) and a fragile architecture that’s hard to adapt when the business needs change.

The Databricks SQL platform is changing that equation. It combines the performance of a data warehouse with the flexibility of a lake, enabling analytics, AI, and governance in one unified system. In this article, we’ll look at how it helps enterprise data leaders reduce costs today and prepare for whatever’s coming next.

The Real Cost Problem with Traditional Architectures

Let’s start with what’s broken.

Traditional BI and analytics stacks are built around separate systems for different purposes:

A data lake for raw, unstructured, and semi-structured data
A data warehouse for structured, analytics-ready data
Multiple ETL pipelines to move data between them
And usually, more than one cloud or vendor involved in the process

This layered approach made sense 10 years ago. But today, it creates inefficiencies that hit both cost and agility.

Here’s how those costs pile up:

Data duplication: Storing the same data in multiple systems.
Pipeline maintenance: Every transformation adds another failure point.
Licensing and egress fees: Moving data between platforms is not free.
Operational overhead: More systems = more people to run them.
Performance tuning costs: Warehouses need constant optimization to stay fast.

If you’re managing these, you’re not just paying for infrastructure – you’re paying for friction.

Databricks SQL Platform: One Engine, Many Workloads

The Databricks SQL platform addresses that friction at its core. Instead of maintaining separate ecosystems for BI and AI, it brings everything into a single Lakehouse architecture, built on open standards and powered by high-performance compute.

One Copy of Data

All your teams, BI analysts, data scientists, and engineers, work on the same Delta Lake tables. There’s no need to copy data into a separate warehouse for analytics.

One Compute Layer

Databricks SQL uses serverless, elastic compute, so you pay only when queries run. The Photon engine ensures warehouse-level speed without the overhead of managing infrastructure.

One Governance Model

Unity Catalog gives you centralized access control, auditing, and lineage across every data asset, structured or unstructured. No separate governance stack required.

One Platform for BI and AI

SQL analytics, ML model training, and AI applications all live in the same environment. No integrations, no context-switching, no duplicated pipelines.

It’s simple: less infrastructure, fewer vendors, lower cost.

Lowering Total Cost of Ownership (TCO)

CIOs and CTOs are under constant pressure to optimize costs without compromising capability. The Databricks SQL platform helps on several fronts:

1. Eliminate Redundant Systems

When BI and AI coexist in the same environment, there’s no need for a separate warehouse, ETL tool, or governance layer. Each system you retire saves licensing fees and operational costs.

2. Optimize Compute Spending

With serverless scaling, compute resources spin up and down automatically. You don’t pay for idle clusters or over-provisioned capacity. That is a direct TCO reduction.

3. Simplify Operations

A unified architecture means fewer integrations to maintain, fewer failure points, and less time spent troubleshooting broken pipelines. Teams can focus on delivering insights, not maintaining infrastructure.

4. Reduce Data Movement Costs

Data stays in open Delta tables: no expensive egress charges or storage duplication. Queries run directly on your data lake, securely and efficiently.

5. Long-Term Savings from Open Standards

Because Databricks runs on open formats, your data is portable. You’re not locked into one vendor’s pricing model, which gives you negotiating leverage and long-term flexibility.

Future-Proofing Your Data Strategy

Lowering cost is only half the story. The other half is making sure your data foundation is ready for what’s next.

Built for AI

The Databricks SQL platform sits on the same Lakehouse used for machine learning, streaming, and AI applications. As generative AI and LLM-based analytics expand, your existing data stack can evolve with minimal friction.

Built for Openness

Databricks supports open-source standards – Delta Lake, Apache Spark, MLflow, and beyond. You’re free to integrate new tools or migrate in the future without re-engineering your data model.

Built for Scale

Photon, Unity Catalog, and Delta Lake together ensure your platform can scale from gigabytes to petabytes without performance collapse. You don’t need to re-architect every time your business grows.

Built for Governance

Security and compliance frameworks evolve fast. Unity Catalog provides unified governance across all workloads, keeping your data compliant by design, not as an afterthought.

In other words, Databricks SQL isn’t just about cutting costs. It’s about building a platform that grows with you.

A CIO/CTO Perspective

From a leadership standpoint, the Databricks SQL platform is less about technical novelty and more about strategic simplification.

You move from:

Many systems → One platform
Many data copies → One source of truth
Many governance tools → One control layer
Static cost models → Elastic usage-based pricing

This isn’t just modernization – it’s operational efficiency at scale. And it frees your teams to innovate instead of babysitting infrastructure.

Conclusion

The hidden advantage of the Databricks SQL platform is that it delivers both short-term savings and long-term flexibility. By consolidating BI, AI, and governance into one Lakehouse environment, it cuts TCO while keeping your data strategy agile and future-proof.

The result?

Fewer systems to manage
Lower operational costs
No vendor lock-in
A foundation ready for the next wave of AI innovation

If your data estate still feels fragmented, this is your chance to simplify and save. Because the smartest investment a CIO or CTO can make today isn’t just in faster analytics, it’s in an architecture built to last.

Resource

From BI to AI: How Databricks SQL Warehouse Bridges the Gap for Enterprise Data Leaders

Category

Databricks

Tags

Databricks, SQL, Analytics, CTO

Last Updated

08 Oct 2025

Summary

Introduction If you’re a CIO or CTO, chances are you’ve been caught in the middle of the “two-platform trap.” On one side, you’ve got your

Introduction

If you’re a CIO or CTO, chances are you’ve been caught in the middle of the “two-platform trap.” On one side, you’ve got your data warehouse running all the traditional BI dashboards. On the other hand, your data lake is fueling AI and machine learning. Two platforms, two governance models, two teams trying to keep them in sync. But let’s be honest, it rarely works smoothly.

The truth is, this split adds complexity, cost, and risk. What if you could close the gap between BI and AI, without duplicating data or reinventing your architecture?

That’s exactly what Databricks SQL warehouse promises.

The Old Model: BI vs. AI

Historically, the enterprise data strategy has looked like this:

Data warehouse = Business Intelligence. Great for structured queries, reporting, dashboards. Limited when it comes to unstructured or advanced workloads.
Data lake = AI and ML. Flexible, scalable storage that can handle all types of data, but not designed for easy SQL analytics.

CIOs and CTOs end up funding both. The warehouse team focuses on reports, while the data science team lives in the lake. Integration between the two is patchy at best.

The result? Data silos, duplicated pipelines, conflicting metrics, and higher governance risk.

Enter the Databricks SQL Warehouse

The Databricks SQL warehouse (sometimes referred to as Databricks SQL in the Lakehouse) is designed to bring these worlds together. Instead of duplicating data between lake and warehouse, it lets you run fast, scalable SQL queries directly on your lakehouse while staying tightly connected to AI/ML workflows.

Here’s what makes it different:

One platform, one copy of the data. BI analysts and data scientists use the same tables – no duplicate ETL pipelines.
Performance at scale. The Photon engine delivers warehouse-grade query speed, but directly on open Delta Lake storage.
Unified governance. With Unity Catalog, security, lineage, and compliance policies apply across BI dashboards and AI models.
Elastic, serverless compute. Spin up SQL workloads on demand, scale them down when idle. Pay only for what you use.
Future-proof foundation. Your data stays in open formats (Delta/Parquet), giving you flexibility as technologies evolve.

It looks like a data warehouse, feels like a data warehouse but strategically, it’s a bridge between BI and AI.

Why This Matters for CIOs and CTOs

The business case goes beyond technical neatness. A Databricks SQL warehouse addresses the pain points that keep enterprise data leaders up at night:

Reduced Complexity

No more maintaining two separate platforms with two sets of pipelines. Everything happens in one place, dramatically cutting integration overhead.

Lower Risk

Unified governance reduces compliance gaps, shadow IT workarounds, and the risk of “multiple versions of truth.”

Cost Efficiency

No duplicate data, no redundant infrastructure. Elastic compute means predictable, usage-based spending.

Faster Time to Value

When BI and AI run on the same platform, insights flow faster. Analysts, engineers, and data scientists share the same source of truth.

Strategic Agility

Because data is in open formats, you’re not tied to one vendor’s closed ecosystem. You can adapt to emerging AI/ML tools without ripping and replacing your foundation.

What to Watch Out For

Of course, moving toward a Databricks SQL warehouse model comes with considerations:

Migration effort. Reports, ETL processes, and dashboards from legacy warehouses will need rethinking.
User adoption. Teams trained in legacy tools will need onboarding to the lakehouse model.
Governance setup. Unity Catalog is powerful, but requires careful policy design up front.
Cost monitoring. Elastic Compute is efficient, but usage spikes can still surprise you without proper monitoring.

These aren’t deal-breakers, but they need to be part of your roadmap.

How to Approach the Shift

Here’s a phased strategy that minimizes risk and maximizes impact:

Run a pilot project. Pick a high-value but low-risk reporting domain (finance, operations).
Set up governance early. Configure roles, policies, and lineage tracking in Unity Catalog.
Integrate BI tools. Connect Power BI, Tableau, or Looker directly to the Databricks SQL warehouse.
Enable your data science teams. Let them access the same Delta tables for ML workloads.
Expand gradually. Migrate reports, dashboards, and workloads domain by domain.
Monitor and optimize. Track performance, query costs, and adoption metrics.

This approach shows business value quickly while allowing time to manage change.

Conclusion

The days of running two parallel stacks, one for BI, one for AI, are numbered. The complexity, cost, and governance risks are simply too high for today’s enterprise.

A Databricks SQL warehouse provides a unified path forward. It gives you the performance and familiarity of a warehouse, the flexibility of a lake, and the governance needed to satisfy enterprise risk teams.

For CIOs and CTOs, this isn’t just a technical upgrade; it’s a strategic move. It simplifies your data estate, reduces risk, and positions your organization for the future of BI + AI on one platform.

The question isn’t if the two worlds will converge. It’s whether you’ll still be paying double when they do.

Resource

The CTO’s Guide to Databricks SQL Lakehouse Analytics

Category

Databricks

Tags

Databricks, SQL, Data Lakehouse, CTO

Last Updated

06 Oct 2025

Summary

A modern alternative to legacy data warehouses: explore how Databricks SQL lakehouse analytics can lower TCO, boost governance, and simplify your data stack.

The Trouble With Traditional Data Warehouses

Let’s face it, data warehouses have served us well for decades. They brought order to structured reporting and gave business leaders dashboards they could trust. But the reality for today’s CIOs and CTOs is that warehouses are starting to feel like legacy baggage.

Here’s why:

Duplication everywhere. Data has to move from source systems → to the data lake → into the warehouse. That’s not just inefficient, it’s costly.
BI only, nothing else. Warehouses are built for business reporting, not for modern workloads like AI, ML, or streaming. Which means you’re running two parallel stacks: one for BI, another for everything else.
Lock-in. Most warehouses operate on proprietary formats. Once you’re inside, getting out is difficult — and expensive.
Unpredictable costs. Yes, compute and storage may be “separate,” but in practice, duplicated data and surging query demand drive bills higher than expected.

So, while warehouses aren’t “bad,” they’re not built for the scale, diversity, and pace of data use today. And that’s the real issue.

What Is Databricks SQL Lakehouse Analytics?

Now, let’s talk about the alternative.

Databricks SQL lakehouse analytics is Databricks’ SQL engine built directly on top of the lakehouse architecture. Instead of pushing your data into a separate warehouse, you query it right where it lives, in Delta Lake, an open, high-performance table format.

Think of it this way:

It looks and feels like SQL.
It connects to the BI tools your teams already use.
But strategically, it flips the game. One copy of the data. One platform for BI, AI, and engineering.

In other words: no more double work.

Key Differentiators That Matter to CIOs and CTOs

Let’s break down what makes Databricks SQL more than just “another SQL engine.”

One Copy, No Duplication

Your analysts and data scientists all work off the same data in the lakehouse. No extra pipelines to load into a warehouse, no out-of-sync copies.

Elastic, Serverless Compute

Workloads scale automatically. You pay for what you use, when you use it. That means no idle clusters burning budget, and no scrambling when concurrency spikes.

High Performance with Photon

The Photon engine is Databricks’ secret weapon. It’s vectorized, optimized, and delivers warehouse-grade speed for queries without the warehouse.

Unified Governance with Unity Catalog

Governance isn’t an afterthought. With Unity Catalog, you manage access, lineage, and compliance across BI dashboards, ML models, and pipelines. One governance model, end-to-end.

Open, Future-Proof Architecture

Your data stays in open formats (Delta, Parquet). You’re not boxed into one vendor’s proprietary system. You keep strategic flexibility for the long term.

Why Databricks SQL Lakehouse Analytics Matters to Tech Leaders

At the leadership level, the question isn’t “does it work?”. It’s “does it align with strategy?” Here’s why this matters to CIOs and CTOs.

Cost efficiency. Eliminating duplicate pipelines and warehouses means lower TCO and more predictable bills.
Simplicity. Fewer platforms to maintain equals fewer points of failure, fewer people juggling pipelines, and less time lost in firefighting.
Speed to insight. Analysts, engineers, and AI teams work from the same data, no waiting, no handoffs.
Governance made easier. One place to define policies, enforce security, and track lineage. Easier audits, fewer compliance risks.
Strategic agility. Because it’s open and extensible, you can adapt as technology shifts. You’re not tied to today’s vendor roadmap.

This isn’t about a shiny new toy. It’s about making your data strategy leaner, more governed, and ready for the future.

Considerations Before Making the Move

Of course, no shift comes without challenges. A few things to keep in mind:

Migration work. Moving reports and pipelines from a legacy DWH takes planning. A proof of concept in one domain is a smart first step.
Change management. Your analysts and BI developers may need training to adapt to the new workflow.
Governance setup. Unity Catalog is powerful, but you’ll need to design policies and roles properly up front.
Cost monitoring. Elastic compute is great, but spikes in usage can surprise you if you’re not tracking workloads closely.

Acknowledging these considerations upfront helps you avoid surprises.

How to Get Started with Databricks SQL Strategically

If you consider moving your Data Warehouse, here’s a practical roadmap that you can follow:

Firstly, start small. Pick one business domain (finance, operations, sales reporting) and test Databricks SQL lakehouse analytics there.
Set up governance early. Define roles, access, and lineage in Unity Catalog from day one.
Run side-by-side. Let your old warehouse and Databricks SQL coexist while you benchmark cost, performance, and adoption.
Enable your users. Train analysts and BI developers. Give them quick wins with dashboards or reports.
Expand gradually. Once you prove cost savings and simplicity, scale across domains and workloads.

This phased approach keeps risk low while showing value early.

Conclusion

The data warehouse isn’t “dead.” But for CIOs and CTOs, the old model of running both a data warehouse and a data lake is quickly becoming impossible to defend. It’s expensive, redundant, and slows down innovation.

Databricks SQL lakehouse analytics offers a new path: one platform, one copy of the data, one governance framework serving both BI and AI.

If you’re tired of juggling two platforms and paying twice for the same answers, it may be time to rethink the data warehouse and embrace the lakehouse model.

Resource

3 Real-World Use Cases of Databricks Unity Catalog Every Enterprise Leader Should Know

Category

Databricks

Tags

Databricks, Unity Catalog, Data Governance

Last Updated

03 Oct 2025

Summary

Explore 3 real-world Databricks Unity Catalog use cases that show how enterprises can move from data chaos to data confidence with stronger governance, compliance, and growth.

Let’s be honest: most enterprises are sitting on mountains of data… but very few actually trust it.

One department pulls numbers from one system, another runs its own reports, and suddenly the leadership team is staring at two dashboards that don’t even agree. Which one’s right? Nobody’s sure.

That’s the reality of data chaos – and it’s costing companies time, money, and opportunities.

The way forward isn’t “more data” or “more dashboards.” It’s governance. And one of the most powerful tools to bring order to the madness is Databricks Unity Catalog.

But let’s skip the buzzwords for a moment. Instead, we’ll show you how Unity Catalog is being used in the real world. Here are three practical use cases that every enterprise leader should know.

1. Media & Publishing: Turning Data Into Trust (and Revenue)

If you’re in media, you live and die by your audience. But what happens when subscriber data is scattered across different brands, departments, and ad systems? You can’t personalize, you can’t target effectively, and compliance becomes a nightmare.

That’s where Unity Catalog came in for Condé Nast. By pulling all subscriber, ad, and content data into a single governed layer, they got:

One trusted view of their audience
The ability to run personalized recommendations at scale
A compliance process that doesn’t cause sleepless nights

The result? Better customer experiences and stronger ad performance. Governance turned from a headache into a growth driver.

2. Manufacturing: One Version of the Truth

Picture this: a global manufacturer with plants in Europe, Asia, and North America. Each region has its own way of tracking supply chain, production, and finance data. When the CEO asks for a global performance report, three different versions land on their desk.

Unity Catalog solved that by giving the company a single governance framework across all regions. Now:

Definitions are standardized
Lineage is clear from raw sensor data to the executive dashboard
Audits that once took weeks now wrap up in hours

Executives no longer debate which report is correct — they make decisions with confidence. And confidence at the top trickles down to the whole organization.

3. Financial Services: Compliance Without Killing Innovation

Banks and financial institutions don’t get much room for error. Between GDPR, SOX, Basel III, and every other acronym, the cost of a slip-up is massive – fines, lost reputation, and legal battles.

But here’s the problem: if you lock everything down too tightly, your analysts and data scientists can’t innovate.

Unity Catalog hits the balance. Financial institutions use it to:

Apply precise access controls (down to the column or row level)
Track lineage automatically for regulators
Ensure machine learning models only train on approved, governed data

This means compliance teams sleep easier, while AI teams keep moving forward. That’s a rare win-win in finance.

Why These Use Cases Matter Beyond These Industries

You don’t have to be a media giant, a manufacturer, or a bank to benefit. The same playbook applies in retail, healthcare, the public sector – anywhere data chaos holds back growth.

Retailers can finally unify customer data for consistent personalization across channels.
Hospitals can protect sensitive patient info while still enabling vital research.
Governments can enforce transparency and accountability in data use.

The industries look different, but the end game is the same: moving from data chaos to data confidence.

Wrapping It Up

At the end of the day, leaders don’t need more dashboards or yet another data platform. What they really need is the confidence that the numbers in front of them are right.

That’s the real value of Unity Catalog. It’s not a fancy extra — it’s the foundation that lets people actually use their data without second-guessing it.

These examples show that governance isn’t paperwork in the background anymore. It’s a driver of growth, sharper decision-making, and, yes, peace of mind.

At Cosmos Thrace, we’ve seen how the right Databricks setup can change the way companies work with data. If you’d like to explore what that could look like for your team, we’re here to chat.

Resource

From Data Chaos to Data Confidence: How Unity Catalog Transforms Enterprise Governance

Category

Databricks

Tags

Databricks, Unity Catalog, Data Governance

Last Updated

02 Oct 2025

Summary

In today’s digital economy, data is a company’s most valuable asset. But for many medium and large enterprises, data feels less like an asset and

In today’s digital economy, data is a company’s most valuable asset. But for many medium and large enterprises, data feels less like an asset and more like a liability. Silos, duplicated reports, and compliance headaches create data chaos that slows down decision-making and increases business risk.

The good news? With the right approach to enterprise governance, data can shift from being a challenge to becoming a source of confidence and competitive advantage.

One of the most powerful tools enabling this shift is the Databricks Unity Catalog for Enterprise Governance.

The Problem: Why Data Chaos Holds Enterprises Back

Businesses today collect massive amounts of data: customer transactions, marketing metrics, financial logs, supply chain information, even IoT data from connected devices. But without a unified governance strategy, enterprises face:

Multiple versions of the truth: Finance, Marketing, and Operations each work with their own datasets.
Compliance risks: Regulations like GDPR, HIPAA, and SOX make poor governance a legal and financial risk.
Inefficient operations: Analysts spend more time finding data than analyzing it.
Distrust in reporting: Executives doubt whether the numbers they see are accurate.

This situation creates data chaos where more data leads to more confusion instead of more clarity.

What Is Unity Catalog?

Databricks Unity Catalog is a unified governance layer built for the Databricks Lakehouse Platform. In simple terms, it’s like a digital library for all enterprise data and AI assets.

Unity Catalog centralizes governance by providing:

Fine-grained access control: Manage permissions at the table, column, or row level.
Centralized metadata and lineage: Track where data came from and how it has been transformed.
Searchable catalog: Quickly find datasets, models, and dashboards across the enterprise.
Cross-workspace governance: Apply one set of policies across multiple departments and even multiple clouds.

For enterprises, Unity Catalog is not just a technical feature. It’s a foundation for enterprise governance – turning scattered data into a trusted resource.

The Benefits of Unity Catalog for Enterprise Governance

From a business perspective, the Unity Catalog for Enterprise Governance delivers four critical benefits:

1. Stronger Security and Compliance

Protects sensitive data with fine-grained access policies.
Simplifies compliance reporting for GDPR, HIPAA, or SOX.
Reduces the risk of breaches and regulatory fines.

2. Faster Time-to-Insight

A single catalog means analysts and data scientists can find the right dataset instantly.
Teams spend less time requesting access and more time generating insights.

3. Reduced Operational Costs

Eliminates duplicate governance efforts across departments.
Consolidates data management under one platform, reducing tool sprawl.

4. Trusted, High-Quality Data

Built-in lineage tracking ensures everyone knows where data came from.
Executives and managers can trust the reports and dashboards they use for decision-making.

Real-World Use Cases

Case 1: Media and Publishing

Condé Nast, the global media company behind Vogue and The New Yorker, struggled with fragmented subscriber and advertising data. By adopting Databricks with Unity Catalog, they created a single governed view of their audience data.

This allowed them to:

Personalize content delivery,
Improve advertising performance, and
Stay compliant with strict privacy regulations.

Why Enterprises Need to Act Now

The volume of data will only keep growing, and so will regulations around it. Enterprises that continue operating in data chaos risk higher costs, slower decisions, and regulatory penalties.

By adopting Unity Catalog for Enterprise Governance, businesses can:

Move from reactive compliance to proactive trust,
Empower teams to innovate faster, and
Turn data into a strategic business asset rather than a liability.

Final Thoughts

Data chaos is a reality for many organizations, but it doesn’t have to be. With the Databricks Unity Catalog, enterprises can achieve data confidence: trusted, secure, and actionable data that fuels business growth.

At Cosmos Thrace, we specialize in helping enterprises implement Databricks solutions that deliver real business impact. If you want to transform your data governance and unlock the full potential of your data, we’d love to talk!

Resource

Databricks and OpenAI Partnership Transforms Enterprise AI Agent Development

Category

Company News

Tags

Databricks, MLOps

Last Updated

29 Sep 2025

Summary

$100 Million Collaboration Brings GPT-5 Natively to 20,000+ Enterprise Customers

What just happened

Databricks and OpenAI have announced a multi-year $100 million partnership to natively integrate GPT-5 and other next-generation models into Databricks’ Data Intelligence Platform and Agent Bricks.

Under this agreement, over 20,000 enterprise customers across various cloud environments will gain direct access to OpenAI models, eliminating the usual burdens of data copying, complex infrastructure setup, and disconnected toolchains.

The promise: organizations can build production-grade AI agents 4× faster, within a secure, governed environment, and without moving data out of their core systems.

Why this matters to business

AI adoption is accelerating. In 2025, 88 % of organizations have dedicated budgets for AI agent development.
Yet deploying AI agents has remained a bottleneck: moving data between systems, stitching tools together, and ensuring security often slows projects from months to quarters.
This partnership addresses exactly that – by bringing models, compute, and governance into one unified platform, enterprises can compress time-to-value and reduce risk.

In other words, what used to take half a year now might take 4–6 weeks. That’s a massive leap in agility and competitiveness.

Deep dive: how this works

The solution: native integration inside Databricks + unified governance

The Databricks and OpenAI partnership addresses one of the most pressing challenges in enterprise AI adoption: the complex integration of advanced language models with existing data infrastructure. Organizations can now deploy production-grade AI agents up to four times faster than traditional methods, reducing deployment timelines from months to weeks.

Key Partnership Features

Native Model Integration: OpenAI models, including GPT-5, are now available directly within the Databricks environment, eliminating the need for data movement or additional setup. This native integration means enterprises can leverage frontier AI capabilities where their data already resides.
Unified Governance Architecture: The Unity Catalog provides comprehensive end-to-end governance and responsible AI controls, ensuring compliance with security, access management, and ethical standards. This framework allows organizations to deploy AI agents with confidence in regulated industries.
High-Capacity Processing: Databricks customers benefit from dedicated high-capacity processing across the latest OpenAI models, ensuring reliable performance for enterprise-scale applications.

Plus: Databricks’ internal research suggests that prompt optimization techniques (e.g. GEPA) can drastically reduce inference costs – making AI more affordable at scale.

Industry Perspective

Enterprise customers are using the Data Intelligence Platform and Agent Bricks to find and treat diseases earlier, identify energy efficiency opportunities, detect financial fraud, and accelerate application development.
Ali Ghodsi, Co-founder and CEO of Databricks, emphasized the market demand: “We’re seeing overwhelming demand from enterprise customers looking to build AI apps and agents on their data, tailored to their unique business needs. This partnership makes it easier for enterprises to securely leverage their data and OpenAI models at scale with best-in-class governance and performance.”
Brad Lightcap, COO of OpenAI, highlighted the simplification of deployment: “Our partnership with Databricks brings our most advanced models to where secure enterprise data already lives, making it easier for businesses to experiment, deploy, and scale AI agents with real impact.”

Key takeaways for enterprise leaders

The Databricks and OpenAI partnership represents a pivotal shift in enterprise AI deployment strategy. By eliminating data movement requirements, providing unified governance, and offering production-ready evaluation frameworks, organizations can now accelerate their AI agent development from conception to production deployment in weeks rather than months.

For enterprises navigating the complex landscape of AI implementation, this partnership offers a streamlined path to deploying sophisticated AI agents while maintaining the security, compliance, and governance standards required for regulated industries.

If you’re considering applying AI agents in your business or wondering how to leverage this new Databricks + OpenAI partnership, Cosmos Thrace is here to help. We specialize in aligning AI investments with real business value, building secure, scalable architectures, and accelerating your path to production.

Stay tuned for more updates, case studies, and guidance in our News section.

Resource

What Is Databricks Unity Catalog? A Business Leader’s Guide

Category

Databricks

Tags

Databricks, Unity Catalog, Data Governance, CTO

Last Updated

17 Sep 2025

Summary

Many enterprises face the same problem: massive amounts of data, but no reliable way to manage, secure, or use it effectively.

Many enterprises face the same problem: massive amounts of data, but no reliable way to manage, secure, or use it effectively. Compliance becomes painful, teams waste time searching for datasets, and “one version of the truth” seems impossible.

This is the problem Databricks Unity Catalog was built to solve.

In this guide, we’ll answer the most common questions business leaders ask about Unity Catalog – what it is, why it matters, and how global enterprises like Goldman Sachs and Shell are using it today.

Quick Answer

Databricks Unity Catalog is a centralized governance layer for all your data and AI assets.It standardizes security, access control, and data lineage across workspaces, clouds, and teams.

Instead of every department creating its own data silos, Unity Catalog provides:

One set of governance rules.
One searchable catalog.
One version of the truth.

Why Do Enterprises Need Unity Catalog?

Most organizations struggle with:

Multiple teams using different versions of the same dataset.
Expensive and stressful compliance audits.
Delays caused by waiting for data access approvals.
Untrusted analytics because data lineage is unclear.

Studies show enterprises waste an average of $2.5M annually on data governance inefficiencies (not including missed business opportunities).

Unity Catalog addresses these challenges by enforcing “define once, secure everywhere” principle.

How Does Unity Catalog Work?

Unity Catalog functions like a digital library for all your data and AI assets.

Core capabilities include:

Centralized Access Control: Fine-grained permissions using SQL syntax.
Complete Audit Trail: Every access and change logged automatically.
Real-Time Lineage: Clear visibility into where data comes from and how it’s transformed.
Data Discovery: A searchable catalog for easier dataset discovery.
Cross-Cloud Governance: Works across AWS, Azure, and Google Cloud.

Real-World Examples

Goldman Sachs: The “Legend Lakehouse”

Challenge: Compliance across 40+ countries, strict security, real-time market data, and AI readiness.

Solution: Unity Catalog + Apache Iceberg.

Results:

Consistent access control across all business units.
Real-time compliance monitoring.
AI-ready foundation for generative AI projects.
Internal coding assistant improved developer productivity by 40%.
New data products deployed in weeks, not months.

Shell: Data Mesh at Scale

Challenge: Multiple business units, country-specific regulations, and the need for secure data sharing.

Solution: Unity Catalog embedded in a Data Mesh architecture.

Results:

Business units own their data products with centralized governance.
Faster digital transformation while meeting compliance standards.
Teams spend less time on permissions, more time generating insights.

Business Benefits of Unity Catalog

Reduced Time to Value → Data projects delivered in weeks, not months.
Lower Compliance Risk → Automated audit trails and consistent policies reduce breach risk.
Increased Business Agility → Teams discover and access data quickly and securely.
Cost Optimization → System tables show usage for cloud cost savings.
Future-Proofing → Built on open standards (Iceberg, Delta Lake) to avoid vendor lock-in.
Democratized Analytics → Safely expand access beyond technical teams.

FAQ About Unity Catalog

Q: What is Databricks Unity Catalog in simple terms?A: It’s a centralized governance system that ensures the right people have the right access to the right data — across clouds and teams.

Q: Does Unity Catalog replace my existing databases?A: No. It integrates with existing systems (Snowflake, Redshift, Azure SQL, etc.) to provide a unified governance layer.

Q: How long does implementation take?A: Most enterprises launch a pilot in 4–6 weeks and complete rollout in 3–4 months.

Q: What industries benefit most?A: Financial services, manufacturing, healthcare, energy, and retail – any industry with compliance, security, or multi-cloud challenges.

Q: Is it only for AI projects?A: No. Unity Catalog governs all data – from raw files to analytics to AI models.

In a nutshell

Unity Catalog changes governance from a bottleneck into a business accelerator.

Companies like Goldman Sachs and Shell are proof: better governance means faster innovation, safer collaboration, and real competitive advantage. The question isn’t whether you need better data governance. The question is whether you’ll implement it proactively to drive business value or wait until compliance issues force your hand.

If you’re ready to explore how Unity Catalog can work for your organization, Cosmos Thrace can help with data governance assessments and implementation.

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How Databricks’ Latest Updates are Revolutionizing Drug Discovery and Patient Outcomes

Category

Industry Solutions

Tags

Databricks, MLOps, Healthcare

Last Updated

15 Sep 2025

Summary

This summer, Databricks unveiled a series of updates that hold profound implications for the healthcare industry, particularly in drug discovery and enhancing patient outcomes.

In the fast-paced world of AI, innovations are happening at breakneck speed. This summer, Databricks unveiled a series of updates that hold profound implications for the healthcare industry, particularly in drug discovery and enhancing patient outcomes. If you’re excited about leveraging AI to make groundbreaking advancements in healthcare, this is for you.

Why AI in Healthcare?

AI is no longer a futuristic concept but a current reality with the potential to transform healthcare. Here’s why AI is vital:

Accelerated Drug Discovery: AI agents process vast amounts of data rapidly, identifying potential drug candidates at a fraction of the time and cost.
Enhanced Patient Outcomes: By analyzing patient data more comprehensively, AI can suggest more effective treatment plans, ultimately improving patient care.

Key Features of Databricks’ Latest Updates

In its latest rollout, Databricks has introduced several features that promise to accelerate these transformations:

Improved Data Processing Speed: The new updates significantly enhance the speed at which AI agents can draw insights from complex datasets.
Increased Accuracy: With advanced algorithms, predictions and recommendations are now more precise, minimizing errors in patient care.
Enhanced Collaboration: New tools facilitate better collaboration between researchers and healthcare professionals, breaking down silos and accelerating the pace of discovery.

How Does This Work?

Databricks employs sophisticated AI models that can automate various tasks, such as:

Data Analysis: Quickly processing clinical trial datasets to identify patterns that might be missed by human eyes.
Predictive Modeling: Using historical data to foresee patient responses to certain treatments, allowing for better-informed decisions.
Natural Language Processing (NLP): Analyzing patient records to provide doctors with actionable insights.

Frequently Asked Questions

Q: Is AI in healthcare truly reliable?

A: While no system is perfect, advancements in AI have significantly improved reliability. Continuous updates and research are closing the gap between AI predictions and real-world outcomes.

Q: How can healthcare providers implement these updates?

A: Providers can integrate Databricks’ solutions through streamlined setups, and the Databricks team offers extensive support to facilitate this process.

Q: What are the cost implications?

A: Initially, there might be upfront investments. However, over time, AI-driven efficiencies are predicted to offset these costs through quicker drug discoveries and improved patient management.

The Future of AI in Healthcare

The potential of AI to transform healthcare is immense. With the right tools and insights, the industry is on the brink of unparalleled advancements. Databricks’ latest updates underline the importance of speed, accuracy, and collaboration in this revolution.

For those passionate about advancing their healthcare capabilities through innovative AI solutions, these updates represent exciting opportunities to make a real impact.

Feel the thrill of fast-tracking healthcare into a new era with Databricks.

Resource

Databricks Summer Updates 2025: Business Impact Explained

Category

Databricks

Tags

Databricks, Analytics

Last Updated

09 Sep 2025

Summary

Databricks released several major updates this summer. Instead of focusing on technical details, this article explains what they mean for your business: how they reduce costs, speed up decision-making, and strengthen compliance.

Databricks released several major updates this summer. Instead of focusing on technical details, this article explains what they mean for your business: how they reduce costs, speed up decision-making, and strengthen compliance.

Key Takeaways (Quick List)

Data agility: Move tables seamlessly between pipelines without rebuilding.
Real-time streaming: Process data in sub-seconds for instant fraud detection, personalization, and monitoring.
Cost transparency: Automatic tracking and billing of synced tables; improved clustering for lower storage and compute costs.
Faster insights: Natural language queries, improved AI/BI Genie, and Agent Bricks (multi-agent AI).
Secure collaboration: Attribute-based access control and faster Power BI dashboards.
Governance: Centralized Python functions in Unity Catalog and secure service credentials.
Productivity: Better notebook editing, search, and job orchestration.

Common Questions About the Databricks Summer Updates

1. How do the updates improve data agility?

Databricks now allows tables to move seamlessly between pipelines.This means businesses can adapt quickly after acquisitions, reporting changes, or scaling — without costly pipeline rebuilds.

2. Why is real-time streaming important?

The new sub-second processing enables instant fraud detection, real-time customer personalization, and proactive supply chain monitoring.Businesses can act on opportunities and threats immediately, not after the fact.

3. How will these updates help control costs?

Synced tables are automatically tracked and billed.
Liquid clustering optimizes compute and storage in the background.Finance teams gain transparency, and businesses avoid surprise expenses.

4. Can non-technical teams benefit?

Yes. Natural language queries allow anyone to ask “What were last quarter’s sales by region?” in plain English. Databricks generates the SQL automatically.Analysts and managers get insights without waiting on engineers.

5. What is Agent Bricks?

Agent Bricks is a new multi-agent AI system (Beta). Specialized agents collaborate on tasks like:

Gathering market trends.
Analyzing financial risks.
Merging results into a clear plan.This accelerates decision-making in complex scenarios.

6. How is collaboration improved?

Attribute-based access control: Share data externally while keeping internal policies intact.
Power BI high-performance driver: Faster, more reliable dashboards.Executives and partners get insights faster with less friction.

7. What about compliance and governance?

Python functions are now centrally governed in Unity Catalog.
Secure service credentials protect connections to external services.Stronger compliance posture with reduced manual oversight.

8. How do these updates boost productivity?

Side-by-side notebook editing.
Smarter search and replace.
Automatic variable restoration after idle timeouts.
More parallelism in job orchestration. 👉 Data teams save hours and deliver projects faster.

Industry-Specific Benefits

Retail & E-commerce: Real-time personalization increases conversions.
Financial Services: Instant fraud detection and governance reduce risk.
Manufacturing: Transparent cost tracking supports better planning.
Healthcare & Life Sciences: Faster AI insights accelerate drug discovery and patient outcomes.

FAQ

Q: Are these updates only for technical teams? No. Many features (natural language queries, Power BI improvements) are designed for business users and executives.

Q: Will this reduce my cloud costs? Yes. Automatic billing transparency and clustering optimizations cut hidden expenses.

Q: How do these updates impact compliance audits? Centralized governance of Python functions and secure credentials mean less manual tracking and fewer compliance risks.

Q: Is Agent Bricks ready for production? Agent Bricks is still in Beta but shows strong potential for complex, AI-driven business decision support.

Final Word

The theme of the summer Databricks updates is clear:Faster decisions. Better cost control. Safer collaboration.

For leaders scaling operations, managing costs, or pursuing innovation, these updates provide a competitive edge.

Want to know how Databricks could impact your specific business? Leave a comment or reach out directly.

Resource

Cosmos Thrace Achieves Databricks Consulting Partner Select Status

Category

Company News

Tags

Databricks

Last Updated

20 Aug 2025

Summary

Cosmos Thrace is now a Databricks Select Consulting Partner, delivering faster lakehouse migrations, governed analytics, and production-ready AI, proudly from Bulgaria.

We are thrilled to announce that Cosmos Thrace has achieved Databricks Consulting Partner Select Status. This recognition not only validates our technical expertise but also positions us among an elite group of global partners trusted by Databricks to deliver cutting-edge data intelligence solutions.

Over the past years we’ve doubled down on Databricks implementations and migrations, helping organizations modernize to the lakehouse and ship analytics, governance, and AI use cases faster. Today’s recognition validates that focus and strengthens our collaboration with Databricks to deliver even greater impact for our customers.

What Select status means for our clients

Databricks Consulting Partners are uniquely positioned to help businesses implement and scale data engineering, collaborative data science, ML, and business analytics on the Databricks Lakehouse. Select status recognizes partners delivering consistent customer success on the platform, so you get proven architecture patterns, accelerators, and delivery discipline from day one.

You’ll feel the difference in:

Time-to-value: faster migrations and net-new builds leveraging lakehouse best practices (Delta Lake, Databricks SQL, Unity Catalog, MLflow).
Confidence at scale: governance and performance tuned for enterprise workloads, not just proofs of concept.
Closer collaboration: tighter alignment with Databricks teams and the broader partner ecosystem, keeping your roadmap in line with platform advances.

Why this matters in Bulgaria and beyond

We’re proud to represent Bulgaria on the global data & AI stage. Our mission is simple: Turn Data On for enterprises by consolidating fragmented systems into a modern, decision-ready platform. That’s why we’ve centered our offerings on Databricks implementation and migration services, backed by certified expertise and pragmatic delivery playbooks.

A recent example of that effort is a project we delivered for a Coworking place in Bansko, Bulgaria:

Executive Dashboards for Nestwork Coworking (Bulgaria): We unified operational data and delivered real-time executive reporting on Databricks to enable faster pricing, promotions, and capacity decisions.

You can read more about our Databricks services by visiting our dedicated page for it.

How we’ll help you win with Databricks

Modernize to Lakehouse: assessment, architecture, and migration with cost/performance guardrails.
Governance that scales: Unity Catalog-first patterns for data, AI, and cross-team access.
Analytics & AI, production-ready: Databricks SQL dashboards, feature pipelines, and MLflow-based MLOps designed for real adoption.

A note of gratitude

Finally, we want to thank our team for the endless effort and commitment in achieving that goal. It was thrilling to watch the whole team focused on one goal and working towards it. Now, it’s time to chase the Elite status with the same determination!

Ready to turn your data on?If you’re planning a Databricks migration or scaling analytics and AI on the lakehouse, we’d love to help. Let’s build something that ships value fast and lasts. We operate and deliver Databricks implementation services in Switzerland, Germany, Austria, the Netherlands, Belgium, Sweden, Norway and UK, and of course, in our East Europe as well, including Bulgaria, Czech Republic, Poland, Slovenia, Croatia and Greece. Customers who were looking for Databricks implementation partners rated us as one of the best, high-quality quality and experienced partners in Europe.

Resource

Databricks Data + AI Summit 2025 Recap: 4 Things That Matter

Category

Company News

Tags

Databricks

Last Updated

30 Jun 2025

Summary

Databricks isn’t just a lakehouse platform anymore. It’s becoming the operating system for data-native, AI-driven businesses.

Earlier this month, our team had the chance to attend the Databricks Data + AI Summit 2025 in San Francisco.

20,000+ people. A packed schedule. Non-stop energy.

But more than anything else – it was a clear signal.

Databricks isn’t just a lakehouse platform anymore.It’s becoming the operating system for data-native, AI-driven businesses.

Here’s what stood out.

1. Lakebase: Postgres Meets the Lakehouse

Until now, if you wanted to build intelligent apps like chat assistants or recommendation engines on Databricks, you had to move your data into an external database. That added friction, complexity, and cost.

Lakebase changes the game.

It’s a fully managed, Postgres-compatible transactional database that runs inside the Databricks lakehouse.

No more ETL. No more third-party DBs.You can build production-grade, data-native applications without leaving Databricks.

Why this matters:→ Faster development→ Reduced data movement→ One platform for analytics + apps

2. Databricks Apps: Native Apps, Built In

With Databricks Apps, teams can now build, deploy, and share secure apps directly inside the Databricks environment.

This means faster time-to-value, especially for data-intensive orgs that want to operationalize models and apps without spinning up new infrastructure.

Why this matters:→ No need to jump across platforms→ Easy to deploy internally or to customers→ Unlocks new app-based use cases inside the lakehouse

3. Agent Bricks: Enterprise-Ready AI Agents

Everyone’s talking about AI agents. But most aren’t production-ready. And many don’t understand your enterprise data.

Agent Bricks aims to fix that.

They allow teams to build grounded, retrieval-augmented AI agents that pull directly from their company’s data, not the open internet.

Why this matters:→ Lower hallucination risk→ Higher-quality, context-aware outputs→ Enterprise-ready, scalable GenAI workflows

4. Free Edition: Learning Without Limits

Databricks also introduced a Free Edition – a forever-free environment to learn and experiment with lakehouse technology.

It’s clearly a long-term play to build community, empower early-career engineers, and onboard future customers at scale.

Why this matters:→ Makes lakehouse tech more accessible→ Great for training, upskilling, POCs→ Builds loyalty and learning at zero cost

Beyond the Tech: Real Connections

Events like this aren’t just about the keynotes and product launches.

They’re about the people you meet.

We connected with partners, engineers, and data leaders from across the US, EMEA, and beyond. The global Databricks community is real and growing fast.

And if you ask us, Databricks Data + AI Summit 2025 made one thing very clear:

If your business is serious about building with data + AI, Databricks just became a central part of the conversation.

Finally, you can read about our Databricks partnership by following this link.

Resource

Key Terms in BI Reporting

Category

Technical Guides

Tags

Power BI, Analytics

Last Updated

05 Feb 2024

Summary

Business Intelligence has the power to transform raw data into meaningful insights for business growth and efficiency. This allows executives to have a complete view of the overall performance of their business and the different departments. Nevertheless, there are certain technical and business terms in BI reporting that team members in organizations need to know. That is so because mastery of the key terms in BI reporting can significantly enhance the ability to leverage it effectively.

The foundation of BI Reporting Terminology

There are three core building blocks in Business Intelligence: Data Ingestion, Data Warehousing and BI. Data Ingestion is the process of combining data from different sources and bringing it together to provide a unified view ultimately. Data Warehousing is the process of storing and staging information and optimizing it for access and analysis in a company. Finally, BI is the visible portion of the business data systems that enables access and delivery of information to business users. We will look at the key terms for each of the three parts.

Data Ingestion Key Terms

Structured data— Structured data refers to data that adheres to a specific format or schema, making it easily searchable and organized in databases. It is typically stored in relational databases and is characterized by its high level of organization, such as in rows and columns, which makes it easy to enter, query, and analyze.

Data governance—A process that enforces consistent definitions, rules, business metrics, policies, and procedures for how a company treats its data. It can encompass many areas including data creation, movement, transformation, integration, and definitions, all the way to consumption. A data governance program helps the organization treat its data as a corporate asset and maximize its value.

Data cleansing—Finding and fixing errors, inconsistencies and inaccuracies in data. The level of cleanliness required depends on each industry’s best practices. Tools for Data quality are used for the more complex processing while data integration tools perform basic processing.

Data Warehousing Key Terms

Data quality—Achieved when data embodies the “five Cs”: clean, consistent, conformed, current, and comprehensive.

ETL (extract, transform, and load)—The process in which data is taken from the source system, configured, and stored in a data warehouse or database. ETL tools automate data integration tasks.

Data preparation—The core set of processes for data integration. These processes gather data from diverse source systems and transform it according to business and technical rules. Afterward, another process stages it for later steps in its life cycle when it becomes information used by information consumers.

Business Intelligence Key Terms

Analytics—The examination of information to uncover insights that give a business person

the knowledge to make informed decisions. Analytics tools enable people to query and analyze information using data visualization to communicate findings in an easy-to-understand way. There are different analytical types: descriptive (what happened), diagnostic (why it

happened), predictive (what is likely to happen) and prescriptive (what actions should be taken).

Reporting—Collecting data from various sources and presenting it to business people in an understandable way so they can analyze it. This is the core BI style. Reports were initially static with predefined formats but have become interactive and customizable.

Data visualization—Visually presenting data, such as graphs and charts, helps business people see insights they might not otherwise discern from tabular data. Data visualization is only as effective as the quality of the data it draws upon.

Dashboards—This BI tool displays numeric and graphical information on a single display, making it easy for a business person to get information from different sources and customize the appearance.

Understanding the key terms in BI reporting is crucial as they form the language through which data insights are communicated. This understanding is not just beneficial for data analysts but also essential for business decision-makers. Knowing proper BI terminology enables effective team communication, facilitates a better understanding of reports and dashboards, and empowers decision-makers to ask the right questions, leading to informed strategic decisions.

Contact us and let’s help you in your BI journey! However, if you want to learn more about our capabilities, check out our services page.

Resource

What are the top 5 best practices in Business Intelligence reporting?

Category

Technical Guides

Tags

Analytics, Power BI, Data Quality

Last Updated

31 Jan 2024

Summary

Having a holistic view of your business is crucial if you want to outperform your competitors. However, achieving that overview of your company can become

Having a holistic view of your business is crucial if you want to outperform your competitors. However, achieving that overview of your company can become a burdensome task, if not done properly. Nowadays, everyone hops on the BI trend to achieve that. Nevertheless, BI should be done properly in order to achieve tangible results. In this article, we will present you the 5 best practices in Business Intelligence reporting that can help your business thrive.

Start with Questions and Understand Your Audience:

Before delving into BI reporting, it’s crucial to identify the specific insights you need and understand who the reporting audience is. This involves asking the right questions to pinpoint what you seek to understand and knowing who will use your BI reports. This step helps tailor the data and insights to the needs of different groups within your organization, such as executives requiring summarized information versus operational managers who may need more detailed data.

Ensure Data Quality and Identify Required Data Sources:

The foundation of effective BI reporting is high-quality data. This involves ensuring the accuracy, reliability, and relevance of the data used. Poor data quality can lead to misguided decisions. Additionally, identifying and securing access to the necessary data sources is essential for a comprehensive BI reporting system.

Align Reports with Business Objectives and Create Mockups:

Your BI reports should align with the overarching business objectives. This means they should be designed to address specific business goals and KPIs. Creating report mockups is also a best practice. Mockups provide a visual representation of the final reports, allowing for early feedback and adjustments before full-scale implementation.

Visualization and User-Friendly Design:

Presenting data in an easily digestible format is crucial. Effective data visualization, through the use of charts, graphs, and dashboards, enhances the understanding and usefulness of BI reports. Additionally, reports should be user-friendly, ensuring accessibility for all stakeholders regardless of their technical expertise.

Automate Distribution and Foster a Data-driven Culture:

Automating the distribution of BI reports ensures that the right stakeholders receive the right information at the right time. This approach saves time and increases efficiency. Fostering a data-driven culture within your organization encourages the use of BI reporting and promotes the utilization of actionable insights across all levels.

Business Intelligence Reporting Benefits

In terms of the general benefits of BI, it allows organizations to make more informed decisions by providing comprehensive insights into various aspects of the business. This includes sales, financial performance, customer behavior, and market trends. BI tools facilitate the collection, analysis, and presentation of business data, helping leaders and managers to identify opportunities, optimize processes, and mitigate risks.

Proper BI reporting is particularly beneficial for executive-level managers. It empowers them to make better-informed decisions by providing a clear, comprehensive view of operational performance and strategic opportunities. Those reports can highlight trends, reveal insights into customer behavior, track performance against goals, and much more.

In conclusion, integrating these top 5 best practices into your BI reporting process can significantly enhance the effectiveness of your business intelligence initiatives. They ensure that the BI reports are accurate, relevant, and actionable, thus empowering executive-level managers and other decision-makers to steer the organization more effectively toward its goals. Contact us and let us help you implement BI reporting in your organization effectively! You can also check our portfolio of services on this page.

Resource

Bringing security to the next level: Achieving ISO 27001 Certification

Category

Company News

Last Updated

24 Jan 2024

Summary

Nowadays, companies are exposed to constant threats that aim to breach the organization’s data assets. That is especially true for businesses in the IT sector

Nowadays, companies are exposed to constant threats that aim to breach the organization’s data assets. That is especially true for businesses in the IT sector that build solutions based on the data their customers generate. That is why, at Cosmos Thrace, one of our top priorities is ensuring that our client’s data is secured and protected. Achieving ISO 27001 Certification is a milestone that tells us that our security efforts are put in the right direction.

What is ISO 27001 Certification?

ISO 27001 is an international standard that provides a framework for Information Security Management Systems (ISMS) to help organizations secure their information assets. This standard is part of the ISO/IEC 27000 family of standards. The standards have been developed by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC).

To achieve ISO 27001 certification, an organization must implement an ISMS following the standard’s requirements and undergo a formal audit by an accredited certification body. In our case, it is an ISMS applied to technology-based services in electronic data processing and information technology. The certification has worldwide recognition and maintaining it requires ongoing compliance and regular audits.

What does ISO 270001 mean for our clients?

The certification impacts you, as our customer, in the following ways:

Enhanced Data Security: You can be assured that your sensitive data, such as customer information, business analytics, and proprietary AI models, are protected according to international standards. The certification means that we have established a comprehensive Information Security Management System (ISMS) that covers every aspect of data handling, from its creation to its eventual disposal.
Compliance: By adhering to ISO 27001 standards, Cosmos Thrace demonstrates compliance with global best practices in information security including GDPR, HIPAA, and more. This means that our data practices are recognized by many different regulators and comply with their legal requirements.
Risk Management and Mitigation: ISO 27001’s attention to risk assessment and mitigation is among the most emphasized activities during the certification process. Establishing proper risk mitigation strategies reassures clients that potential security risks are proactively identified and managed.
Regular Monitoring and Improvement: The ISO 27001 certification requires ongoing audits and improvements. This means that we will be monitoring and updating our security practices every month to be up to date with the latest best practices in data security. Moreover, we will be monitored by the International Security Organization so they will be providing an expert external opinion on how our security efforts are being developed.

In a nutshell, the ISO 27001 certification is not just a one-time achievement that makes our company look more reliable. This is a commitment to continuous enhancement in security practices that offer long-term reliability and safety to our clients. It is a dedication and investment into a data security strategy that in the next years is going to be the backbone of all our Business Intelligence and AI projects.

You can check all our services here.

Resource

Unveiling the Core Business Requirements for Effective BI Reporting

Category

Technical Guides

Tags

Power BI, Analytics, CTO

Last Updated

11 Jan 2024

Summary

In the fast-paced world of business, having a robust Business Intelligence (BI) system is no longer a luxury but a necessity. As part of the

In the fast-paced world of business, having a robust Business Intelligence (BI) system is no longer a luxury but a necessity. As part of the executive management of a company, it’s crucial to understand the key business requirements that contribute to effective BI reporting. In this article, we’ll delve into the essential elements that businesses need to consider to harness the full potential of their BI reporting capabilities.

1. Data Accuracy and Consistency:

At the heart of any BI reporting system lies the need for accurate and consistent data. BI reporting business requirements start with ensuring that the data being collected and analyzed is reliable. Inaccuracies in data can lead to flawed insights and misguided business decisions. Implementing data validation processes and maintaining data integrity across all sources is imperative to building a trustworthy BI foundation.

2. Scalability for Growing Data Volumes:

As businesses evolve, so does the volume of data they generate. Your BI reporting system must be scalable to accommodate increasing data volumes seamlessly. Scalability ensures that your BI solution remains efficient and effective as your business grows, allowing you to derive meaningful insights even from vast datasets.

3. User-Friendly Interface and Accessibility:

The success of a BI reporting system heavily depends on its user adoption. A user-friendly interface that is easily navigable by both technical and non-technical users is essential. Accessibility across various devices and platforms ensures that stakeholders can access critical insights on the go, fostering a culture of data-driven decision-making throughout the organization.

4. Integration with Existing Systems:

To maximize the utility of BI reporting, it should seamlessly integrate with your existing business systems. Whether it’s ERP, CRM, or other data repositories, the BI solution should be capable of pulling data from diverse sources. This integration streamlines the reporting process, providing a holistic view of your business operations.

5. Customization to Meet Unique Business Needs:

Every business is unique, and so are its reporting requirements. An effective BI system should offer customization options that allow organizations to tailor reports to their specific needs. This flexibility ensures that the insights derived are directly relevant to the business context, facilitating more informed decision-making.

6. Real-Time Reporting Capabilities:

In today’s dynamic business environment, real-time insights can be a game-changer. BI reporting business requirements must include the ability to generate reports in real-time, enabling stakeholders to respond promptly to changing market conditions and emerging opportunities.

7. Security and Compliance:

As businesses handle sensitive data, security is paramount. Your BI reporting system should adhere to the latest security standards and compliance regulations. Robust access controls, encryption, and audit trails are essential features to safeguard your data and maintain the trust of stakeholders.

In conclusion, meeting BI reporting business requirements is fundamental to unlocking the true potential of your Business Intelligence solution. From data accuracy and scalability to user-friendly interfaces and security, a holistic approach ensures that your BI reporting system becomes a strategic asset, driving informed decision-making and enhancing overall business performance.

Resource

Your Pathway to Becoming a Skilled BI Reporting Analyst

Category

Technical Guides

Tags

Power BI, Analytics, Data Engineer

Last Updated

11 Jan 2024

Summary

In the data-centric world of business, the demand for skilled BI Reporting Analysts is growing. This article provides a structured guide to becoming a successful

In the data-centric world of business, the demand for skilled BI Reporting Analysts is growing. This article provides a structured guide to becoming a successful BI Reporting Analyst and pursuing a career that will be both beneficial and rewarding.

Essential Educational Background and Technical Skills

A solid educational foundation is crucial to embark on your journey as a BI Reporting Analyst. Degrees in computer science, statistics, or business administration are highly beneficial. The core of your technical expertise will revolve around BI tools like Microsoft Power BI, Tableau, or QlikView, which are crucial for data analysis and reporting.

Moreover, proficiency in database systems such as SQL and programming languages like Python or R will significantly enhance your analytical capabilities. Here’s a quick checklist of the technical skills needed:

Proficiency in BI tools (Power BI, Tableau, QlikView);
Database management (SQL);
Programming languages (Python, R – optional but beneficial);
Advanced Excel skills.

Developing Soft Skills and Continuous Learning

Beyond technical skills, a BI Reporting Analyst must possess strong analytical thinking to interpret complex data. This role requires not just analyzing data, but also effectively communicating these insights to non-technical stakeholders. Attention to detail and problem-solving abilities are paramount to ensure the accuracy and relevance of your reports.

A critical aspect of this role is continuous learning. The BI field is dynamic, with new tools and techniques constantly emerging. Staying updated is essential:

Analytical and critical thinking;
Excellent communication skills;
Attention to detail;
Adaptability and continuous learning.

Gaining Practical Experience

Hands-on experience, through internships or projects, is invaluable in applying your skills in real-world scenarios. This experience will prepare you for the diverse challenges faced by BI Reporting Analysts in the industry.

In conclusion, a blend of technical knowledge, soft skills, and practical experience forms the cornerstone of a successful career as a BI Reporting Analyst. Focus on these areas, and you’ll be well on your way to a rewarding career in business intelligence.

If you want to learn more about the career openings at Cosmos Thrace visit our careers page.

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What is the difference between Business Intelligence and Reporting?

Category

Technical Guides

Tags

Analytics, Power BI

Last Updated

11 Jan 2024

Summary

In the ever-evolving world of business technology, understanding the distinction between Business Intelligence (BI) and reporting is crucial for executives seeking to make informed decisions.

In the ever-evolving world of business technology, understanding the distinction between Business Intelligence (BI) and reporting is crucial for executives seeking to make informed decisions. While these terms are often used interchangeably, a nuanced understanding of their differences can significantly impact how organizations harness data for strategic decision-making.

1. Scope and Purpose:

At its core, reporting is a subset of Business Intelligence. Reporting primarily involves the presentation of data in a structured format, often through charts, graphs, and tables. It provides a snapshot of historical performance and answers specific questions. On the other hand, BI encompasses a broader spectrum, including reporting but extending beyond to data analysis, predictive modeling, and strategic planning. BI is a comprehensive approach that empowers executives to not only understand what happened but also why it happened and what might happen in the future.

2. Time Horizon:

One key difference between BI and reporting lies in their time horizons. Reporting typically deals with historical data, offering a retrospective view of past performance. In contrast, BI incorporates historical data but also focuses on the present and future. It equips executives with real-time insights, enabling proactive decision-making based on current market conditions and emerging trends.

3. Interactivity and Flexibility:

Reporting often follows a predefined format, providing a static view of data. BI, however, emphasizes interactivity and flexibility. Business Intelligence tools enable executives to explore data dynamically, drill down into details, and generate ad-hoc reports tailored to specific queries. This flexibility is vital in a rapidly changing business environment where executives need to adapt swiftly to new information.

4. Data Complexity and Sources:

While reporting tends to deal with structured data in a standardized format, Business Intelligence tackles a broader range of data complexities. BI solutions can handle diverse data sources, including unstructured data, providing a more comprehensive view of the business landscape. This capability allows executives to glean insights from various channels, from traditional databases to social media and beyond.

5. Decision Support:

Reporting primarily serves as a tool for tracking Key Performance Indicators (KPIs) and operational metrics. Business Intelligence, on the other hand, goes a step further by offering decision support. BI tools incorporate advanced analytics and predictive modeling, empowering executives to make strategic decisions based on a deeper understanding of trends and patterns.

6. Integration with Strategic Planning:

While reporting informs tactical decisions, BI is inherently tied to strategic planning. Business Intelligence aligns data insights with organizational goals, facilitating long-term planning and helping executives make decisions that contribute to the overall business strategy.

In conclusion, understanding the difference between Business Intelligence and reporting is pivotal for business executives aiming to leverage data effectively. The nuanced capabilities of BI extend beyond traditional reporting, providing a holistic approach to data analysis, decision support, and strategic planning. By embracing both reporting and BI, executives can create a well-rounded approach to data-driven decision-making, ensuring agility and competitiveness in today’s dynamic business landscape.

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Celebrating the start of our Horizon journey

Category

Company News

Tags

Manufacturing

Last Updated

18 Dec 2023

Summary

We are delighted to announce that we have secured our first Horizon flexible laser-based manufacturing through a precision photon distribution (FLASH) project, in recognition of

We are delighted to announce that we have secured our first Horizon flexible laser-based manufacturing through a precision photon distribution (FLASH) project, in recognition of our expertise in innovation, technology and manufacturing. We are grateful and excited to be one of the few SME companies from our EU region to win a place on a project of this size and start their Horizon journey. Our role in the € 4 131 800.25 three-year project will be to develop machine learning algorithms to better predict and recommend the optimal processing regimes across the platform. Winning a place in a consortium alongside other highly experienced companies is evidence of the expertise of our team and the trust that other organizations have in us. The news marks an exciting end to 2023 and points to a great start to 2024, with the project launching in January.

The Horizon Europe program is aimed at streamlining the EU’s position as a world leader in science. Its ultimate goal is to bring excellent research results to market to deliver direct benefits to citizens, such as affordable health care, protection against cybercrime and the transition to a resource-efficient, low-carbon economy.

Project Overview: Advancing Manufacturing

The FLASH project will focus on “highly customized laser-based production including new and advanced methods to provide a highly precise distribution of photons at the right place and time”. The consortium’s goal is to develop a multi-process laser-machine tool capable of thermal-based (hardening, welding, cutting, macro polishing and drilling of metals and alloys) and ablation-based (texturing, micro-machining/polishing) processes, as well as multi-beam cutting and drilling of advanced, reflective, composite and ceramic materials.

This will be achieved by combining multiple beams that will be deliverable, in any combination, to the delivery systems. It will allow a single versatile machine tool to replace up to three separate machine tools, with full material and process flexibility. In addition, it will deliver an unprecedented level of manufacturing line agility, by enabling the reconfiguration of processes through the automated changeover of the laser beam delivery system in just seconds, compared to the several hours needed to replace or reconfigure traditional mechanical or laser machines. This highly precise type of manufacturing reduces waste, defects and energy consumption, leading to more successful and sustainable products.

Delivering Better Outcomes for Business

The FLASH project will aim to deliver a fully optimized processing strategy for the industry. Considering all processes and parameters available to the FLASH machine platform, the process design package will identify the best sequence of individual processing steps and provide optimized parameters for each (e.g., tool path, power, pulse length, beam shape, spatial coherence, etc.). In contrast with existing modeling solutions, FLASH will deliver predictive capabilities for manufacturing. This will enable non-expert users, including SMEs, to rapidly develop new processes during accelerated product and process development cycles. The FLASH process design platform will also integrate a digital decision model that considers environmental impact indicators in two categories:

Combined environmental impact indicators of the multi-step laser processes and selected parameters, compared to alternative processes (e.g. energy input, material waste, etc.).
Environmental impact indicators over the product lifecycle as a result of differences to the final component/product resulting from the applied laser processes (e.g. emission reduction due to product lightweight, re-manufacturing energy reduction as a result of extended product lifetime, etc.).

This robust focus on lifecycle environmental impact will be unprecedented in laser-based manufacturing process design, helping to drive the European industry’s green and digital transition goals.

Our role in the project

At Cosmos Thrace, we have developed and commercialized multiple unique AI solutions, including via the creation of a spin-off company to help our clients optimize their business processes, reduce costs and gain a competitive edge. At the start of our Horizon journey, we are excited to apply our know-how on a larger scale and in collaboration with professionals in their respective fields, to ultimately deliver a platform that will accelerate the future of manufacturing. The Cosmos Thrace team will create methods to better predict and recommend the optimal processing regimes across all capabilities offered by the FLASH-IANUS platform. We will develop machine-learning models that will validate the performance of the machines in identifying optimized laser processes. As well as confirming our expertise in advancing manufacturing through technology, our involvement in this project will enable us to grow our data science team.

Our project partners:

Looking forward to many more exciting projects that will continue our Horizon journey! You can also contact us if you want to learn more about us and our services!

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Should Your Business Leverage Business Intelligence (BI) Reporting?

Category

Technical Guides

Tags

Power BI, Analytics, CTO

Last Updated

04 Dec 2023

Summary

Every single business needs BI reporting to prepare, analyze, and present business metrics. To begin with, there are two types of Business intelligence reporting.

Every single business needs BI reporting to prepare, analyze, and present business metrics. To begin with, there are two types of Business intelligence reporting. Managed reporting is the process of preparing data for non-technical users by a technical professional, such as an IT associate or data analyst. A BI platform’s ad-hoc reporting feature enables non-technical users to build new reports from start to end or change old ones without contacting IT. Users of reports can slice and dice tables to find links between variables and see data patterns over time. Reports may be produced in a variety of forms, including tables, spreadsheets, and PDFs. Charts, graphs, and histograms are examples of visualizations used in BI reporting. BI reports may now be automated and run at a predetermined time thanks to modern technologies.

How does all of this work in a synchronized system of processes?

Data is pulled and read from on-site and cloud-based data sources of a company via BI reporting tools. The reporting tool may be used to produce analyses by selecting metrics like sales, revenue, inventory counts, etc., and applying dimensions like date, purchase order, or customer information. Drag-and-drop feature allows users to select dimensions and metrics while creating reports. Depending on the selected data, a team may produce tables, histograms, or pie charts, as well as predict models. These charts may be used to build dashboards, and they can be set to update periodically or almost instantly. Tableau provides demonstrations and training films to help users better understand the potential and capabilities of BI reporting.

Furthermore, to enhance decision-making, BI reporting aims to offer data insights.

However, not just analysts and IT developers should have the authority to slice data to find patterns and insights. Reports that are arranged and tailored in the forms that the company’s decision-makers prefer are needed. They must be able to read the report, comprehend the information, make decisions, and make wise judgments. Eliminating data silos and establishing a single source of truth are two further objectives of BI reporting. There are no longer different versions of the truth when teams use the same datasets and analytic platform. Decision-making processes can reach a better view of the problem and change data in one location; thus, data analysis is faster and more precise. However, without a BI reporting tool, these objectives cannot be achieved.

Therefore, establishing a BI reporting strategy in your business has several advantages.

Using self-service BI technologies, non-technical users may now make their own reports. Employees may find it simpler to use data and comprehend key business indicators with the help of BI tools. Teams can utilize reporting data to share the data with others and improve their decision-making. Data from business intelligence can help decision-makers. By combining data from several departments and enabling users to engage with that data, BI reports can assist businesses in seeing the wider picture. Because big data reporting solutions are intended to reduce the amount of time spent processing data for reporting, all of this can be finished faster and more effectively. BI solutions may also automatically publish reports on a timetable that is tailored to security permissions.

In addition, Online reporting and business intelligence are intertwined. No matter what business or area you work in, if you collect data, you must evaluate and display it well. As a result, you may make better strategic decisions for your company and get knowledge that would not have otherwise been known.

Nevertheless, BI platforms and their functions vary from one another, so it is best practice to evaluate BI tools in advance according to your business’s needs.

Although there are numerous BI software platforms out there, not every tool will work for your company. Select a platform that provides these 4 key factors:

Programmable Dashboards – Well-structured platforms can enable all users to quickly develop insights that are driven by visuals.
Simple User-friendly Interface – Non-analysts should be able to quickly query and obtain the desired results.
Plug-and-play Capability – The ideal platform works with your security and governance environment without the need for extra infrastructure, fitting inside your current data architecture.
Scalability – The platform is adaptable enough to grow along with your company as it expands.

To conclude, Business BI reporting possesses the objective of enabling end users to view data in a way they can evaluate it and make it intelligible through analysis. When the purpose of business intelligence is to see, understand, and act upon data, then efficient reporting aims come first. Data is the subject of reporting; however, analysis is what transforms the data into knowledge.

Resource

How to design Power BI reports

Category

Technical Guides

Tags

Power BI, Analytics

Last Updated

04 Dec 2023

Summary

Design often takes a back seat in the world of data analytics, but its impact on Power BI reports cannot be overstated.

Design often takes a back seat in the world of data analytics, but its impact on Power BI reports cannot be overstated. In this guide, we’ll explore the significance of design in Power BI reports and provide a step-by-step process to create visually compelling and effective reports. So, let’s learn how to design a power BI report in just under 5 minutes!

Part 1 – Why You Need Design

Design is the key to making your reports stand out and ensuring that your data is not only read but remembered. Implementing design:

Unlocks Insights: Making data visually appealing helps users understand and interpret information, leading to the discovery of new insights.
Improves User Experience: Well-designed reports enhance the end-user experience by making information easy to digest and navigate.
Adds Trust & Credibility: Professional design builds a reputable and credible brand, instilling trust in the insights presented.

Part 2 – Design Principles

Before delving into the process, understanding fundamental design principles is crucial:

Simplicity: Prioritize creating a simple but accurate report to avoid clutter and complexity.
Consistency: Maintain uniformity in data representation, color schemes, icons, and date formats to enhance user understanding.
Accuracy: Govern the layout, structure, and proportions to ensure a clear depiction of data.

Part 3 – Step-by-step Process to Building & Designing Reports

Step 1: Defining your insights

Audience: Clearly define your audience to create relevant and memorable visualizations.
Purpose: Identify the purpose of your report, focusing on answering vital questions.
Overarching Insights: Prioritize 6-10 insights to avoid clutter and maintain focus.

Step 2: Deciding on the Data

Choose the Right Data Set: Select a dataset that aligns with your insights to avoid redundant reports.

Step 3: Choosing your Visuals

Match Insights with Visuals: Utilize a list of visual types to match the right visual with each insight.

Step 4: Positioning of Visuals

Layout is Critical: Ensure a consistent layout with relevant metrics grouped logically for easy user interpretation.

Step 5: Design Tips Checklist

Visualizing Insights: Stick to 6-10 insights per page for simplicity.
Match Insights with Visuals: Ensure each insight is paired with the correct visual.
Logical Layout: Follow a logical layout and group related metrics together.
Visual Hierarchy: Emphasize the importance of elements through visual hierarchy.
Headings and Labels: Add clear headings and labels for clarity and context.
Color Palette: Use a consistent color palette across your report.
Consistent Theme: Stay true to your theme’s color palette for visual consistency.
Distinguishable Colors: Use distinguishable colors with enough contrast.

Conclusion

Implementing design principles in Power BI report creation is vital for user engagement and effective communication of insights. Use this comprehensive guide to elevate your report design and make your data truly impactful.

For additional information, explore external resources such as the Microsoft Power BI Documentation and Power BI Community.

Ready to transform your data into compelling reports? Book a call with us to discuss your Power BI needs and unlock the full potential of your

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Unlocking Success: Best Practices for Power BI Reporting

Category

Technical Guides

Tags

Power BI, Analytics

Last Updated

04 Dec 2023

Summary

In the dynamic landscape of data analytics and business intelligence, Power BI has emerged as a game-changer, empowering organizations to transform raw data into actionable

In the dynamic landscape of data analytics and business intelligence, Power BI has emerged as a game-changer, empowering organizations to transform raw data into actionable insights. To harness the full potential of this powerful tool, it’s essential to adopt best practices for Power BI reporting. Let’s explore key strategies to elevate your reporting game and drive informed decision-making.

1. Understanding Your Audience

Effective Power BI reporting begins with a deep understanding of your audience. Different stakeholders have varying information needs, and tailoring your reports to address these specific requirements is crucial. Utilize the user-friendly interface of Power BI to create customized dashboards that provide relevant insights to executives, analysts, and operational teams.

2. Data Quality and Cleansing

That is the foundation of reliable reporting. Accurate reporting hinges on the quality of your data. Establish a robust data quality framework to ensure consistency, completeness, and reliability. Leverage Power BI’s data cleansing capabilities to identify and rectify discrepancies, ensuring that your reports are built on a solid foundation of trustworthy information.

3. Transforming Data into Compelling Narratives

Compelling visualizations are at the heart of effective reporting. Choose visual elements that resonate with your audience and clearly communicate your message. Leverage Power BI’s extensive library of visualizations to create intuitive charts, graphs, and maps. Remember, simplicity often leads to better comprehension, so avoid clutter and focus on conveying key insights.

4. Performance Optimization

Optimizing the performance of your Power BI reports is vital for a seamless user experience. Implement efficient data models, minimize the use of unnecessary visuals, and leverage Power BI’s performance analyzer to identify and address bottlenecks. A responsive and swift reporting system ensures that users can access insights without delays.

5. Regular Updates and Maintenance

The business environment is dynamic, and so should your reports. Regularly update your Power BI reports to reflect changes in business goals, data sources, and user requirements. Establish a maintenance routine to review and optimize reports, ensuring they remain relevant and aligned with organizational objectives.

Maximizing the Impact of Power BI

In conclusion, invest in training programs to empower your team with the skills needed to leverage Power BI effectively. Microsoft offers comprehensive training resources, including online courses and certification programs. A well-trained team ensures that your organization can unlock the full potential of Power BI, driving innovation and efficiency.

Mastering Power BI reporting involves a holistic approach that encompasses audience understanding, data quality, visualization, performance optimization, regular updates, and user empowerment. By adopting these best practices for power BI reporting, you can transform raw data into actionable insights, driving informed decision-making across your organization.

Ready to take your Power BI reporting to the next level? Book a call with us today to explore how our expertise can empower your business.

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Unveiling the Essence: Top 3 Goals in BI Reporting

Category

Technical Guides

Tags

Power BI, Analytics

Last Updated

13 Nov 2023

Summary

In the ever-evolving landscape of data analytics and business intelligence (BI), effective reporting plays a pivotal role in driving informed decision-making.

In the ever-evolving landscape of data analytics and business intelligence (BI), effective reporting plays a pivotal role in driving informed decision-making. As organizations harness the power of data, understanding and aligning with the top 3 goals in BI reporting becomes imperative. In this article, we will delve into the three key objectives that businesses should prioritize to unlock the full potential of their BI initiatives.

Strategic Decision-Making:

At the heart of BI reporting lies the mission to empower strategic decision-making. Businesses generate vast amounts of data daily, and the ability to distill this information into actionable insights is paramount. BI reporting allows stakeholders to gain a comprehensive view of their business landscape, identify trends, and foresee potential challenges. By setting the goal of facilitating strategic decision-making through BI reporting, organizations can leverage data-driven insights to navigate the competitive market with precision.

Operational Efficiency and Performance Monitoring:

Another crucial goal in BI reporting is enhancing operational efficiency and monitoring performance. Through detailed reporting, businesses can streamline their processes, identify bottlenecks, and optimize workflows. Real-time performance monitoring enables organizations to react swiftly to changes, ensuring that they stay agile and responsive in a dynamic business environment. Setting the goal of improving operational efficiency through BI reporting fosters a culture of continuous improvement, driving sustainable growth.

Enhanced Data Accuracy and Governance:

The third key goal in BI reporting centers around ensuring data accuracy and governance. Reliable and accurate data is the bedrock of successful BI initiatives. BI reporting systems should not only present data in a comprehensible manner but also guarantee its integrity. By prioritizing data governance, businesses can establish robust frameworks for data quality, security, and compliance. This goal ensures that decision-makers can trust the information presented in their reports, fostering a data-driven culture throughout the organization.

In conclusion, the top 3 goals in BI reporting—strategic decision-making, operational efficiency, and data accuracy and governance—form the cornerstone of a successful data-driven strategy. Embracing these goals positions organizations to harness the full potential of their data, gain a competitive edge, and drive innovation.

Ready to elevate your BI reporting game and unlock the true potential of your data? Book a free call with us and let’s discuss how we can tailor a solution to meet your business needs!

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Top 5 Applications of Artificial Intelligence in Retail

Category

Industry Solutions

Tags

Retail, Ecommerce, MLOps

Last Updated

25 Oct 2023

Summary

Artificial Intelligence (AI) has significantly transformed the retail landscape, revolutionizing how businesses operate and how customers shop. In this article, we will delve into the

Artificial Intelligence (AI) has significantly transformed the retail landscape, revolutionizing how businesses operate and how customers shop. In this article, we will delve into the top five applications of AI in the retail industry, demonstrating how this cutting-edge technology is reshaping the way retailers connect with their customers and optimize their operations.

1. Personalized Shopping Experiences

One of the most compelling applications of AI in retail is creating personalized shopping experiences. AI algorithms analyze customer data, including purchase history, preferences, and online behavior, to recommend products that align with individual tastes. Amazon is a prime example of this with its personalized product recommendations, which lead to increased sales and customer satisfaction.

2. Inventory Management and Demand Forecasting

AI plays a pivotal role in optimizing inventory management and demand forecasting. Retailers use machine learning models to analyze historical sales data, seasonal trends, and even external factors like weather forecasts. The result is a more accurate prediction of demand, reducing overstocking and understocking issues. Walmart, for instance, uses AI to improve its inventory management, saving millions of dollars in the process.

3. Chatbots and Virtual Assistants

Chatbots and virtual assistants powered by AI have revolutionized customer service in retail. These AI-driven tools provide 24/7 customer support, answer queries, and help shoppers find the products they need. AI chatbots can handle a vast number of inquiries simultaneously, offering efficient support and enhancing the customer experience. Sephora’s chatbot is a case in point, offering personalized beauty advice and product recommendations.

4. Visual Search and Image Recognition

Visual search is another exciting AI application in retail. Shoppers can now use their smartphones to snap pictures of items they want, and AI-powered systems can find similar products in the retailer’s inventory. Pinterest Lens is a real-world example, allowing users to search for products based on images, leading to increased engagement and purchases.

5. Fraud Detection and Security

AI is critical for retail security and fraud detection. It can analyze transactions and customer data in real time to identify suspicious activities, such as fraudulent payments or account breaches. AI-powered security systems can swiftly respond to potential threats, safeguarding both the retailer and the customers.

Real Use Case: Amazon Go

A prime illustration of AI’s practical application in retail is Amazon Go, the cashier-less convenience store. AI algorithms track customer movements and items taken from shelves. When shoppers leave the store, their Amazon accounts are automatically charged for the items they’ve taken, eliminating the need for traditional checkout. This AI-powered innovation has streamlined the shopping experience, reduced friction, and gained widespread popularity.

External Resources for Deeper Insights

For a more in-depth understanding of AI’s role in retail, consider these resources:

Deloitte’s “AI in Retail: Welcome to the Cognitive Shopper”: Deloitte’s report provides comprehensive insights into AI’s impact on the retail sector.
MIT Sloan Management Review’s “How Retailers Can Use AI to Get Closer to Their Customers“: This article discusses the ways AI can enhance customer relationships in retail.

Embrace AI for Retail Success

AI is revolutionizing the retail industry by enhancing customer experiences, optimizing operations, and increasing security. To stay competitive and meet the evolving needs of consumers, retailers must harness the potential of AI.

Ready to leverage the power of AI in your retail business? Schedule a consultation with us to explore how AI can transform your retail operations and customer engagement. Book a call with us now! You can also check our portfolio of services.

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How Big Data is Transforming Retail Marketing Analytics

Category

Industry Solutions

Tags

Retail, Ecommerce, Analytics

Last Updated

13 Oct 2023

Summary

In today’s fast-paced business landscape, the ability to harness data-driven insights can be the difference between success and stagnation. As the retail industry continually adapts

In today’s fast-paced business landscape, the ability to harness data-driven insights can be the difference between success and stagnation. As the retail industry continually adapts to meet evolving consumer demands, the role of big data in marketing analytics has become increasingly vital. In this article, we’ll explore how big data is reshaping the industry and driving business success.

Understanding Big Data in Retail

To comprehend the impact of big data in retail, it’s crucial to define what it entails. Big data refers to vast sets of structured and unstructured information that retail businesses collect from various sources, including sales records, customer behavior, social media interactions, and more. This data is processed and analyzed to extract valuable insights, helping retailers make informed decisions.

Enhancing Customer Experience

One significant way big data is changing retail marketing is by enhancing the customer experience. Retailers can now gain a comprehensive understanding of their customers’ preferences, shopping habits, and expectations. By analyzing this data, they can tailor marketing strategies and product offerings to meet individual needs, thus increasing customer satisfaction and loyalty.

Use Case: Personalized Product Recommendations

To illustrate the real-world application of big data in retail, let’s look at a popular example: personalized product recommendations. E-commerce giants like Amazon have harnessed the power of big data to provide shoppers with tailored product suggestions. By analyzing a customer’s purchase history, browsing behavior, and demographic data, these platforms can suggest products that are highly likely to appeal to the individual. As a result, customers are more likely to make additional purchases, increasing sales and revenue for the retailer.

Operational Efficiency

Another critical aspect of big data in retail is the optimization of operational efficiency. Retailers can use data analytics to streamline inventory management, forecast demand more accurately, and reduce supply chain costs. This translates into reduced overheads and increased profitability.

Embrace Big Data for Retail Success

In conclusion, the retail industry is experiencing a data-driven revolution, and those who leverage big it are reaping the benefits. From enhancing the customer experience to improving operational efficiency, this technology has become an essential tool for success. To stay competitive and meet the ever-evolving demands of consumers, retail businesses must embrace the power of data analytics.

Incorporating big data into your retail marketing strategy can be a game-changer. Don’t miss out on the opportunity to stay ahead of the curve. Book a call with us now! You can also take a look at our portfolio of ser v ices.

External Resources for Deeper Insights

If you’re interested in delving further into the impact of big data in retail, here are a couple of resources to explore:

Harvard Business Review: HBR often features insightful articles on digital transformation in various industries, including retail.National Retail Federation (NRF): The NRF website offers a wealth of resources, including reports and case studies, focusing on the retail industry’s digital transformation.

Resource

Demystifying DAX in Power BI: Unveiling the Power Behind Data Analysis Expressions

Category

Technical Guides

Tags

Power BI, SQL, Analytics

Last Updated

09 Aug 2023

Summary

In the realm of data analytics and business intelligence, Power BI has emerged as a dominant tool for transforming raw data into actionable insights.

In the realm of data analytics and business intelligence, Power BI has emerged as a dominant tool for transforming raw data into actionable insights. At the heart of this tool lies a powerful formula language known as Data Analysis Expressions, or DAX. In this article, we’ll take a deep dive into the world of DAX in Power BI, unraveling its significance, functionality, and applications.

What is DAX in Power BI?

Data Analysis Expressions, abbreviated as DAX, is a specialized formula language developed by Microsoft. It is primarily used within Power BI, Excel Power Pivot, and SQL Server Analysis Services (SSAS) to perform advanced calculations, create custom formulas, and manipulate data within tabular models.

The Power of DAX: Key Features and Capabilities

Custom Calculations: DAX allows you to create calculated columns and measures that go beyond basic arithmetic. You can define intricate calculations tailored to your business requirements.
Aggregation and Summarization: DAX empowers you to perform various aggregations such as sum, average, count, and more. These calculations dynamically adjust as users interact with your reports and dashboards.
Time Intelligence: Working with time-based data is easier with DAX’s time intelligence functions. Calculate year-to-date, moving averages, and compare data across different periods effortlessly.
Complex Filtering: DAX enables the creation of advanced filters and slicers, allowing you to respond dynamically to user interactions and deliver personalized insights.
Ranking and Percentiles: You can easily compute rankings and percentiles using DAX functions, adding valuable context to your data analysis.

Getting Started with DAX: Syntax and Formulas

DAX formulas consist of functions, operators, and references to columns or tables within your data model. Here’s a basic example:

In this formula, ‘Sales'[Amount] refers to the “Amount” column in the “Sales” table, and the “SUM” function calculates the sum of all values in that column.

Real-World Applications of DAX in Power BI

Financial Analysis: Calculate compound interest, present value, and other financial metrics to make informed decisions.
Sales and Revenue Analysis: Create measures for year-over-year growth, sales conversion rates, and profitability analysis.
Inventory Management: Monitor stock levels, reorder points, and inventory turnover ratios using custom DAX calculations.
Customer Insights: Analyze customer behavior, retention rates, and lifetime value to optimize marketing strategies.

Tips for Effective DAX Usage

Start Simple: Begin with basic calculations and gradually explore more complex functions as you become comfortable with DAX.
Optimize Performance: Overuse of certain DAX functions can impact performance. Learn optimization techniques to maintain responsiveness in your reports.
Practice and Experiment: DAX proficiency comes with practice. Experiment with different functions and formulas to unlock new insights.

Conclusion

DAX is the beating heart of Power BI, driving advanced calculations and insights that empower businesses to make data-driven decisions. With its customizability, flexibility, and a wide array of functions, DAX transforms raw data into actionable intelligence. By mastering DAX, you gain the ability to harness the true potential of Power BI and elevate your data analysis game to new heights.

Unlock the power of DAX today and witness the transformative impact it can have on your data analysis journey. You can reach out to us to help you with building insightful Power BI reports or check our other services here!

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Transforming Construction: The Power of the Digital Twins Model

Category

Industry Solutions

Tags

Manufacturing, Industry 4.0

Last Updated

29 Jun 2023

Summary

In the rapidly evolving digital landscape, the construction industry is embracing innovative technologies to enhance project efficiency, reduce costs, and improve decision-making processes.

In the rapidly evolving digital landscape, the construction industry is embracing innovative technologies to enhance project efficiency, reduce costs, and improve decision-making processes. One such transformative tool is the Digital Twins model. With its ability to replicate and simulate real-world construction scenarios, the Digital Twins model is revolutionizing the industry by providing valuable insights and optimizing construction processes. In this article, we will explore how the Digital Twins model can be applied in the construction industry, revolutionizing project management, design, and maintenance.

Digital Twins Model in Construction Project Management

The Digital Twins model in construction project management enables stakeholders to gain comprehensive visibility into the project’s lifecycle. By creating a virtual replica of the physical task, managers can monitor real-time progress, identify bottlenecks, and make informed decisions to ensure timely completion. The model also facilitates effective resource allocation, enabling teams to optimize labor, equipment, and material usage. With accurate data and predictive analytics, project managers can proactively identify and address potential risks, leading to better risk management strategies.

Enhanced Design and Collaboration through Digital Twins Model

The Digital Twins model has the potential to revolutionize the design process in that industry. By creating a digital replica of the project, architects and engineers can visualize and simulate different design options, allowing them to identify potential design flaws and optimize the overall structure. This virtual testing and simulation significantly reduce the risk of costly design errors and rework during construction. Moreover, the model promotes stakeholder collaboration by providing a common platform to share and exchange design information, leading to better coordination and more efficient decision-making.

Optimized Maintenance and Operation with Digital Twins Model

Beyond the construction phase, the model continues to provide value during the maintenance and operation of the built environment. By integrating real-time sensor data from the physical structure, the model enables predictive maintenance, minimizing downtime and reducing costs associated with reactive repairs. Facility managers can monitor the performance of critical systems, such as HVAC and electrical, and proactively address maintenance needs before they become disruptive. The model also facilitates energy optimization by analyzing the building’s energy consumption patterns, allowing for smarter and more sustainable operation strategies.

Conclusion:

The Digital Twins model is reshaping the construction industry, offering unprecedented opportunities for project management, design, and maintenance. By creating virtual replicas of physical assets, stakeholders can benefit from enhanced visibility, improved collaboration, and optimized decision-making. As the industry embraces this transformative technology, it is crucial for construction professionals to stay ahead of the curve and leverage the power of the Digital Twins model to drive efficiency, reduce costs, and deliver successful projects in a rapidly evolving digital era.

Do you want to bring your construction project to the next level? Contact us and let us help you! You can also look at our full portfolio of services!

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Digital Twins in Healthcare: Revolutionizing Patient Care

Category

Industry Solutions

Tags

Healthcare, Industry 4.0

Last Updated

29 Jun 2023

Summary

Digital Twins in Healthcare are transforming the landscape of medical practice, ushering in a new era of personalized and precise patient care. This cutting-edge technology

Digital Twins in Healthcare are transforming the landscape of medical practice, ushering in a new era of personalized and precise patient care. This cutting-edge technology allows healthcare professionals to create virtual replicas of individuals, enabling them to monitor, analyze, and optimize patients’ health in real time. With the potential to revolutionize diagnosis, treatment, and prevention, digital twins are poised to make a significant impact on healthcare outcomes.

What is a Digital Twin

A digital twin is a virtual representation of a patient that mirrors their physical characteristics, physiology, and medical history. By integrating data from various sources such as wearable devices, electronic health records, and genetic profiles, healthcare providers can create a comprehensive and dynamic model of an individual’s health. This digital replica captures the intricacies of a person’s unique biology, enabling healthcare professionals to gain deeper insights and make more informed decisions. However, you can learn more about the concept of Digital Twin in this article.

Applications of Digital Twin Models in Healthcare

Prediction and prevention

One of the most significant applications of digital twins in healthcare is in disease prediction and prevention. Therefore, by continuously monitoring a patient’s digital twin, healthcare providers can identify subtle changes in vital signs, biomarkers, and behaviors that may indicate the onset of a disease. This early detection allows for timely intervention, preventing the progression of illnesses and potentially saving lives. Additionally, digital twins can simulate the effects of lifestyle modifications or treatments, helping individuals make informed decisions about their health.

Patient treatment

In the realm of treatment, digital twins provide a platform for precision medicine. With access to comprehensive patient data, healthcare professionals can tailor therapies and medications to individual needs, maximizing efficacy while minimizing adverse effects. The virtual replica can simulate the effects of different interventions, enabling healthcare providers to optimize treatment strategies and predict patient responses. This personalized approach improves treatment outcomes and enhances patient satisfaction.

Surgery planning and training

Digital twins also play a crucial role in surgical planning and training. By constructing a virtual replica of a patient’s anatomy, surgeons can simulate complex procedures and anticipate potential challenges before stepping into the operating room. This preoperative planning enhances surgical precision, reduces the risk of complications, and shortens recovery times. Moreover, digital twins offer a valuable tool for training healthcare professionals, allowing them to practice and refine their skills in a realistic virtual environment.

Analytics of Patients’ Data

The potential of digital twins in healthcare extends beyond individual patient care. These virtual replicas can aggregate anonymized data from multiple patients to generate population-level insights. By analyzing patterns, trends, and responses to various interventions, healthcare researchers can identify novel therapies, optimize healthcare delivery, and develop preventive strategies. Therefore, this data-driven approach has the potential to drive medical advancements and improve public health outcomes.

In conclusion, digital twins in healthcare hold immense promise for revolutionizing patient care. By creating virtual replicas that capture the intricacies of an individual’s health, healthcare providers can make more informed decisions, tailor treatments, and improve outcomes. From disease prevention and precision medicine to surgical planning and population health insights, digital twins have the potential to reshape the healthcare landscape. The Digital Twins model has numerous applications in other sectors as well.

Contact us if your want to learn more about the different applications of the Digital Twins model! You can also check our full portfolio of services!

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You can never be too young to be a software engineer!

Category

Company News

Tags

Our Stories

Last Updated

16 Jun 2023

Summary

Aleks Milchov has been part of Cosmos Thrace for over a year and a half now. His unique and kind personality, talent, and determination make

Aleks Milchov has been part of Cosmos Thrace for over a year and a half now. His unique and kind personality, talent, and determination make him a vital part of our technical team. Although he is merely twenty-one years old, he has over 3 years of professional programming experience. He is absolute proof that in our area age is just a number and that you should not judge the book by its cover. How the career of this young software engineer is progressing in Cosmos Thrace? Read the rest of the article to find the answer!

How did his Cosmos Thrace adventure start?

Almost a couple of years ago Aleks was still in high school while also working professionally as a software engineer in a big IT corporation. However, he was looking for a new challenge in his career. A challenge that would give him the opportunity to grow not only in terms of technical skills but also in terms of soft and managerial skills.

He stumbled upon a job opening from Cosmos Thrace for a Business Intelligence specialist and applied. After an interview with Viktor Elevterov, the CEO of Cosmos Thrace, it was clear that Aleks was a perfect match for the young and ambitious team of Cosmos Thrace. However, he first started as a React Developer for a software development project within the company.

How is his Cosmos Thrace adventure continuing?

After a few months, Aleks joined another project for a US client of the company. His role was of an implementation specialist. This differed from his past position, however, it enabled him to expand and diversify his skills and knowledge. Currently, he is leading a project for another client of Cosmos Thrace, where a Business Intelligence platform is being developed.

When asked about the things that Aleks likes the most in Cosmos Thrace, his top reason is the people within the company.“I like the people and the atmosphere a lot. We have versatile professionals in the company and we can all learn from each other’s skills. The biggest benefit is that we are really supportive of each other – whenever I have a difficulty there is someone I can rely on and assist me. This start-up vibe is what makes me love the company.”

How will the adventure continue?

Aleks mentions that he sees a big difference compared to his last company and Cosmos Thrace. This difference is presented in the less stressful and chill internal order within the company:

“My former company was a big corporation and the hierarchical order was very strict and well-established. I had one person above me to whom I had to report and look for support. However, in Cosmos Thrace, I am able to communicate freely with everyone. Moreover, my work and contribution are well more appreciated here. This motivates me to keep working and developing my professional skills.”

Therefore, he sees himself playing a key role for Cosmos Thrace in the next few years. Nevertheless, he feels that he can contribute even more by turning his sight a bit from strictly technical tasks and focusing on managerial duties.“I want to shift my focus a bit on management. I want to be a team leader and be able to manage the people on the team. My philosophy is that good management skills can make employees independent and can easily progress in their careers and then be supportive of new employees. In Cosmos Thrace, I have the freedom to go after that opportunity and growth path”.

Work hard, play hard

Being one of the funniest guys in the company, Aleks is always in for a party or a weekend barbeque. During team-building activities of the company, he is always the one to look out for. His endless positive energy and desire for good times are qualities that make everyone enjoy his company.Therefore, at the end of his interview, we asked him to tell a memorable story that he remembers since joining Cosmos Thrace:

“The situation I remember best is when, at one of the team building events organized by the company, we talked about the technologies that everyone is familiar with. However, I was just brought into a new project. During the teambuilding, we discussed my upcoming onboarding in the project with the company’s managerial team in a chill and friendly atmosphere and paved the path forward. That’s the beauty of Cosmos Thrace. We are all united and make our decisions together.”

Are you a young software engineer looking to bring your career to the next level? Check our open positions and find your next dream job!

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A guide to Power BI reporting

Category

Technical Guides

Tags

Power BI, Analytics

Last Updated

07 Jun 2023

Summary

The first steps of every new challenge can often seem difficult and confusing. The purpose of this article is to serve as a guide to

The first steps of every new challenge can often seem difficult and confusing. The purpose of this article is to serve as a guide to power BI reporting. Through it, we hope that new and aspiring BI specialists will be able to navigate Power BI more easily. Read it for insights to help you build your first report in minutes!

Power BI: An overview

Power BI is a business intelligence tool that allows you to generate useful insights based on your data in the form of reports and dashboards. It is a Microsoft product and has a number of interesting features such as supporting various data formats, being able to connect different data sources and powerful visualization formats. You can read a more detailed overview of the platform in this article.

For the purpose of this article, it should be mentioned that Power BI has three main elements:

Power BI Desktop: The main application of Power BI Desktop is to build and design reports
Power BI Service: Through Power BI Service you can share reports and build dashboards
Power BI Mobile App: Used for viewing reports and dashboards with your mobile phone.

The components of Power BI Desktop

As stated above, Power BI Desktop is the element of Power BI that enables report generation and design. We recommend downloading the app from the official Microsoft website and installing it according to the specified steps.

Upon launching Power BI Desktop, a blank report will appear. Around the blank canvas you will find a few important components that deserve a mention:

Top Ribbon – This is where most of the controls and options that you need to build a report are located
Views – This is made up of the report view, the data view and the model view.
Filters – From here you can select different options for filtering the data.
Visualizations – One of the most interesting components of the app, this contains all the possible options for visually representing your data.
Fields – This section contains the tables and fields that are available in the data model.

Key steps to generating a Power BI Report

Now that you know which element of Power BI you need for generating a report, let’s break the whole process down into a couple of simple steps. In the following lines you will see our guide to Power BI reporting.

Importing data into Power BI and supported data sources

The first step when creating a report is to import the data that will construct it. You already know that Power BI enables you to connect data from various data sources. Those can be Excel files, CSV files, SQL Databases, SharePoint lists and many more. Once you have the data on your device, you can easily import it into the app. If, for example, you import an Excel file, upon importing it, you will be able to select whether to proceed with the whole file, a table, a sheet and so on. During that process you will also have the option to directly import or transform the data first, using Power Query.

Data modeling and navigation

The next step of the process is to model the data that you have imported and establish relationships. Those relationships enable you to connect data from different sources. Data modeling is one of the most powerful features of Power BI Desktop. Through it, you can create custom calculations based on the data sets that have been imported. You can then visualize those calculations in an interesting and meaningful way.

Data visualization

Once you have established relationships between different data sets and created calculations, it is time to visualize the data in an organized and attractive way. As mentioned above, Power BI Desktop gives you the ability to visualize data through a variety of options. For example, you can use bar charts, line charts, pie charts, tables, cards, interactive maps, etc. This powerful feature of the app is what makes it stand out from the rest. You can even create your own visualizations, if you have some programming experience.

Afterward, you just need to do some styling and design in order for your reports to look good and be easy to read. Although this looks like a simple step, it is often overlooked by professionals, making their reports unattractive and often unreadable.

Publishing reports

The final step of the process is publishing the report to your Power BI Workspace. Keep in mind that once the report is published it can be found on Power BI Service. Moreover, publishing the reports also publishes the data related to that report.

Gaining a competitive edge with Power BI

Getting useful insights from the data that your business generates is crucial in today’s business world. It can give you the competitive advantage to stand out from your competitors and optimize your operations. To achieve this, effective and comprehensive reporting and analytics of that data are required. Power BI gives you the ability to build meaningful reports in a few very easy and accessible steps. We hope that this guide to Power Bi reporting will enable you to make use of it today!

For more insights on how to maximize Power BI, view our other articles on the topic. You can also schedule a free call and let us help you start your digital transformation journey!

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Should You Consider Digital Transformation as a Way to Gain Competitive Advantage?

Category

Data Strategy

Tags

Data Migration, CTO

Last Updated

12 May 2023

Summary

Many business owners try to figure out how to apply digital transformation amid all the buzz about it.

Many business owners try to figure out how to apply digital transformation amid all the buzz about it. It can take numerous shapes from online payment to labor automation, encompassing concepts like Industry 4.0, Predictive Maintenance and Digital twins. Therefore, a company should aim to implement digital transformation in order to remain competitive and efficient.

Then, how exactly should a company leverage digital transformation in order to build a competitive advantage?

A competitive company should strive to highly optimize its processes. The capacity to design solutions for efficiency improvement is the clearest illustration of how digital transformation provides a competitive advantage. One does not have to be a manufacturer to see the value in using digital technology to improve operations and harvest the benefits of greater process structures.

For example, an AI-based tool for translating voicemail to text will improve the company’s administrative function, enhancing overall efficiency. Furthermore, even simple digitalization of sales procedures saves time and resources for the sales force, which only then can be fully focused on building customer relationships.

The benefits of Digital Transformation:

Increased customer satisfaction:Providing a good customer experience is the best approach to appeal to consumer loyalty. According to a PWC survey, more satisfied customers demonstrate a higher tendency to pay more for services and products. Actually, a company can make its customers happier by providing a platform for customers to share their feedback. Buyers worldwide are accustomed to using digital devices to manage the bulk of their lives. As a result, a business having a digital presence is more than essential. It is a must that allows the company to communicate with its target audience. Furthermore, big data adoption may assist businesses in better understanding their consumers and delivering the best possible service to them.
Improved company efficiency:Employees are the lifeblood of a business. Creating a well-optimized and progressive working environment would immediately lead to improved performance. The present market for digital accelerators offers a wide range of cost-effective solutions for practically every aspect of business processes. The digital workplace is the “one-stop-shop” for all the employees in the sense of tools they would need daily – group chat applications like Slack and Blitz, project management platforms like Podio or Trello, and larger and more sophisticated applications like CRMs. Of course, if businesses do not focus on effective and speedy adoption of these technologies, the integration of so many productivity-boosting digital tools might backfire.
Widened partnership opportunities: Digital transformation allows companies to join exclusive groups of like-minded potential partnerships. By removing constraints such as location and time, a digital implementation may provide companies with attainable prospects, which before seemed to be impossible. Business owners can find new partners and markets in other countries due to digital transformation.

The way forward

There is a significant tendency among businesses to incorporate technology to increase efficiency, productivity, and future opportunities. Almost every young company and startup design their business strategy from a technical aspect. With that being observed, if businessmen do not want their businesses to stagnate, it is time to think about how digital transformation might help them gain a competitive advantage. Being leaders, it is their responsibility to acknowledge the benefits of digital transformation today so their company could enjoy the prosperity of tomorrow.

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Can a Rock and Roll Star be a Junior Python Developer?

Category

Company News

Tags

Python, Our Stories

Last Updated

22 Apr 2023

Summary

Martin Dimitrov has been in Cosmos Thrace for almost two years and proved to be a unique part of our team. His background and experience

Martin Dimitrov has been in Cosmos Thrace for almost two years and proved to be a unique part of our team. His background and experience might at first sight seem not inline with the background of a typical junior Python developer but that is what makes him stand out of the crowd.

His ability to commit himself to delivering the best result possible and make everyone in the room laugh have positioned him as one of the most important team members within the company. Moreover, almost two years later Martin and the company are both developing with the same speed and determination.

The journey to becoming part of Cosmos Thrace

“I have played guitar for nearly 10 years and after COVID-19 hit I was wondering what else to do. I decided to enroll in a free course on programming fundamentals and try it out. I haven’t had any passion about it before I started so I just wanted to experiment with it. I started to gain a little bit of knowledge after a few weeks and my interest started to grow. I like it so much that I did it non-stop for 6 months straight for around 20 hours per day.”

At that moment Martin enrolled into two more programming courses that were building on the fundamentals. Although his schedule became quite busy, he was attending all the lectures and completing all the homeworks properly. At that moment, the head lecturer who was overseeing both courses decided to contact Martin and offer him a job opportunity.

The lecturer connected Martin and Viktor Elevterov with each other. Viktor interviewed Martin, liked his attitude and determination and decided to offer him a job as Junior Python Developer at Cosmos Thrace.

This was a pivotal moment for Martin and as he explains:

“At the beginning I was scared because during my studies I have done some abstract exercises but not real-world cases. That frightened me a bit but I started anyway. While working for Cosmos I also continued with my studies. So 1 year later I am still here and love it!”

The current working environment in Cosmos Thrace

“I love the atmosphere here! Everybody is trying to help each other and the company. All the people are working really hard and it may sound like a cliche but it doesn’t feel like working. I am enjoying it. Yeah, sometimes there is a rush and pressure but it is part of the game. Moreover, I feel like as the company grows I am growing with it as well.”

That is how Martin sums up the current working environment within Cosmos Thrace. For him as a junior python developer at the beginning of his career it was very important to have someone to rely on whenever he faced a challenge. Also, he needed people who were not there only to tell him what to do but to push him into finding solutions on his own. In Cosmos Thrace he found the perfect mix of both and looks forward to becoming even more knowledgeable and independent in the future.

The future ahead

“I definitely see myself staying at Cosmos Thrace for the next few years! I have also discussed that with Viktor. I really appreciate everything that the company has done for me – I had basically zero practical experience and Cosmos Thrace hired me and gave me the opportunity to grow and develop. I am very grateful for that.”

At the moment Martin tries out different areas of programming by participating in various projects. His main goal is to develop his generic knowledge and skills in programming and afterwards to specilise in a concrete area. Moreover, parallel with his work Martin continues his studies in order to gain additional knowledge.

The Rock and Roll life outside Cosmos Thrace

“Music is my real passion. The discipline that I gained from playing guitar helped me a lot when I started studying software engineering. Now, doing them both is a bit hard as they demand a lot of time. I am doing my best though – I am still developing myself as a guitar player. The band where I play we are playing rock and heavy metal music. I am also experimenting with different genres such as jazz and classical music.”

Yeah, as you can see “It’s a long way to the top, if you wanna rock and roll”. Although Martin was faced with a lot of challenges and uncertainties when Covid hit, he managed to make the best of it – start a professional career as a python developer and in his free time do what he loves the most – play guitar and live the rockstar life!

Do you want to learn more about our open positions? Check our careers page!Do you want to learn more about our services? Check our services page!

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What Do We Call Business Intelligence?

Category

Technical Guides

Tags

Analytics, Power BI

Last Updated

09 Mar 2023

Summary

Individuals and even businesses have different views of business intelligence (BI). Some claim it has more to do with the visual component, some with the

Individuals and even businesses have different views of business intelligence (BI). Some claim it has more to do with the visual component, some with the analytical, and yet others with just the attempt to access their data. In fact, the genuine answer to the definition has less to do with technology and more to do with the end goal: how can a company reinvent itself and start making more data-driven decisions? What is the future of business with business intelligence?

Business intelligence is more than a reporting tool or platform. It is a business strategy that allows a company to act and make choices more quickly.

The quantity of various sources from which data is taken is the most common difficulty encountered when it comes to reporting and analyzing data. Depending on the reasoning and source, data may be in a single database, human labor-intensive Excel files, or a more EDW-type platform, resulting in a variety of solutions to the same query. Nothing is more frustrating than entering a meeting with data you believe is right only to discover that someone else has different answers to the same question. So, how can we solve this issue?

Database architecture is just as much an art as a dashboard design.

An entire system can lock up due to a single improper joint between two important tables. If a table has too many columns, it may become useless in the future. The balance between these two parts of business intelligence is more vital to comprehend. No matter how appealing a dashboard appears, it will be ineffective without a solid database architecture. The same can be said for dashboard design in reverse.

While various businesses are at different stages of data reporting maturity, there is a lot that can be done on both the front and back end. Often, an organization will focus its efforts on one side or the other. For example, a firm may have one of the top data warehouses in the industry, but the IT department has locked it down, and all reporting is based on flat data files provided by IT and evaluated in Excel. Another company may have some of the greatest aesthetics and dashboards, but its data approach is still segregated. The ideal situation is for a company to employ a strategy in which IT opens data to analytics teams, who are then enabled to construct powerful visual dashboards with the help of business users. This can only be done when a company first considers the whole operations that stand behind the company.

The most crucial stage in integrating BI in an organization is to assess not only the present architecture but also how people interact with data. A DBA could poll individuals to determine what metrics matter the most to them and what sorts of decisions they are making with the data. All of this data will aid a business intelligence architect in making the best possible selections about the sort of platform that an organization requires.

The next stage in a Business Intelligence journey is to learn more about the types of platforms that might be used inside a company.

When it comes to Business Intelligence, each firm will have specific, “tailored-made” requirements. Some businesses are heavily data-driven and just want assistance in integrating the entire platform with a powerful visualization tool. Some people have no notion of what to do with their data and are working on a tight budget. Then the large corporations are always seeking to innovate and position themselves above their competitors. For each sort of requirement, there are multiple tactics and platforms. We love to deliver custom data solutions so check out our services page.

Therefore, there are some products, such as Birst or Qlik, that advertise themselves as a one-stop-shop for all things BI. These platforms might cover all the elements of a robust Business Intelligence strategy (also known as “Plug-and-Play”), from data warehousing to data modeling and visualization. In reality, these platforms are designed to be highly sticky within a business and attempting to develop outside of the platform can be rigid. This may be beneficial to a smaller firm since it helps them to streamline their data management and focus on growing their business. It might also work for a corporation with several data sources for different divisions, and the platform is utilized as a data aggregation tool for executive reporting.

To conclude, business intelligence is not just a collection of tools and dashboards. It is more of a platform but also a strategy. One that enables an organization to shift from reactive decision-making based on a descriptive tale to proactive decision-making based on data-driven analytics. Decisions that give the decision-maker confidence in the outcomes they are observing and seeking. This approach is unique to each company, and it must be determined not by creating criteria and comparing tools to what they can achieve, but by looking at the company as a whole and committing to a fundamental change in how business users interact with data.

Resource

What do a business analysis career and making desserts have in common?

Category

Company News

Tags

Our Stories

Last Updated

03 Mar 2023

Summary

Anna Kostova has been part of Cosmos Thrace for more than a year and during that time she has proved to be valuable for the

Anna Kostova has been part of Cosmos Thrace for more than a year and during that time she has proved to be valuable for the well-being of the company! She is currently at the end of her university journey, doing her Bachelor’s in Liberal Arts in Sciences with a Major in Cognitive Neuroscience. Currently, she is pursuing a business analysis career.

What led her to take this professional path was her love for helping people become their best selves and the opportunity to do it in a corporate environment.

How did her Cosmos Thrace adventure start?

Actually, two of her former high-school fellow students are already part of Cosmos Thrace. Once Anna decided to start looking for a job Svetoslav and Viktor shared their feedback about working for the company and also told her that there was an open position for a business analyst.

How is her Cosmos Thrace adventure evolving?

Anna has proven herself to be an integral part of our team as she is involved with everything from project management to team building. Although she has a lot of different tasks, instead of daunting her, motivates her even more. In her own words: “It is challenging at times because you have to have a lot of intrinsic motivation to strive and figure things out for yourself. However, that doesn’t discourage you from doing your research into areas that are not very familiar to you. At the same time, the freedom we have brings a lot of responsibilities which I do enjoy”

Another thing that makes Anna enjoy her Cosmos adventure is the environment within the team.

How will the adventure continue?

What do a business analysis career and making desserts have in common?

When it comes to life outside work, Anna has a couple of hobbies that she loves. The first one is listening to music, especially alternative rock. She says that this creates a really emotional space for her which brings calm and clarity.

Anna also loves cooking. While has a lot of recipes up her sleeve what she loves the most is baking desserts.

In her opinion, baking has a lot in common with her job as they both require a lot of precision and a calm mind. She tries to bring precision from the cooking table to her daily tasks and believes that they complement each other. Moreover, both in cooking and in working at Cosmos, Anna does what she loves – putting a smile on people’s faces 🙂 Learn about our open positions here.

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Which IoT cloud solution should you choose and why?

Category

Technical Guides

Tags

Azure, AWS, GCP

Last Updated

23 Feb 2023

Summary

Nowadays cloud computing has become a necessity and it is no longer a question of whether businesses need it or not. Businesses should choose a

Nowadays cloud computing has become a necessity and it is no longer a question of whether businesses need it or not. Businesses should choose a provider, however, there are various options on the market. The top three that stand out as top three cloud providers are Azzure, AWS, and Google Cloud. But even with that in mind the question “Which IoT cloud solution to choose” remains. In this article, we will go through the major factors of each hyper scaler so we can lessen your choice.

Why cloud computing?

But before that let’s answer the question “why cloud computing is so important”. In fact, there are countless reasons why it is vital for businesses, but we may say that the main reason is that it provides a great number of services in the public cloud. The global market value of the cloud computing industry is estimated to be $371 billion, and it is expected to reach $832 billion in 2025. (researchandmarkets.com) And the three main hyper scalers hold more than 80% of the global market share for cloud services for IoT.

According to IoT Analytics, cloud services are classified into three main categories – application management, device management, and data management. Application management aims to help developers manage IoT applications as the name suggests. Device management service enables connected devices to work accurately by running updates and patches for the software and applications. Data management allows seamless connectivity, storing, and analyzing of IoT data.

The top cloud service providers and their best qualities! Which IoT cloud solution to choose?

AWS

The first hyperscaler we will look at in detail is AWS, which is actually the first provider to introduce public cloud services in 2006 and expand it to IoT services later in 2015. As the first cloud provider, AWS remains innovative because of the earlier foundation that it builds on. As you can probably already tell, its biggest strength is public cloud services. Moreover, AWS is the leader in serverless computing because of its services Lambada and Athena. AWS is identified as the most enterprise-ready supplier due to its simplicity in delivering scalability, superior privacy measures, and security to its clients. The number of services that it offers is more than 200 in order to cater to any demand of its millions of users. In terms of IoT-focused services, AWS offers 13 and it has the largest portfolio. IoT Core is a service that ensures secure bi-directional communication for IoT devices; however, the client can decide whether to add a Device Management service to it. Last year, the provider added two new services (AWS IoT TwinMaker and FleetWise) and three other services that are relevant for IoT (RoboRunner, ExpressLink, and AWS Private 5G).

Google Cloud

Google Cloud was released in 2008, two years after AWS, however, it managed to keep its position as one of the strongest challenges on the market. The greatest advantage of this cloud solution is its analytics capabilities. Although Google is the creator of the most-adopted cloud services, Google Cloud is a lot different from AWS and Azure. Its strongest sides are analytics, Big Data, AI, and ML. The most used cloud service is called Cloud IoT Core and it can be combined with other services by Google to come up with an end-to-end solution way easier. Clearly, Google is not focusing on IoT services for now, as they offer only one.

Microsoft Azure

Last but not least, we will talk about Microsoft Azure. It has the strongest enterprise customer base of the three cloud solutions that we discussed in this article. The main reason why most clients prefer Azure over another cloud solution is that it is easy to add it to already existing Microsoft tools and usually companies are already using Microsoft products. That makes the transition to Azure easier than having to learn a completely new interface. Moreover, the company has IoT as a priority, like Google Cloud. Azure has nine IoT services in total, but the most used services are IoT Hub and Azure Edge. Azure allows data transferring without leaving its network, resulting in decreased costs, better security, and lower latency. In contrast to AWS Azure offers industry-specific services (Microsoft Cloud for Manufacturing). Another key differentiator is its ability to integrate with business intelligence tools like Power BI. The integration between Azure and Power B is very simple. On the other hand, integrating Power BI with AWS or Google Cloud might cause some problems. What is more, the UI is very user-friendly, even for people that do not have a programming background. And last, but not least while AWS features strong application management, Azure is more oriented toward device management.

We hope that this article has helped you understand a bit more about cloud computing and the top three providers. If you are still wondering which IoT cloud solution to choose, feel free to contact us. We will be more than happy to help! Also, check our other services!

Resource

What is the better place to develop your Python skills – A university in the Netherlands or a Bulgarian AI company?

Category

Company News

Tags

Python, Our Stories

Last Updated

16 Feb 2023

Summary

Svetoslav Ivanov has been part of Cosmos Thrace for almost two years. He joined the company as a student in a Dutch university where he

Svetoslav Ivanov has been part of Cosmos Thrace for almost two years. He joined the company as a student in a Dutch university where he is studying Cognitive Science and Artificial Intelligence. He applied for the job of Python developer as he had already been taking Python courses but wanted to gain some real-life experience in the area and develop his Python skills.

Two years later and on the verge of his university graduation, many colleagues would argue that he has become one of the best Python professionals in the company but also a great friend, colleague, and often the funniest guy in the room.

The journey to becoming part of Cosmos Thrace

The path to becoming part of Cosmos Thrace was pretty straightforward for Svetoslav. He was looking for a job to complement his studies when he found out that we were hiring. From the first interview, it was obvious that we shared common values and common goals.

Regarding the hiring process and the incentives behind applying for the job he says the following:

“I was very passionate about Python programming in particular, which was one of the main languages used in Data Science and AI, hence in my university program, so I was very familiar with it. I found out that there was an open Python Developer position here at Cosmos Thrace, so I just wanted to try it out and get real-life programming experience. Looking at it now, I think that it was a great decision.“

The current working environment in Cosmos Thrace

“Being part of the company for almost two years now, I can say that everyone in it is open to a lot of changes and suggestions. Both on a project basis and on a company basis, you can propose a lot of things and your opinion is always heard. Another big benefit is that you have access to people who are higher up in management and are always open to assisting you with any issues that you might have.”

On top of that Svetoslav mentions that the environment within the company is very friendly, but at times can be competitive, leading to minor project disagreements. However, he took this as a learning experience, stating that it helped him improve his managerial and soft skills.

Professional and personal development in the past two years

“Honestly, I develop my Python skills way more at Cosmos Thrace than at university. I got involved in projects that demand really advanced knowledge and that made me grow a lot in terms of Python development. The projects were harder than what I have been used to but also the colleagues were more knowledgeable and were always open to helping me , while leaving the space for me to be more independent.

“However, in the future I see myself developing more on the management side rather than on the software engineering one. I had the opportunity to do it a few times in the past year and I really enjoy it. I feel like I am good at delegating tasks and also having an overall view of the state of the project and what needs to be done for it to smoothly move forward.”

Life outside Cosmos Thrace

Being both enrolled in university and working professionally has its downsides as well. As Svetoslav mentions, currently he does not have enough time to practice his favorite hobbies. He is generally a very active person as most of his hobbies are in the sporting sphere. Before having such a busy schedule he was often in the gym doing some weightlifting training or at the boxing gym, sparring.

“Also I used to love running – I have actually completed the Sofia marathon three times. So I have a lot of kilometers behind me. Actually, programming is a lot like running a marathon – it takes a lot of time and effort but you are always satisfied with the end result. I can also mention gaming as something I like – I would say that most of the people my age fell in love with programming because of computer games.”

Take a look at our careers page in order to be able to develop your Python skills as well!

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Trading off a job at one of the biggest European companies for a startup. Is it worth it?

Category

Company News

Tags

Our Stories

Last Updated

31 Jan 2023

Summary

Darinka Velikova is one of our senior software developers and has been part of the company for more than six months. She is mainly focused

Darinka Velikova is one of our senior software developers and has been part of the company for more than six months. She is mainly focused in the field of data warehousing and is now one of our most knowledgeable team members in it. Prior to joining Cosmos Thrace, she worked for 12 years in one of the biggest European telecommunication companies. So, she went from a big company to a startup but does it worth it?

If we had asked her a year ago whether she would change her previous job and join a startup, her answer would have definitely been “No”. However, let’s find out what made her trade off 12 years in a well-established international company for a startup that is now making a big statement to the world!

The journey to becoming part of Cosmos Thrace

“Viktor Elevterov (our CEO) and I knew each other because in the past we have worked together on some projects. So, he gave me a call and offered me the opportunity to join Cosmos Thrace. At that point, I had already worked in my former company for more than ten years and the offer to join a startup was at the same time interesting but also very different from what I was used to.”

She says that at that exact moment she was not particularly looking for a new job. However, Viktor being so persistent convinced Darinka and she saw an opportunity to develop her professional career based on the innovative and interesting projects the company works on.

The current working environment in Cosmos Thrace

“I have been in the company for around six months now. What I like the most so far is the team and people I work with. I like the fact that we help each other constantly and whenever you have a problem or question everyone is open to assisting you or giving you an answer.”

For Darinka, being able to choose where to work from is a very important factor as well. What she enjoys a lot is the freedom that each team member has in choosing his or her workplace.

“In my opinion we should all have the right to choose where we work from. At Cosmos Thrace we have both an office where we can go whenever we want and we can also work from home.

Darinka feels she is in the right place in Cosmos Thrace because there are constantly new and interesting things happening.

“For example, in our team there are many juniors and when I joined, the project we worked on was starting to lean away from the proper data warehouse structure. So, I had to assist them to put everything in place. This was very interesting for me and I really enjoy such challenges.”

Life outside Cosmos Thrace

Besides being a well-established professional in her field, Darinka greatly enjoys her free time. Her main passion is folk dancing, which she has been doing for many years, and until recently, was even performing on stage.

“At the moment, I am practicing just for the joy of it. It makes me forget all the worries in life and while I am dancing, I’m always smiling. It is a feeling you cannot describe but is definitely something that relaxes my mind and re-charges my batteries.“

Are you also considering transitioning from a big company to a startup? Give us a call or check out our open positions!

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What are the top 5 most lucrative use cases of AI until 2025?

Category

Data Strategy

Tags

MLOps, Logistics, Manufacturing

Last Updated

19 Dec 2022

Summary

With every year, AI and ML become more mature and well rounded. At the same time data quantity and quality grow at breakneck speeds.

What does this mean for the future of your business?

We recently got a peak at the forecast for global revenues of different fields of AI (see below).

Naturally, this got us thinking: ‘’Why these areas?’’, so we combed through a lot of research, both our own and externally sourced.

So, here are our findings on the top 5 most lucrative use cases of AI until 2025.

1.Static image recognition, classification, and tagging

A computer ‘‘vision’’ technique that allows for the interpretation and categorization of what is “seen” in images or videos. Often referred to as “image classification” or “image labelling” (or tagging), this task is essential in solving many computers vision-based ML issues.

Some interesting current and potential uses:

Customer analysis – learning more about individuals through detection of logos or text in the products they consume and use.
Facial verification of users – Face ID
Tagging of images – using tags or keywords on images to organize and sift through them better. Enables organization and categorization.
Content moderation for different purposes related to social media
Accessibility – for the visually impaired.
Visual Search – using real images to procure more reliable online searches. Allows for better targeting based on shopper behaviours and interests.

2. Algorithmic trading strategy performance improvement

This uses AI to place, buy and sell orders automatically according to a specified set of rules or logic. These rules are collectively referred to as the trading algorithm.

So, why is it lucrative?

With stock, bond, Forex, Crypto, and all other forms of trading currently using AI algorithm trading, optimization and improvement in this field can only bring higher and higher returns. From reduced transaction costs to precise execution of trades, and accurate formulation of decisions while accounting for multiple factors.

3. Efficient, Scalable processing of patient data

AI and ML are used in the medical field to search through medical data and find insights to help improve health outcomes and patient experiences. This also helps medical practitioners by saving valuable time otherwise spent in searching through data. Clinical decision support provided by AI is still improving. CT scans, x-rays, MRI’s, and other graphical data is also analysed with great success.

Disease detection and diagnosis is also something that AI can provide. The current pandemic boosted the development of AI in healthcare, and this growth is predicted to increase greatly by 2025, with accelerated drug development being a massive part of the predicted revenue. AI can formulate better drug designs and find new drug combinations easier.

4. Predictive Maintenance

When it comes to why Predictive Maintenance is expected to grow so much, there is one main prediction in development – cognitive maintenance.

Cognitive maintenance – based on cognitive analytics where AI can learn from past actions and results, can look for correlations and prescribe courses of action. It changes a company’s behaviour from reactive to prescriptive. In short, data from connected equipment and products is analysed, combined with service knowledge and data, and using the result to improve equipment and uptime.

So, while Predictive maintenance accurately identifies and predicts faults in equipment, cognitive maintenance then provides the engineers responsible with detailed information on how to prevent or repair the equipment.

5. Object identification, detection, classification, tracking

Object detection, similar to the first point of our article, is used to analyse pictures and video frames. The difference however is that in the case of object detection, the AI analyses the image or video frame looking for a specific visual object. The goal is to answer the question ‘’What objects are where?’’.

How will this field earn the predicted revenue?

There are many applications, such as healthcare monitoring, autonomous driving, video surveillance, anomaly detection, or robot vision. Agriculture has also recently started to benefit from this technology with animal detection – counting and monitoring. The broad and varied uses for this tool are what makes it lucrative.

Conclusion

We believe that the list is surprisingly on track with which areas of AI will top the charts, however, there is an argument that the top 5 most lucrative use cases of AI are interchangeable, and only time can show which developments prove to be ahead of others. In any case AI will keep developing at breakneck speeds and drive our possibilities forward.

Cosmos Thrace believes that implementing AI in your business can future proof it, as having the foundations set now, can lead to much easier adoption of future technologies. Get in touch if you would like to discuss the ‘’why?’’ and ‘’how’’ of the most lucrative aspects of AI development.

Resource

How to increase executive confidence in AI-assisted decisions

Category

Data Strategy

Tags

MLOps, CTO, AI Ethics

Last Updated

16 Dec 2022

Summary

AI-driven solutions are businesses’ key preferences when it comes to optimization. Or is it? Despite today’s businesses’ increasing expenditures on artificial intelligence (AI), the C-suite’s

AI-driven solutions are businesses’ key preferences when it comes to optimization. Or is it?

Despite today’s businesses’ increasing expenditures on artificial intelligence (AI), the C-suite’s belief in AI may be hit or miss. Is it just that CEOs are wary of a new and unproven technology, or is there anything more to it? Executives have traditionally opposed the use of data analytics for higher-level decision-making, preferring instead to depend on gut-level choices based on field experience rather than AI-assisted suggestions. So, how to increase executive confidence in AI?

In several sectors, AI has been widely embraced for tactical, lower-level decision-making – credit scoring, upselling suggestions, chatbots, and machine performance management are just a few examples. However, it has yet to demonstrate its worth when it comes to higher-level strategic decisions like recasting product lines, altering corporate strategy, reallocating human resources across departments, or forming new partnerships.

The first step in increasing executive confidence in AI is by creating reliable models that provide businesses with consistent insights.

Executive apprehension can originate from bad experiences, such as when an AI system produces false sales results. A lack of data quality is a common factor in almost every unsuccessful AI effort. Structured data was prevalent in the traditional business model, which categorized data as it entered from the source and made it very straightforward to put it to immediate use. However, AI also consumes large volumes of unstructured data in creating ML and DL models.

Therefore, it is no surprise that many data scientists devote half of their time to data preparation, which is still the most important task in developing dependable AI models that produce accurate results. Context and dependability are essential for gaining executive confidence. There are various AI tools available to aid with data prep – from synthetic data to data debiasing and data purification.

Secondly, after the whole data preparation, there should also be an avoidance of data biases to boost confidence in executives.

Executive apprehension may stem from a well-founded fear that AI outcomes would lead to discrimination inside their businesses or harm customers. In the same way, AI bias might be swaying company choices in the wrong direction. When an AI model is trained on skewed data, the model will be skewed and provide skewed recommendations.

Hence, data utilized in higher-level decision-making should be extensively examined to ensure that executives have confidence that information is proven, authoritative, validated, and derived from trustworthy sources. It must be free of known discriminatory practices that can cause algorithms to be skewed. For example, discrimination may be considerably decreased at a low incremental cost by regulating categorization accuracy. Controlling discriminating, reducing dataset distortion, and retaining usefulness should be the focus of this data pre-processing optimization.

And here we reach the question of AI ethics in decision-making processes.

Executive reluctance may reflect the reality that businesses are under great pressure to guarantee moral and ethical operations. Currently, research institutions try to apply human values to AI, by converting them into engineering terminology that robots can grasp. For instance, Stuart Russell, a computer science professor at the University of California, Berkeley, pioneered the Value Alignment Principle, which basically “rewards” AI systems for more acceptable conduct. AI systems or robots may be taught to read tales, understand appropriate sequences of events, and better reflect successful behavior.

Lastly, we should consider transparency when implementing and relying on AI solutions.

If there is a lack of transparency, executives may be hesitant to absorb AI judgments. Thus, the data used to train algorithms must be stored securely, validated, audited, and encrypted to be held accountable. Immutable and auditable storage is also possible with emerging approaches like blockchain and other distributed ledger technologies. Furthermore, a third-party governance system should verify that AI choices are not just understandable but also founded on factual data. Ultimately, it should be feasible to demonstrate that a human, given the identical data, would come to the same conclusions.

For strategic reasons, more AI-assisted decision-making will inevitably be observed in the executive suite. Furthermore, AI can aid people in decision-making to conduct enhanced intelligence, rather than deliver perfect insights. Assuring that the output of these AI-assisted judgments is based on trustworthy, impartial, explainable, ethical, and transparent insights will help business leaders have confidence in AI-assisted decisions not only today but also in the future.

Did we answer the question “How to increase executive confidence in AI”? If yes, book a free call with us and let us help you in you digital transformation journey!

Resource

How to Get Returns from AI Investments?

Category

Data Strategy

Tags

MLOps, Cost Optimization, CTO

Last Updated

16 Dec 2022

Summary

Now that the AI buzz has worn off, the technology’s major problems are focused on making profits rather than finding out how to make it

Now that the AI buzz has worn off, the technology’s major problems are focused on making profits rather than finding out how to make it helpful. AI can offer great value for many enterprises, thanks to the expanding number of AI professionals and machine learning services. When it comes to AI, however, corporations frequently fail to recoup their initial expenditures. Moreover, the first question they ask before starting the implementation is “How to get returns from AI investments?”

The foundation of the problem is that it is impossible to earn economic returns on technology that has not yet been put into production. Furthermore, even when AI programs are launched, they frequently fail to deliver the desired results.

However, companies who want to get higher returns on AI investments should first consider that AI is data-driven

Surprisingly, a lack of data culture is one of the most common issues that businesses encounter when it comes to reaching AI’s full potential. AI attempts will almost certainly fail if the company’s management and senior workers lack data competence. Even the most carefully developed AI systems will fall short of their full potential if the workforce does not use data-driven decision-making. Another common AI implementation problem is a lack of change management.

Artificial intelligence (AI) frequently requires considerable changes in organizational structure and strategy, as well as personnel attitudes and abilities. As a result, companies should make the change of management a key aspect of any AI deployment strategy, and make sure the company’s leaders have the skills and motivation to develop an AI-centric culture.

While it is tempting to construct large-scale AI systems, focusing on low-hanging fruit is typically a better strategy

It could be a good idea for a company to first start with robotic process automation (RPA), which is less expensive than AI and has a faster return on investment. RPA is non-invasive, which means it does not disturb the flow of existing systems as many AI solutions do.

Small steps and wins in AI initiatives can also assist justify more ambitious AI expenditures and guarantee stakeholder buy-in in the future.

How to define proper objectives when it comes to AI deployment.

Clear expectations regarding the consequences of an AI program are critical. End users seldom participate actively in AI initiatives, so when the technical team creates faultless AI systems, they add minimal value to the business. Therefore, it is vital to include all stakeholders in the project from the start.

Furthermore, AI initiatives frequently provide benefits that cannot be quantified. Improved employee happiness or a better customer experience, for example, is significantly more difficult to monitor than cost or time savings. Let’s imagine a company wants to develop an AI solution to help the IT staff categorize tickets faster. First, because the system will have to decipher free-form text using natural language processing, it will not be perfect at the very beginning. As a result, the team will need to calculate the acceptable mistake rate and factor it into the ROI calculation.

In addition, if there is a major issue that requires quick attention from IT workers, and an AI system incorrectly classifies the ticket as low priority, this will complicate ROI calculations because it is difficult to quantify the negative consequences of such a situation. Hence, it is crucial to start with initiatives that can be adequately quantified in terms of ROI. Many industrial organizations, for example, are able to achieve economic returns on AI quality control programs since their ROI is quite simple to calculate.

To conclude, the AI implementation journey requires high AI maturity and a great focus on business analysis.

So, how to get returns from AI investments? Generally, firms that are more mature and experienced have a higher chance of benefiting from AI. Data governance standards, sophisticated training programs, performance monitoring systems, and specific project goals are common features of such businesses. These are significant distinctions between firms that thrive and those that fail when it comes to AI deployment.

Given the unpredictability of project success rates, AI, more than any other technology, needs a strong foundation in key management areas. The ability of a company to track, measure, and manage operations is typically linked to its likelihood of benefitting from AI. Do you need help with your AI implementation? Book a free call or look at our portfolio of services!

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What is The Role of The Digital Twins Model in The Industry 4.0 Revolution

Category

Industry Solutions

Tags

Manufacturing, Industry 4.0

Last Updated

16 Dec 2022

Summary

The concept of a digital twin has been around since 2002. However, it has only recently become cost-effective to execute thanks to the Internet of

The concept of a digital twin has been around since 2002. However, it has only recently become cost-effective to execute thanks to the Internet of Things (IoT). The internet has been buzzing with the catchphrase “Digital Twin”. It assists bridge the gap between the physical and digital worlds in the age of Industry 4.0. The digital twins model is a living, breathing virtual representation of a physical product. These models serve many purposes, but they all provide a strong link to the product for diagnostics and design adjustments. Digital twins are increasingly being used by businesses to enhance their products in ways that were before unthinkable or unachievable.

But before businesses appreciate the advances of digital twins, they should be familiarized with the core meaning behind the term.

A digital twin is a virtual reproduction of a physical product, process, or system at its most basic level. It uses sensors to capture real-time data on a tangible item. It then acts as a link between the physical and digital worlds. Afterward, this information is used to build a digital copy of the thing. The copy may subsequently be comprehended, examined, altered, and optimized. Virtual prototyping, hybrid twin technology, virtual twin, and digital asset management are some of the other terms that have been used to characterize the technology over the years.

A digital twin is constructed from the ground up by experts, usually in data science or applied mathematics.

These programmers investigate the physics that underpins the physical object or system being emulated. Then use that information to create a mathematical model that replicates the real-world original in virtual space.

The digital twins model is designed to take input from sensors that collect data from a real-world equivalent. This enables the twin to imitate the physical device in real-time, providing information on performance and potential issues. The twin could also be built on a prototype of its physical counterpart. Therefore, it can provide input as the product is refined; in this case, the twin could even act as a prototype before the real version is constructed. Moreover, a digital twin can be as complex or as basic as one prefers. The amount of data used to create and update digital twins defines how accurately it is imitating a physical object.

Digital twins are masterminds that can help businesses worldwide innovate and perform better.

The role of digital twins is to provide value in the form of lower maintenance costs, faster time to market, and better product management. Companies can easily integrate digital twins into their manufacturing process as technology advances and new ideas emerge. While digital twin technology is predominantly employed in manufacturing, operational processes still adapt to the industrial revolution, thus, they are rapidly changing.

And soon Digital Twins will be an unseparated part of the future of businesses.

Implementing them in hospitals or commercial real estate facilities, for example, has the potential to benefit not only building administrators or owners, but also the people who work and live there. In this way, they can be used to solve huge challenges and create long-term value by taking a people-centric approach, then looking at problems and context, and ultimately adding IT systems and linked devices.

Digital twins are the next stage in the digital journey for enterprises and organizations that currently employ IoT. They can be used to increase efficiencies, optimize operations, discover problems before they happen, and even invent new products in the future.

Contact us, if you want to learn more about how the digital twins model can benefit your company. You can also look at our full portfolio of services!

Resource

The rise of Quality 4.0 in Manufacturing

Category

Industry Solutions

Tags

Manufacturing, Data Quality, Industry 4.0

Last Updated

16 Dec 2022

Summary

The fourth industrial revolution, sometimes known as “Industry 4.0,” has emerged as a result of recent technological advancements. It’s a revolution fueled by the exponential

The fourth industrial revolution, sometimes known as “Industry 4.0,” has emerged as a result of recent technological advancements. It’s a revolution fueled by the exponential expansion of disruptive technologies, as well as the changes they bring to the workplace, labor, and markets. In the context of Industry 4.0, the term “Quality 4.0” in Manufacturing refers to the future of quality and organizational excellence.

Quality 4.0: Principles and Implementation

It is critical that quality professionals assist their organizations in making the link between quality excellence and constant change. Utilizing quality concepts to facilitate transformation and growth is highly connected with three important factors – people, processes, and technology.

Quality 4.0 in Manufacturing is more than just a technological advancement. It’s a new approach for quality professionals to manage quality using today’s digital tools and figuring out how to use them to achieve excellence via quality. They may upgrade their job from enforcers to navigators by successfully guiding enterprises through digital disruption and toward excellence.
The necessity for faultless processes remains the same, if not more vital, as Industry 4.0 automate more and more activities. Existing procedures will be disrupted, necessitating the training of the next generation of workers to execute new processes and strategies. Quality is a critical link in the digital transformation process. Therefore, the strategic level should consider to ensure its long-term viability.
Organizations’ platforms, such as processes, systems, data, operations, and governance, must keep up with technology, which is expanding ten times faster than it used to. Technology is also a leveler since it allows everyone with the appropriate concept and intent access to capabilities that were previously only available to giant corporations.

Technologies

Quality 4.0 technologies that apply in Industry 4.0 are Machine Learning (ML) and Artificial Intelligence (AI). These include text analysis, recommendation systems, email spam filters, fraud detection, forecasting, computer vision, language processing, and chatbots. Moreover, critical Quality 4.0 technologies are Mobile technology, Augmented Reality (AR), Virtual Reality (VR), and Cloud technologies. However, quality excellence requires a high level of mastery in data collection, which makes Big Data and Blockchain inseparable part of Quality 4.0.

Value Proposition

One advantage of Quality 4.0 is the improvement upon human intelligence which simultaneously results in speed and quality of decision-making being increased. The above-mentioned technologies would also improve transparency and traceability when implemented. Especially in industries with high complexity, specifics, and exceptions in processes and know-how systems that can support organizations with the knowledge reservation and transfer become critical.

In addition, Quality 4.0 offers a plethora of options for automating compliance. Today’s technology suppliers offer highly flexible, automated, and networked QMS systems, as well as validation automation tools. The concept of Quality 4.0 is strongly related to the way in which businesses would be able to predict outcomes, adapt to novelties, and embrace efficiency by increasing quality to excellence.

Strategies

In order for a business to harvest the fruits of Quality 4.0, business owners should build strategies accordingly.

A Quality 4.0 plan is a great way to re-align quality with corporate goals. Many manufacturers have Big Data and Industry 4.0 strategic objectives, which frequently include quality enhancement use cases. Only 16% of the present market sees a clear and compelling link between quality and corporate strategy. This creates a great chance to both influence perceptions and redirect quality.
When bad data automates procedures, one is likely to make a lot of bad decisions. Business owners should emphasize strategies connected to solid traditional quality foundations by setting clear goals.
The Quality 4.0 approach enables businesses to address long-standing challenges, such as weak quality culture, a lack of data-driven choices, and insufficient cross-functional insights.

The future of Quality 4.0 in Manufacturing

The Fourth Industrial Revolution is taking place all around us. It expands the third revolution’s digital effect and integrates it with the physical and natural worlds. It creates new connections between people, machines, and data, democratizes technology previously only available to specialists. Thus, these technologies are critical for quality since they allow for changes in culture, leadership, collaboration, compliance, and strategic development.

We at Cosmos Thrace would love to assist you with innovation in your manufacturing business. Book a meeting, if you want to learn more. You can also check out our portfolio of services.

Resource

Industry 4.0: What Would the Next Industrial Revolution Look Like?

Category

Industry Solutions

Tags

Manufacturing, Industry 4.0

Last Updated

16 Dec 2022

Summary

Due to the digitization of production, we are on the edge of a dramatic change in the way we manufacture products. This transformation is so

Due to the digitization of production, we are on the edge of a dramatic change in the way we manufacture products. This transformation is so compelling that it has been named “Industry 4.0” to signify the fourth manufacturing revolution. The first industrial revolution identified by water and steam power was just the beginning of a long ongoing process. The second revolution followed with mass production and assembly lines using electricity. After decades of development, the fourth industrial revolution is just around the corner. Industry 4.0 will build on what was started in the third revolution with the adoption of computers and automation by adding smart and autonomous systems powered by data and machine learning.

What is Industry 4.0?

Industry 4.0 is the digital transformation of the value chain and manufacturing processes. The core of Industry 4.0 are cyber-physical systems, such as “smart machines.” They have implemented software systems and modern control technologies that keep them connected to Internet addresses. By doing this, production processes get networked and can communicate to optimize work in real-time, as well as enable new ways of production. Such smart machines are the fuel of smart factories as they boost capabilities from the Industrial Internet of Things – remote monitoring, and track and trace.

Main Characteristics of Industry 4.0

To better understand the notion of the fourth revolution, there are some specifics that make it different from the previous three revolutions:

Automation, which is developed to a next level in contrast with the third revolution;
Data exchange in control systems – the cyber-physical systems, enabled by Industrial IoT;
One smart system where smart products define the production processes and steps rather than a central industrial control system;
Personalized products.

Fundamentally, the technologies that enable it make use of existing data and new data sources, creating a smooth flow of information across the value chain. This corresponds to data from connected assets which results in efficiencies on multiple levels and transforming existing manufacturing processes.

Application of Industry 4.0

The use of smart machines can improve businesses in several different cases:

Internet of Things and the Cloud

As it was mentioned above, IoT is an essential characteristic of industry 4.0. It aids internal operations by storing data in a cloud. Thus, equipment and operations could be optimized by utilizing the knowledge of others who use the same equipment. As a result, even small enterprises can access technologies they would not be able to on their own.

Opportunities Identification

Since data is collected from sensors of connected machines, it could warn maintenance and performance teams in advance of possible errors. You can learn about the concept of Predictive Maintenance in our article. Usually, this information could be acquired by a human; the technology analyzes and identifies patterns that guarantee enough time for reaction. Industry 4.0 helps manufacturers increase efficiency by showing them what needs attention.

Optimization of Logistics and Supply Chain

When new information is supplied, a connected supply chain may modify and accommodate accordingly. For example, if a shipment is delayed due to weather, a networked system can anticipate the situation and adapt manufacturing priorities.

The Future of Industry 4.0

Although there are plenty of benefits that the next industrial revolution carries with it, the world is still on the entering stage of adapting its novelties. Many businesses around the world have already acknowledged the potential of smart machines but there is still room for improvement. Industry 4.0 is the digital transformation of manufacturing. However, the concept requires some more experience and time to become fully acceptable by business owners

Do you want to revolutionalize your manufacturing business? Book a free meeting and let us help you achieve that goal!

Resource

How AI and ML can unleash the potential of Manufacturing

Category

Industry Solutions

Tags

Manufacturing, MLOps, Industry 4.0

Last Updated

12 Dec 2022

Summary

In more recent years Artificial Intelligence (AI) and Machine Learning (ML) have been on a conquest to change the face of numerous industries, and the

In more recent years Artificial Intelligence (AI) and Machine Learning (ML) have been on a conquest to change the face of numerous industries, and the Manufacturing industry is no exception.As this sector is the backbone of any economy, it is easy to see why the changes brought on are important to comprehend anyone and why 85% of executives believe AI could give their companies a strong advantage. How AI and ML can unleash manufacturing?

The benefits

Let us start with outlining the benefits that AI and ML bring to the table

Improvements in workplace safety;
Easier Machine Maintenance;
Greater Cybersecurity;
Increased Productivity;
Easier product Development;
Robotics Development;
Better Decision Making.

Let’s dive deeper:

Safety:

How do AI and ML affect safety? Employee entry can be easily monitored and controlled without the help of other staff. Additionally, staff that is already present can also be monitored thus ensuring the safety of the premises. In the current COVID-19 pandemic, this proves to be especially helpful time and time again. Effective tracing of the spreading of the virus inside of the premises of the business can be seamlessly executed and more spreading can be prevented. AI can also identify potential risk areas, or, in the aftermath of an incident, a root cause analysis can be provided.

Machine Maintenance:

When it comes to Machine Maintenance, proper integration of AI and ML can enable predictive maintenance. McKinsey finds that predictive maintenance can generate a value of between 0.5 billion to 0.7 billion dollars across the globe. Based on information from data and sensors, equipment failures can be accurately predicted, thus saving the company time and money in the long run by reducing downtime, in fact, companies save close to 30% of costs this way.

A major firm in the Manufacturing sector reported on a profit improvement of around 300 million euro by using ML to forecast 10 years of repairs needed for their fleet of over 17,000 aircrafts.

Cybersecurity:

Companies that integrate AI and ML can easily recognize cybersecurity improvements. Moreover, in today’s day and age manufacturing companies produce vast amounts of data, which raises the need for better security. In data-rich environments, with cybersecurity being one of the lowest budget areas of any business, AI can scan through a vast number of records, identifying gaps and loopholes that are vulnerable, enabling easy mending of the issues.

Productivity:

Detailed analysis of patterns in various data can enhance productivity – from performance data to sensor data, and anything else a business is tracking. For example, areas where resources are wasted, and productivity can be enhanced are clearly flagged.

Development:

Developing products using painstaking amounts of time and resources is made redundant by AI and ML. This allows to create a perfect digital copy of a concept, and a business can test any and all features and possibilities virtually. This not only saves time but speeds up development by an astounding amount. It also enables testing in environments never accessible before, allowing for potentially discovering new features of a product. General Electric (NYSE: GE) gained a 12% improvement in the efficiency of the airplanes they produced by taking advantage of an algorithm that designed flight planning.

Robotics

Robotics as a field has aided manufacturing since its conception. The robots are capable of working in strenuous conditions, 24 hours a day, 7 days a week, with the only downtime being when the robot breaks down. The predictive maintenance mentioned above can help with that too.

Conclusion

In a nutshell, experts predict that AI and ML will play major roles in the future of manufacturing. Additionally, Their integration is a major hurdle for many businesses. We at Cosmos Thrace specialize in adapting and implementing these technologies to our client’s needs. Learn more about how AI and ML can unleash manufacturing or about our other services here.

Resource

Building a better future: AI and robots in the construction industry

Category

Industry Solutions

Tags

Manufacturing, MLOps, Industry 4.0

Last Updated

12 Dec 2022

Summary

Despite its key role in creating and adapting the spaces in which we live and work, the construction industry has continued to rely primarily on

Despite its key role in creating and adapting the spaces in which we live and work, the construction industry has continued to rely primarily on manual labour, with little to no automation. While there have been many important advances in robotics and AI over the last decade, construction has lagged behind other sectors in introducing automation on a large scale. Therefore, let’s take a look at the role of AI and robots in the construction industry.

AI and robots in the construction industry

According to a survey commissioned by robotics firm, ABB, 91% of construction businesses in Europe are predicting a skills crisis by 2030. The shift will inevitably boost interest in this area and drive the application of robots and other automated solutions.

However, there are obstacles hindering the use and adoption of AI and robotics in the construction industry. One of them is the sheer unpredictability of construction sites. While robots excel at repetitive tasks, a construction site requires adaptability and flexibility. It is more technically feasible to automate predictable physical activities than unpredictable ones.

Construction robots and their applications

Below are some examples of construction robots and the range of activities they can undertake on-site:

Housebuilding accelerated

Hadrian X is an automated bricklaying machine capable of building the walls of a house by calculating the required materials and movements without any supervision. Developed by an Australian firm by incorporating an intelligent control system, the robot can lay bricks at a speed of 1,000 per hour with 100% accuracy. It can also detect changes caused by external factors such as wind and vibrations and adapt the design accordingly. It has been estimated that the robot takes two days to complete work that, when done manually, could otherwise take around four to six weeks!

Making construction safer

Another important way in which the application of AI and robotics can enhance the construction industry is in accident prevention. Sadly, construction continues to be one of the most dangerous industries for workers. In 2018, it presented the highest number of fatal worksite injuries in the USA – 20% of all workplace fatalities.

Enhanced design and engineering

While most research focuses on the actual delivery of projects in the construction sector, there are many applications of AI within the other phases of project development, such as architectural design and structural engineering. These areas are less dependent on physical work and are relatively easier to empower through AI solutions.

Constructing a better future together

While we’re currently at tipping point, we believe that AI will inevitably take over construction by becoming more integrated into the software and equipment used by the industry and its related disciplines.

However, as the development of computers didn’t remove the need for accounting firms, AI will enhance rather than replace the construction industry. It will achieve this by making construction safer and more effective and introducing greater co-operation between people and machines.

At Cosmos Thrace, we enable businesses from many sectors to implement AI Solutions and optimize their business operations. Through our expertise, we adapt those technologies to each customer’s specific needs, requirements, and priorities in an efficient and timely manner.

Learn how to enable AI in your business.

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Bulgaria took the first step toward becoming the leader in Eastern Europe in Artificial Intelligence and Computer Science

Category

Company News

Tags

MLOps

Last Updated

12 Dec 2022

Summary

The technology giants Google, DeepMind, and Amazon Web Services are entering with serious investments of 7 million dollars in total in the first of its

The technology giants Google, DeepMind, and Amazon Web Services are entering with serious investments of 7 million dollars in total in the first of its kind Bulgarian Institute of Computer Science and Artificial Intelligence INSAIT. The institute was officially opened on April 11, 2022, at a special event at the National Palace of Culture.

Not only that but the institute was established and will work in partnership with the two largest technological universities in Switzerland – the Federal Institute of Technology in Zurich (ETH Zurich) and the Polytechnic University of Lausanne (EPFL) and will be led by world-renowned scientists – Bulgarians and foreigners of the best universities in the United States and Israel.

The goals of INSAIT are for Bulgaria to become the best place in the world in the field of computer science and artificial intelligence. The aim is Bulgaria to attract the largest companies in the world and open their research centers in the country, according to the Prime Minister of Bulgaria, Kiril Petkov. In addition to the first foreign investments announced so far by the American technology giants, the state and Bulgarian businesses are involved in the long-term financing of the institute in unprecedented cooperation. Bulgaria will invest 10 million in INSAIT in each of the coming years and Bulgarian technology companies, led by SiteGround, are also involved with a $ 6m investment.

INSAIT has the ambition and potential to establish itself as one of the world’s leading research and education centers, providing for the first time working conditions comparable to those in Western universities. The institute will develop its activity under the auspices of Sofia University “St. Kliment Ohridski.” Behind the creation of this center is the great idea of promoting economic growth through education. INSAIT has the potential not only to attract world-class scientists to Bulgaria but also to keep its valuable young minds in the country.

The realization of the first high-tech research center of its kind in Bulgaria was a dream of more than a decade ago by a graduate student at that time in Cambridge. Subsequently, he became one of the most successful and award-winning Bulgarian scientists in the field of computer science and artificial intelligence, and at the opening, he was presented as the ideological architect of INSAIT – Prof. Martin Vechev.

“When I started my postgraduate studies in Cambridge years ago and applied for a scholarship, there was a question on the form: ‘What do you want to achieve in 20-30 years?,’ said Prof. Vechev. “I wrote that I dream of creating a world-class research center like Cambridge. This dream is already a fact and when you leave this opening ceremony and you are asked what happened at this event, say that for the first time in Bulgaria there is a place that allows anyone who wants to change the world to do so from here.”

The opening of the Institute for Computer Science, Artificial Intelligence and Technology (INSAIT) in Sofia is a memorable event. INSAIT has the potential to become the factory for “unicorns” not only in Bulgaria but throughout Eastern Europe, as added by the EU Commissioner Maria Gabriel who shared the desire to make the institute a platform for future Nobel laureates.

“There are events that change the course of human history and today we have the privilege of being part of such an event. Behind them are always people with knowledge, vision, and energy,” said President Rumen Radev. “The establishment of this institute managed to achieve the impossible for the Bulgarian political reality – to unite the efforts of four successive governments.”

INSAIT’s mission is to turn Bulgaria into a global destination for research and innovation, as well as a center of a new ecosystem with a direct impact on the country’s development. This is a turning point in efforts to create a high-tech and competitive economy based on high value-added products and intellectual property and to overcome negative trends such as depopulation and the so-called brain drain.

Eastern Europe has an incredible array of talents in computer science and engineering. Jeff Dean, Google AI’s director, shared their desire to help INSAIT become a world-class hub that attracts the best researchers from the region and the world. Google will invest $ 3 million in computing resources, and will also establish special scholarships for women to develop high expertise in computer science.

Resource

Evaluating Risk Through AI Models in Order to Detect Financial Fraud

Category

Industry Solutions

Tags

Financial Services, Retail, MLOps

Last Updated

12 Dec 2022

Summary

Fraud detection is a tough challenge. Fraudulent transactions are extremely infrequent; they account for a small percentage of the total activity within a company.

Fraud detection is a tough challenge. Fraudulent transactions are extremely infrequent; they account for a small percentage of the total activity within a company. The problem is that without the correct tools and systems in place, a small number of activities can soon develop into severe cash losses. However, AI can be used in financial fraud situation in order to detect it.

The emergence of new digital payment channels and technology has created a breeding environment for sophisticated fraud opportunities and bad actors, creating new fraud detection, prevention, and prediction issues.

Unfortunately, the fight against financial fraud is an ongoing challenging process.

The reason is the combination of three factors. Firstly, we have numerous transactions in the data structures. The increasing volume of digital transactions and payments puts strain on fraud detection and prevention systems, which must process, analyze, and authorize a greater volume and diversity of payment traffic.

Secondly, the sophistication of “bad actors” is increasing which strongly threatens the business world. Criminals are using sophisticated tools and cutting-edge cyber strategies to bypass older security systems in the ongoing arms race between fraudsters and financial institutions.

Lastly, the identification of verification is difficult. Identity theft is a serious form of cybercrime that affects both individuals and businesses. For instance, real name theft, synthetic theft, and account takeover are the most prevalent types of identity theft. In those cases stolen information is used to open fraudulent new accounts, such as bank checking accounts, credit card accounts, or mobile phone accounts.

However, the widespread digitalization did not come as an unreversible fraud-feeder environment. The biggest combat against fraud is the 21st century’s game-changer – Artificial Intelligence.

In fact, businesses have benefited from using AI to detect fraud by enhancing internal security and simplifying corporate procedures. As a result of its enhanced efficiency, AI has emerged as a crucial instrument for preventing financial crimes.

For example, Artificial Intelligence (AI) may be used to analyze large amounts of data in order to find fraud patterns, which can then be used to detect fraud in real-time. Thus, when fraud is suspected, AI models can be used to either reject or flag transactions for additional investigation. Furthermore, it can grade the likelihood of fraud, allowing investigators to focus their efforts on the most promising cases.

To be more specific, supervised Machine Learning uses alert reduction algorithms to drastically reduce false positive alerts and alert storms. This approach allows teams to concentrate their efforts on the most important warnings. Users also notice a decrease in the number of legitimate transactions that are incorrectly refused. On the other hand, even when criminal actors adopt new approaches and schemes, AI systems efficiently capture false negatives and minimize the number of fraud cases.

As a result of the use of AI, fraud prevention expenditures are being reduced. Financial organizations can benefit from risk scoring and fraud protection systems in a variety of ways. While the major benefit is lower fraud-related monetary damages, these technologies also benefit the bottom line by lowering expenses. Those costs cane be for manual fraud investigations, enhancing customer happiness, and lowering customer attrition.

Takeaways

Combining supervised and unsupervised machine learning as part of a bigger Artificial Intelligence (AI) fraud detection strategy can help digital businesses discover automated and more complex fraud attempts faster and more accurately. The digital age is, undoubtedly, an open space for innovations, but businesses should not underestimate the risks and challenges. In a nutshell, companies should extract digitalization’s benefits learn how to control it to their advantage.

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From a Student to a Key Member of the Team of Cosmos Thrace

Category

Company News

Tags

Our Stories

Last Updated

08 Dec 2022

Summary

Dani Matev is a fourth-year student at the Technical University in Sofia, majoring in Computer and Software Engineering. Although he was born and raised in

Dani Matev is a fourth-year student at the Technical University in Sofia, majoring in Computer and Software Engineering. Although he was born and raised in Varna, the sea capital of Bulgaria, his passion for data analysis and Artificial Intelligence inspired him to move to Sofia where is currently pursuing a data analytics career.

The Beginning of a Collaboration

Dani has been at Cosmos Thrace for a year now and the knowledge he gained from his first-hand experience at the company has strengthened his interest in process automatization and computer science. He is currently working as a data scientist and AI specialist at Cosmos Thrace in order to pursue a data analytics career.

He recalls how a close friend of his, who also worked at Cosmos Thrace, introduced him to the culture of the company and its vision. His interview with the CEO Viktor Elefterov created an instant rapport as they both found a match in their capabilities and beliefs.

Where does your Inspiration Come From?

Dani has recently started working on two data science and AI projects. One of them is the preventative measure for machinery breakdowns. The project focuses on building a system of predictive maintenance that predicts the time at which a certain machine might fail in order to prevent this from happening.

Another interesting project Dani is involved in is a bank transaction classification system where machine learning assigns a category to different bank transactions, for example, food, loan payments, etc. The projects have been engaging so far, helping Dani to apply his theoretical knowledge in practice.

Opportunity for Growth

Dani highly appreciates the opportunity for growth and autonomy provided at Cosmos Thrace.

“There is no judgmental culture, so if you want to improve, you can. If you want to improve a certain process, even if there is a risk of failure you will be given the chance to grow. This way you can improve both yourself and the company,” shares Dani.

Academic, Professional and Personal Life

Being a student at the Technical University in Sofia and pursuing a data analytics career takes up a lot of Dani’s time. Currently, he is spending a lot of time improving himself as a person. He likes reading self-help books about emotional intelligence and learning more about how the human brain works. In other words – learn how your inside world operates to master the art of building an operational artificial one.

He has also been spotted scribbling on the big whiteboards in the office, where you can find drawings from his favorite movies and illustrations of many internal jokes between the teammates at Cosmos Thrace. Dani believes this is another way in which the company encourages freedom of expression and development.

What Does the Future Hold?

Dani is striving to further advance his tech team leadership and management skills.

“I want to improve my management skills and I believe Cosmos Thrace is giving really good opportunities to people like me. I strongly support this as a part of the culture of Cosmos Thrace – to encourage innovation and to allow proactive people,” claims Dani.

He has already managed a team project and has been a scrum master of another. Even though he admits he does not have a lot of experience in that area, the company has given him the chance to find what he enjoys doing, namely being a project manager.

Check out our open positions to learn more!

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What role does Data Migration play in the Digital Transformation journey?

Category

Technical Guides

Tags

Data Migration, Databricks

Last Updated

07 Dec 2022

Summary

One of the most challenging and complex aspects of any digital transformation is data migration. In the last decade, the importance of data in business

One of the most challenging and complex aspects of any digital transformation is data migration. In the last decade, the importance of data in business has shifted considerably. It has evolved from a departmental role to a critical facilitator of digital transformation. The core of modern enterprises of all sizes is data centricity. Building a genuinely data-driven business require large-scale data transfer and modernization. However, this data transformation process entails much more than simply moving the data estate to the cloud.

Therefore, data migration should move up on the companies’ lists of project priorities.

However, most businesses overlook the need for data migration work as a prerequisite for a successful ERP implementation/digital transformation. Many businesses are unaware of the numerous difficulties or stages that are part of the digital transformation journey. It is an iterative process to migrate data. Some people wrongly believe that data can be transferred using (auto-magical) “lift and shift” technologies, or that someone else on the team will design and conduct the heavy lifting.

Data is frequently added too late in the project cycle when data readiness is not a well-planned pillar of the project. This results in the early discovery of system problems. Project delays and costly design/build/test rework cycles can cause late modifications to the new system. A possible impact is “dirty data.” When you fail to plan and implement data adequately in advance usually “trash in, garbage out” happens. Business intelligence, data analytics, the internet of things (IoT), and other large-scale IT efforts have potential. However, if a company has corrupted, inconsistent, or wrong data, these technological tools are useless.

Here comes another term in data transformation, namely “data cleansing.”

Data cleaning necessitates more extensive pre-work or may require external validation. Therefore, it is usually done before the actual data migration. Usually, skipping data cleansing phase is uncommon, even though it should start before software selection.

Data transformation is a systematic, programmed method for transforming large amounts of data according to established business and technical standards. The project team will be able to incorporate significant volumes of changed live data into the newly developed system for effective/realistic testing. Moreover, transformation is meant to be done iteratively and frequently. This entails not only comprehending the technical elements but also obtaining the necessary authorization for the mapping as to who has access to what data.

Even when digital transformation is now an essence in the business world, some companies still put off confirming their data until user acceptability testing.

The main reason here is the lack of a “data readiness track” to ensure that any meaningful data is available for testing. A significant drawback of data testing late in the journey is that is unlikely to complete it before going live. In addition, downstream systems, reports, and analytics will not be executed or tested with master and transactional data.

Since the system integrator is usually primarily responsible for the main ERP system, it cannot guarantee that downstream systems and reporting can correctly process the outputs. This can lead to an unpleasant surprise when an executive realises the poor state of data in their new dashboards. Data readiness preparation might help companies avoid unpleasant surprises after project’s completion.

And finally, what happens with the privacy and security concerns around data migration?

Data migration, data privacy, and data security all need data mapping and cataloging efforts. A company must first understand what data it has and how it travels and is utilized within the business to safeguard it. If teams adopt data privacy and security in time, the data transfer team can benefit from it. When a company is considering a data privacy or security program (highly recommended for any size company), they should look for ways to combine part of their work with data migration efforts. Furthermore, a migration team can work with (and understand) the relational benefits of doing so – a company can save a lot of time, resources, and expenses.

Do you need data migration service in order to complete your digital transformation journey? Do not hesitate to book a free call or look at our full portfolio of services!

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AI and E-commerce: Forecasting Sales for Better Planning

Category

Industry Solutions

Tags

Retail, Ecommerce, MLOps, Analytics

Last Updated

07 Dec 2022

Summary

One of the most important variables in predicting long-term business growth is sales forecasting. It largely occupies managers’ time and attention. Unfortunately, sales forecasting has

One of the most important variables in predicting long-term business growth is sales forecasting. It largely occupies managers’ time and attention. Unfortunately, sales forecasting has proven as an imprecise art, despite the enormous time spent on researching and analyzing different data. Here comes the role of AI in E-commerce and its numerous possibilities. Read along to learn how to forecast sales through Artificial Intelligence technology.

How is sales forecasting currently done?

While conventional sales forecasting methods range from in-depth historical investigations to gut instincts, weighted pipeline forecasting is the most widely utilized method in today’s business world. In it, every sales opportunity in the pipeline gets a percentage likelihood of closing a deal. Then, it is multiplied by the revenue value associated with the opportunity. The sales forecast is estimated by the sum of all revenue values.

So, how can sales forecasting be enhanced for better planning?

The way organizations are using artificial intelligence (AI), machine learning (ML), and predictive analytics, revolutionizes how the world perceives predicting sales. Only a quarter of businesses, according to Salesforce, use predictive analytics. Moreover, of those who use it, 86 percent of individuals, have already noticed a beneficial result. Predictive analytics may be integrated into the CRM to help sales representatives, partners, and managers produce more accurate sales projections. Through that approach, they can gain actionable insights.

How does AI in E-commerce contribute to the welfare of businesses?

Business is currently driven by huge amounts of data. Companies must have the right skills and tools to translate data into actionable insights to use it effectively. This is the foundation of a data-driven experience or in other words “intelligent experience” where system users are supplied with actionable information in the context of their workflow. A corporation utilizing a typical CRM, for example, can view pipeline and opportunity data as well as expected sales figures.

In contrast, a corporation that uses intelligent forecasting (AI) gets all of the same data with some additional assumptions and suggestions in a usable format. Firstly, the intelligent forecast predictions will look at prior opportunities, hits, misses, win rates, and other factors. Then they will generate a recommended forecast that can be compared to field inputs. The most important differentiation when it comes to intelligent forecasting is the capacity to improve accuracy and confidence, as well as provide insights and actions to boost sales volumes.

How to approach intelligence experience in forecasting?

A multi-tier channel sales organization, for example, will have different requirements than a direct sales organization. To estimate each income channel, multiple data sets and algorithms will be necessary. Propensity-based predictions for direct sales and run rate predictions for both channel sales and aggregate forecast rollups are the two most frequent prediction methodologies. Run rate models look at aggregate sales volumes across segments of the business, whereas propensity-based models look at individual opportunities and score them (i.e., channel, geography, product, etc.). The insights are pooled across both techniques and integrated into user processes within the CRM once they’ve been applied.

Intelligent forecasting in e-commerce.

On the one hand, when used in areas like fraud detection, picture categorization, price optimization, and consumer segmentation, it can improve corporate efficiency. However, it has a lot of potential for improving the user experience on these platforms by making algorithm-driven product suggestions and providing content personalization, virtual assistants, and better search capabilities. All of this will help businesses stand out from the crowd, allowing them to better position themselves against the competition.

Customers are beginning to expect a personalized experience while transacting; therefore, understanding each consumer individually enables this. Companies accomplish this through machine learning-driven technologies that enable real-time content updates depending on user behaviour. Those algorithms predict the shopper’s next move in the online store and expedite the buying process, putting the merchant ahead of the game. Furthermore, chatbots or virtual assistants not only increase consumer interactions but may also give support and create promotions depending on user preferences.

Intelligent forecasting is now more accessible than ever before. Companies are merging data, analytics, and artificial intelligence (AI) to increase sales predictability and performance in e-commerce. Making the most out of it benefits companies to get a competitive advantage and utilize their connection with their customers.

Contact us to improve your E-commerce business and gain a competitive advantage! You can also check our full portfolio of services!

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Digital Twins: What to expect and look out for?

Category

Industry Solutions

Tags

Manufacturing, Industry 4.0

Last Updated

07 Dec 2022

Summary

A virtual representation, simulation, or mapping of a real entity, such as a process, person, item, system, or any other abstraction, is known as a

A virtual representation, simulation, or mapping of a real entity, such as a process, person, item, system, or any other abstraction, is known as a Digital Twin. While the concept is not new, IoT proliferation, hyper-automation, and real-time data flow are increasing digital twin technology’s significance. Digital twins are a new type of business software that leverages the Internet of Things (IoT) to track, analyze, and manage business operations.

There are several varieties of digital twins that are ideal for the jobs that must be completed during the manufacturing process.

With rudimentary filtering, a status twin can provide real-time monitoring and visualization possibilities. The users are left to interpret the outcomes in this case. Basic analytics can be used by an operational twin to conditionally monitor a process. It can make suggestions and do automatic actions. Organizations can use predictive twins to anticipate the quality of output or even an equipment breakdown. Thus, this so-called sort of twin can help you optimize your maintenance cycles and strike a balance between corrective and preventative maintenance. A simulation twin may simulate several scenarios and preview various sorts of outcomes by replicating a system’s various operations.

Undoubtedly, digital twins are here for good. However, what has the business world already exploited from them?

Digital twins provide a real-time view of what’s going on with physical assets, which can significantly reduce maintenance costs. Companies utilize digital twin technology for a variety of reasons, including improving existing operations, training staff, and testing new goods or procedures before releasing them into the real world, where fixing any flaws becomes more expensive and harder.

Firstly, a striking benefit of digital twins is the possibility of a test before manufacturing. Before investing in the construction or deployment of systems, equipment concepts, or service models, companies can employ digital twins to design and test them. If a model proves to be successful, it may be connected to the physical construct for real-time monitoring.

Moreover, using digital twins to monitor everyday operations and simplify manufacturing lowers excessive wear and tear on machinery while also alerting business managers to possible cost-cutting opportunities. Improved total productivity and efficiency help organizations to maintain a competitive edge through faster maintenance and repair.

Lastly, customer-facing applications, such as remote troubleshooting, are also available with digital twins. Instead of depending on default procedures, personnel may do diagnostic testing from anywhere and take consumers through the appropriate processes for repair by using virtual models. The data acquired during these sessions is extremely useful for future product planning and development.

Despite the advantages, there are also drawbacks to using digital twins.

For everyone engaged, the technology provides a new range of applications. As a result, there are no industry-ready products available for deployment off the shelf. However, each sector needs customized digital twin solutions that are tailored to their specific demands. As a result, a slew of digital twin companies is offering packaged offers alongside their headline goods and services to meet those demands.

Here comes the infrastructural problem. The infrastructure required to deploy the digital twin concept is not available to all businesses. Many firms have yet to be exposed to the technology because it is still relatively new. Real-time data may be used to uncover the true value of digital twins. This means that this technology is largely reliant on Internet of Things (IoT) devices and a system of networked equipment, objects, or people with unique IDs and a continuous data flow between them. It is difficult to apply this technology in firms that have not developed this infrastructure.

Therefore, the faster a new form of technology spreads, the less attention it receives in terms of security. The gathering and use of huge data volumes come from a variety of endpoints. Any of them may be a source of failure. Every time a new connection is established, and more data is sent between devices and the cloud, the danger of a security breach grows. As a result, companies contemplating digital twin technology should always be cautious. Furthermore, they should not proceed without first analyzing and upgrading their present security policies.

Conclusion

The implementation of digital twins usually is a crossroad of data science and business process management. Before commencing on this digital twin adoption journey, an organization should analyze its data maturity and process measurement capabilities. A business should assess its needs to adopt the proper type of digital twin and harvest the benefits of its implementation. Nevertheless, Digital twin deployment might cost a lot of money depending on how complicated the project is. Furthermore, adopting digital twins technology on one process usually impacts other processes, meaning that it needs extensive planning and collaboration. Therefore, companies should look out for the challenges that come along this automation journey.

Resource

AI-based Predictive Maintenance

Category

Industry Solutions

Tags

Manufacturing, MLOps, Industry 4.0

Last Updated

07 Dec 2022

Summary

Artificial Intelligence offers a wide range of applications in business, from workflow management to trend forecasting. However, when it comes to the manufacturing industry, there

Artificial Intelligence offers a wide range of applications in business, from workflow management to trend forecasting. However, when it comes to the manufacturing industry, there is one concept that stands out – Predictive Maintenance. Through it businesses are able to use their data not only for reporting the past but also to predict the future.

What is Predictive Maintenance?

Predictive maintenance is a term used in AI and machine learning to describe the capacity to use large amounts of data to forecast and point out possible issues. Sensors detect errors before they lead to failures in operation processes or systems. Having robust predictive maintenance technologies in place, allows organizations to foresee when and where possible service breakdowns may occur. Hence, they can react accordingly and quickly to avoid service interruptions.

What are the benefits of adopting Predictive Maintenance?

The implementation of Predictive Maintenance can be done in accordance with the company’s needs and preferences. They can vary from complex data gathering and model building to simple computations. Either way, predictive maintenance tools are used by companies to maintain critical assets to work efficiently and effectively. Therefore, the following goals are reached:

Revenues increase as productivity increases and costs for new materials decrease.
Operational expenses decrease and the risk of unplanned failures reduces.
Equipment life cycle is extended and improved.
Repetitive rework is minimized, possible defects decrease, and quality increases.
Safety and energy efficiency increase.
Keeping supply chains intact.

How does it work?

The process begins with the analysis of the business case – what the pain point in terms of costs is and how AI would improve it. Then, Machine Learning (ML) is infrastructured in data research and data collection. The following stage is to train the ML to recognize areas of errors and how to address them. The desired outcomes are for predictive maintenance to provide coherent and accurate information.

To illustrate the whole process by breaking it into seven more detailed stages, the timetable would be as follows:

Understand the need – consider the costs, wastes, and inefficiency; then, identify the optimal case for the business.
Extract data – allow the technology to have access to the data collectors and storage.
Explore, analyze and delete data – use only relevant and understandable data.
Enrich data – manipulate the data so that the sensors could address it as one unit rather than separated parts.
Predict – use all the collected data and sources to create a predictive model.
Get visualized – the feedback is received as a product from the model which helps managers to react accordingly.
Execute – start using the model in real-time cases.

Conclusion

The most immediate benefits from implementing Predictive Maintenance are both cost reduction and an increase in quality. By adopting a more sophisticated and larger Predictive Maintenance strategy, however, the lifetime of high-valued assets could be expanded to provide additional income rather than simply being a cost.

The AI tools are no longer a thing of the future – they are the present and more and more businesses are finding their usage favorable for their success.

Resource

Boosting Sales and Customer Experience with AI

Category

Industry Solutions

Tags

Retail, Ecommerce, MLOps

Last Updated

07 Dec 2022

Summary

It has been for a while now that AI is being used by businesses to improve human interactions. By entering the new era of technology,

It has been for a while now that AI is being used by businesses to improve human interactions. By entering the new era of technology, companies aim to increase customer satisfaction and respond to customer care concerns more quickly – all without the need for a real customer service worker. Moreover, sales management teams and salespeople claim AI as a new approach for boosting sales. Therefore, they believe AI helps to improve bottom-line outcomes and handle live client inquiries by simply reducing error and time.

Is AI a promising solution for boosting sales

Keep in mind that holding clients away from salespeople is not always optimal. For example, consider the customer-sales representative relation or add-on sales. Customers who call and get their questions answered by AI chatbots may become frustrated or eventually be unsatisfied if the AI bot is not properly designed or educated. Thus, when companies apply AI solutions, they must acknowledge their preferences precisely and construct chatbots that follow them.

Nevertheless, to be clear, it is not all negative – several AI tools are promising an advantageous future development.

Companies have learned that artificial intelligence (AI) has real-world applications in sales and, when applied effectively, may boost profits. By automating tedious chores like the input of CRM data and scheduling business meetings, AI can force-multiply sales representatives. It assists salespeople in prioritizing tasks so that they may concentrate on the high-probabilities, high-payoff prospects, and, namely, clients that are most important to them. AI can also give insights on sales representative-to-potential-client communications, such as emails, phone calls, and chats. In addition, AI optimizes sales forecasting, lead qualifying, routing/matching, and consulting sales/customer care representatives.

Not only are sales and revenues boosted, but AI also enhances contact centers’ efficiency.

Predictive call routing improves customer experience by analyzing prior customer behavior and preferences. By deploying this AI solution, sales representatives and customer service agents can forecast the abilities and personality qualities required to give the best possible service. In this case, AI reduces escalation time and improves the overall satisfaction of both the caller and the agent.

Furthermore, there is the issue of contact centers facing the difficulty of training large numbers of agents. Agent growth and coaching are major priorities, and AI may help by assessing agent data and caller replies to offer customized on-demand learning and promote advanced performance. AI-enabled performance intelligence can allow managers to spot concerns with agent performance sooner to prevent any further disruptions.

Finally, AI can enable adopters to anticipate events and trends.

AI-enabled predictive forecasting of trends, moods and important events is possible because of the vast amounts of public data. This provides a significant number of new options for anticipating and addressing challenges in multiple marketplaces, as well as proactively minimizing hostile digital risks to companies, brands, or customers. This is an important but often overlooked aspect of customer experience and sales performance.

A key takeaway is that, AI will become increasingly valuable to customer experience (CX), customer satisfaction (CS), and sales teams. Considering the source data which AI consumes and how it becomes richer, the departmental data silos that become more linked, and corporate resources that become more publicly shared, AI is no way to be eliminated from the business world – quite the opposite.

Resource

How can AI help in demand forecasting in Supply Chain

Category

Industry Solutions

Tags

Logistics, Supply Chain, MLOps

Last Updated

30 Aug 2022

Summary

What is demand forecasting and why is it important? Demand forecasting is an essential component of the supply chain. It is the process that allows

What is demand forecasting and why is it important?

Demand forecasting is an essential component of the supply chain. It is the process that allows companies accurately to plan and predict the anticipated demand for products and shipments. Demand forecasting methods help organizations avoid inefficiencies, caused by a mismatch in demand and supply and prevent having an excessive amount of products in or out of stock/overstocking. It allows businesses to analyze the difference between forecast sales and actual sales, however, it is one of the most difficult business processes. So, what is the connection between AI and demand forecasting?

The problem with traditional forecasting methods

One of the most common problems in traditional (manual) forecasting is demand volatility. Those methods generally use past demand as an indicator of future demand.

As the industry is growing fast and becoming more complex, companies are trying to adopt other, much more efficient methods to improve demand forecasting, such as automation, which will radically change the workflow process and who it is done by.

How AI improves demand forecasting

The implementation of an AI solution depends on many variables, such as the quality and availability of data, the complexity of the processes, and the expectations from the forecasting solution. AI is more reliable for forecasting demand because it takes into measure both internal and external variables that impact sales. It frees up employees from repetitive tasks while providing higher speed and precision. All of this creates a considerable level of automation in the logistics department.

Benefits of an AI-based solution:

Improves accuracy in demand: According to the Tools group, AI-based forecasting can lower errors between 30% and 50% and increase accuracy up to 20%. The reduction is due to its focus on gathering and storing data constantly. The improvement in accuracy leads to a reduction of up to 65% in lost sales and warehouse inventory costs by up to 40% while increasing Return-on-Investment (ROI) at the same time.
AI provides a real-time solution: AI is managing all types of data in real-time. This is an advantage because AI is making planning production and adjusting volume in unexpected situations easier.
AI is using both real-time and historical data: With the help of AI, businesses can analyze a large amount of data, related to sales history, and customer data, and process other types of data at the same time. AI and ML-based algorithms increase the accuracy of demand forecasting by managing complex relationships in huge data sets that are hard to be processed for an employee.
AI increases productivity and efficiency: As AI is taking care of time-consuming and repetitive tasks, employees can focus on completing more complex tasks that require human intervention. Accurate demand forecasting allows the reduction of inventory costs and data errors.

The future of warehousing:

AI is transforming the logistics industry and it is becoming more popular. Consequently, more and more companies will try to adapt it to their day-to-day operations. It is expected that by 2027, the market for AI in the supply chain will grow up to $12 billion. (all the research) 27% of warehouse companies plan to implement full automation, and 61% consider partial automation by 2024.

If you are looking for a way to maximize warehouse and sales efficiently, contact the professionals at Cosmos Thrace or take a look at our portfolio of services!

Resource

AI Powered Optimization for Distribution Centers

Category

Industry Solutions

Tags

Logistics, Supply Chain, MLOps

Last Updated

30 Aug 2022

Summary

350 Distribution Centers in the US and UK participated in an independent research study. The findings? Most of them use AI in some area of

350 Distribution Centers in the US and UK participated in an independent research study. The findings?

However, no beating around the bush, the key issues noted were cost, complexity, and lack of understanding of how to use AI properly.

Why do distribution centers use AI?

The warehouse automation market has been growing steadily and experts expect it to reach some 30 Billion USD by 2026. The pandemic has helped this growth, as it drives improvements in supply chains, and thus in distribution centers. Growth in eCommerce, expensive and hard-to-find physical labor and increased customer expectations all lead to investments in automation. Good data is also a necessity of AI, and distribution centers are perfect environments for collecting large datasets.

Certainly however, there is a consensus on the fact that AI is the future as they bring numerous benefits to businesses, such as:

AI Optimization for Distribution Centers:

Lower labor costs;
Dynamic Slotting – fully automated and optimized, with lower costs as no in-depth CAD drawing is required;
Higher employee satisfaction;
Production output;
Automatic workflow orchestration;
Safety;
Management effectiveness and better Performance Management;
In-Warehouse Travel Optimization – it is indicated that 30% of labor costs are attributed to time spent navigating a warehouse inefficiently.

A more futuristic implementation of AI in distribution centers and warehouses is wearable technology. Natural language processing enables voice picking. Smart glasses that automatically scan barcodes and other technology that is still in the testing stage will soon be on the scene.

Read our in-depth look into the benefits of AI in warehousing here.

Issues with implementing AI:

While many benefits exist, 99% of organizations say they find it difficult to use AI efficiently. On top of that, 80% state that there is a lack of understanding of how to put AI into practice.

The MHI Annual Industry Report 2020 finds that only 12% of the respondents in the Supply Chain Industry are using AI tech.

The reasons:

Perception of high cost to benefit ratio;
Issues with Risk and control operation decisions;
Time and monetary costs of training;
Lack of understanding for implementation.

Cosmos Thrace is there to help in the implementation, integration, and support of AI in the distribution center. But most importantly, we aim to help inform people about the benefits of the technology. Contact us to find out how we can help on your journey to automation or look at our portfolio of services.

Resource

How can AI solve the talent shortage challenge?

Category

Data Strategy

Tags

MLOps

Last Updated

30 Aug 2022

Summary

In the highly digitalized business world and the high-sky demand for talent, recruiters must rely on the assistance of AI and automation to solve hiring

In the highly digitalized business world and the high-sky demand for talent, recruiters must rely on the assistance of AI and automation to solve hiring issues and fill crucial posts quickly in a competitive labor market. How is that possible? In other words, this article will look at how AI technology can solve the talent shortage challenge.

Employers and HR professionals must reimagine talent experiences to gain a competitive advantage. AI and automation may help companies achieve this aim by providing hyper-personalization that boosts productivity. At the same time, they can also provide a competitive talent data intelligence system. Moreover, HR directors now have an even larger potential to enable employee migration inside their businesses, grow internal sourcing, and minimize time-to-fill with the use of AI technology.

How and where to start on the journey of AI combating talent scarcity?

To fulfill current talent expectations, companies and HR professionals must first build their competitive advantage. They can do this by rethinking talent experiences and considering how a comprehensive approach to the talent experience may help them. In fact, considering how vital the culture of advancement is for a business, HR teams, managers, and leaderships think that this is where they should begin to inspire talent to stay. Motivation, development, and a sense of progress have always been among the top reasons why employees remain in business.

The way automation fits into the development of such culture is through making recruiters comfortable with internal mobility and recruitment. There must be a normality of providing possibilities for internal advancements. Hence, AI can assist employees, executives, and recruiters in advance more naturally and smoothly.

However, we should also consider recruiting talent shortage when speaking of talent scarcity.

The business world has reached the point of the necessity for HR departments to embrace AI and automation. In other words, AI’s purpose is not to take people’s jobs or fill in the gaps when hiring is difficult, but to assist recruiters in managing processes. These are the manual, time-consuming tasks that prevent them from truly adding value to the recruiting and onboarding process. Therefore, the value of AI lies in its ability to relieve recruiting staff of tasks such as scheduling and screening, allowing them to focus on advising hiring managers.

Ethical considerations attached to the concept of automating recruiting processes.

There is a lot of ambiguity about AI and its capacity to eliminate and create prejudice. Thus, there is a lot of interest in learning how HR teams may use AI ethically and avoid bias.

No algorithm is intrinsically ethical or unethical since AI learns on current data sets that are developed and shaped by human activity. Transparency must be at the heart of AI systems for them to function ethically. In that way, nobody, regardless of their expertise in machine learning, can comprehend the algorithm makes a decision. Hence, smart machines should be dealt with accordingly to address malfunctioning scenarios in ethical choices and prevent the misuse of ethical requirements in their performance.

Many businesses can benefit from arming organizations with technology that can automate time-consuming recruiting processes. Not only that but it can also provide individualized experiences and advancement possibilities to the top candidates and internal workers. AI solutions optimize the performance in industries and the talent shortage challenge should not be left alone. The good thing is that both sides (managers and employees) can benefit from the use of automation. Moreover, they can explore even further the possibilities of finding the perfect fit for the company.

Let us allow your data to help you in your hiring process! Book a free call now or take a look at our portfolio of services!

Source

Resource

Is AI a Threat to Human Rights

Category

Data Strategy

Tags

AI Ethics, Data Governance

Last Updated

30 Aug 2022

Summary

The possibility for a clash between Artificial Intelligence and Human Rights rises as people become more reliant on robots to enhance the effectiveness of operations

The possibility for a clash between Artificial Intelligence and Human Rights rises as people become more reliant on robots to enhance the effectiveness of operations and make informed decisions. Artificial Intelligence, if left unregulated, has the potential to cause inequity and even be used to actively violate human rights around the world. However, when utilized properly, AI can improve human rights, promote shared development, and help us all have a better future. So, in general – is AI a threat to human rights?

Threats and challenges related to AI:

Threat to employers: Undoubtedly, the use of AI boosts productivity and efficiency to extreme levels. Moreover, AI is now replacing even some higher-order tasks, such as driving or filling prescriptions. Thus, AI implementation could lead to a huge exodus of workers. Automation, on the other hand, does not always have to be a net negative for the workforce; it can often have a positive impact on it by spurring economic growth and lowering prices. As a result, this same workforce can be transitioned to new tasks that require higher-level thinking and soft skills, in this sense – interpersonal skills and emotional intelligence.
Threats to privacy: are another issue when it comes to AI. AI collects and extrapolates huge volumes of data, including streams of data from mobile devices and other gadgets. When businesses have vast amounts of data about their consumers and competitors, the right to privacy may be jeopardized. Especially as AI advances and new ways to use personal data emerge. Additional precautions, such as data anonymization techniques or actively screening algorithms for privacy vulnerabilities, should be implemented by companies that want to prevent violating privacy.
Threat to job seekers: Yes, and no. For instance, if an organization gets information from public records, it should avoid making assumptions about possible candidates’ personalities and/or skills purely based on this sample information. When a human assessment is combined with AI, it may more accurately determine whether a candidate meets the company’s requirements. A good practice for companies is to unite human consciousness with AI to guarantee a lack of discrimination.

What’s next for AI and Human Rights?

To cover everything, businesses that seek ways to establish trust in their customers should be honest and transparent. Companies worldwide may often appeal to a bigger set of consumers and maintain their existing clients by adopting technology and being upfront about how the firm makes decisions. The implementation of AI should come along with implementing additional measures to reduce the danger of prejudice. Companies can take additional precautions to protect their users’ privacy, or their employees fear becoming unemployed.

By being aware of the potential human rights risks, leaders can utilize AI to improve their business and drive productivity. At the same time, they can avoid some of the negative repercussions that AI may bring. We hope that this information helps you answer the question “Is AI a threat to Human Rights”.

If you want to learn more about Artificial Intelligence, book a free call with us! You can also take a look at our full portfolio of services.

Resource

The moral principles behind AI technology

Category

Data Strategy

Tags

AI Ethics

Last Updated

30 Aug 2022

Summary

Artificial intelligence (AI) and robots are digital technologies that already have and will continue to have a major influence on humanity’s progress and development.

Artificial intelligence (AI) and robots are digital technologies that already have and will continue to have a major influence on humanity’s progress and development. Their use has highlighted basic concerns about what companies should do with those systems, what hazards they pose, and how businesses might manage them. Moreover, everyone nowadays is wondering what are the moral principles behind AI. We try to answer this question in the following lines.

The hazard distribution of AI ethics principles and their meaning.

Although AI ethics are based on concepts, the field has yet to coalesce around a single set of guiding principles. The business world should think about concepts that are better tailored to each domain and situation and strive toward more tangible manifestations that genuinely assist practitioners to lead their activities. However, if we have principles on one side, it, for sure, is necessary to have tools on the other. That is what one research aimed to identify by classifying AI ethics tools and examining the ones already existing on the market.

Key findings of the research acknowledged two main gaps.

To begin with, critical stakeholders, such as members of marginalized populations, are underrepresented in the use of AI ethical tools and their outcomes.
Moreover, there are no external auditing tools for AI ethics, which is a barrier to the responsibility and integrity of companies that build AI systems. Nevertheless, the basis is set – there are at least some AI ethics tools already available in the business environment which need addressing improvements.

AI Ethics and privacy.

The arrival of AI raised privacy issues when compared to what humans were capable of previously. AI allows us to shine a light on every nook and cranny, every crevice, and sometimes even crevices we did not think to search for. Privacy, on the other hand, can serve as a paradigm for translating abstract ideas into actual implementations. Regulation to set actual rules that corporations must follow as it did for privacy, might work for AI ethics as well. On that note, before GDPR, the discussion of privacy was also abstract and unimaginable.

What it succeeded in was that the introduction of GDPR generated a feeling of urgency, requiring enterprises to take real steps to comply.

This example illustrates the need for concrete regulations to keep AI Ethics steady and the same for all.

The lack of well-defined AI ethics structures and the way businesses deploy AI tools.

Various sets of legislation are already appearing in AI regulation. They impose different viewpoints and expectations from different regions of the world. As a result, we may end up in a chaos of regulatory systems that conflicts with human rights. This will make it difficult for businesses to traverse the landscape and comply with the requirements, favoring those with greater resources. Furthermore, if the GDPR precedent is any indication, certain firms may withdraw from particular markets, limiting users’ options and customer satisfaction.

One approach to avoid this is for rules to come with their own set of open-source compliance tools. This would make it easier for numerous firms to compete while also guaranteeing regulatory compliance.

All in all, businesses are still testing the waters of the ethics and moral principles behind AI. In a highly digitalized world, novelties emerge every day. However, nothing should go unnoticed, especially when we speak about AI technologies and the principles supporting them. The use of AI tools, the control of privacy, and the need for systemic restrictions are just the beginning of the issues that require strong measures. On grounds of business development, AI can give a significant competitive advantage. Nonetheless, by lacking any AI ethics, companies may misuse their benefits.

Resource

How can AI technology be applied to enhance Cyber Security

Category

Data Strategy

Tags

AI Ethics, Data Governance

Last Updated

30 Aug 2022

Summary

In 2021 weekly cyber-attacks on corporate networks went up 50% compared to 2020 according to Check Point Research. That means over 900 attacks per organization per

In 2021 weekly cyber-attacks on corporate networks went up 50% compared to 2020 according to Check Point Research. That means over 900 attacks per organization per week in the fourth quarter of 2021. What does this translate to?

Cyber Security is not a human-scale problem anymore. AI is and will be relied upon to handle this vast number of threats.

But how?

The first task of AI and ML when it comes to Cyber Security is identifying and prioritizing Zero-Day malware threats.

Apart from the high volume of threats, the sheer speed at which these threats adapt and change is a key issue for Cyber Security professionals. Malware keeps improving, and humans are unable to cope with the pressure. Even legacy software such as antivirus programs are unable to detect new threats, as by the time they are updated to account for a new piece of malware, there are already newer threats available.

AI can combat this ever-evolving threat by using deep learning algorithms in order to cross-check data and apply an ‘if this, then that’ approach to detecting problems. It looks at past occurrences and analyses current and predictive data to form a solid point of reference that detects malware. The more data and malware it goes over, the better it becomes at detecting new malware. This is done at speeds capable of outperforming the competition.

On top of that Ensemble learning is also used. This translates to using a layered approach, where multiple learning models are combined to create one that is more robust. This can boost detection rates and lower errors drastically.

Of course, all of the above is done under human supervision, where the Cyber Security team can focus on the AI and the real attacks, rather than on false positives.

Endpoint security is becoming absolutely necessary.

Cyber-attacks haven’t only increased on corporate networks. Most people have spent a lot of time working from home in the last two years. This has led to the fast development of new malware and ransomware, capable of consistent attacks that businesses have to defend their employees at home from.

Aside from the increasing number of threats, and the ever-adaptiveness of them, business also face an increasing number of endpoint devices – from laptops, to computers, to phones, tablets, and wearable technology. Cyber threats target weaknesses based on user behavior and poor digital hygiene.

This variety of devices and threats can drain a Cyber Security team of their time and resources if they rely on standard practices. Meanwhile legacy software can cause potential disconnects, and zero-day exploits can targeted antivirus programs. As mentioned above, false positives also drain time and resources.

That is why AI and ML have become more and more in demand when it comes to endpoint security. The ability to examine every device, and monitor activity is crucial.

The information to be processed is too large to be examined by humans, but AI can do that easily.

In conclusion, just like any tool, AI and ML are best used when backed up by experience and knowledge. While AI technology won’t eliminate the need for a dedicated Cyber Security team, they will make its job much easier.

You can rely on Cosmos Thrace to give you the security that only AI can afford. With our hands on experience, we will support you on every step of the journey to security.

Resource

Top 5 Applications of AI in Supply Chain

Category

Industry Solutions

Tags

Logistics, Supply Chain, MLOps

Last Updated

30 Aug 2022

Summary

The top 5 Applications of AI in Supply Chain: In recent years, supply chains have become considerably more challenging to manage due to the constant

The top 5 Applications of AI in Supply Chain:

In recent years, supply chains have become considerably more challenging to manage due to the constant data flow. The accurate forecasting of demand has a vital role in a business from any category to continue running smoothly and achieve sales and profit growth. Experts have relied on traditional forecasting methods and tools to predict demand. Although these methods have helped them a lot in reducing demand-supply mismatch, there is still much room for improvement. Read the article and learn about the Top 5 applications of AI in Supply Chain.

1 Demand Forecasting

Usually, businesses either underestimate or overestimate demand. This happens because the traditional models guess future demand based on historical data sales. However, these methods tend to reduce accuracy as past patterns change. They do not consider external factors that had an impact on previous demand, and they cannot apply them for future demands as well. On the contrary, AI-based solution looks at both external and internal factors to provide real-time data that can optimize processes. AI has the right set of tools and algorithms to predict complex and unpredictable for humans fluctuations in demand volumes and deliver accuracy. Those tools are proven to be effective in analyzing data quickly, providing timely guidance for forecasting seasonal demand. AI implementing will bring striking improvements in customer satisfaction, increase revenue, and reduce inventory expenses.

2 Slotting Adjustment

One example where the traditional methods fail, and AI delivers the best results is dynamic slotting. Slotting is an example of a complex problem that requires considering many factors and has many goals, which sometimes even compete. Traditional slotting methods require a customized model, extensive engineering, and regular data update. Whereas AI minimizes the need for engineering work as well as eliminates manual warehouse mapping and data inputs. AI calculates the travel time required for slotting and self-adapts as conditions change over time. Thus, proper slotting has an impact on productivity, accuracy, order cycle time, and storage quantity and capacity.

3 Staff planning and allocation

AI can provide a solution to other problems in the supply chain as well. One of them is staff allocation, which is vital to ensure that orders will get out on time. To avoid overstaffing or understaffing, and lower overtime managers have to decide on how to allocate staff properly. Usually, managers take labor allocation decisions based on a shift in the volume of work, deadlines, and productivity. The predictions, however, are based on the experience and the skills of the manager, leaving room for error. AI can be used to predict staff requirements and to estimate the deadline for an order. Moreover, AI can determine the best way to fulfill an order, while avoiding delays, through simulations.

4 Labor Management

Another important benefit of the AI approach is more effective performance management. Labor management systems and Engineered Labor Standards (ELS) have been used for years; however, AI has proven to be way more effective. ELS-based performance management requires intensive data collection and uses learning algorithms to estimate the required time for task completion. Meanwhile, the AI integration process is much easier, and the results are more accurate, as it automatically adjusts to changes. The AI solution is based on real-time performance data and considers multiple variables as the type of the task, starting location, quantity.

5 Suppliers Relationship Management

Companies working in the supply chain heavily depend on their partners and suppliers to maintain working efficiently. To continue a smooth working process, they need the right technology that will combine both strategic and sustainable factors, while managing risks. For the big warehouses, the suppliers’ network is complex. Choosing the right supplier for a specific product can be challenging having into consideration possible discounts, delivery deadlines, and other factors. AI will help in managing the huge datasets about the suppliers, the products that provide, and their condition so that the company can manage its orders in the most optimal way in terms of cost, time and risk.

Conclusion:

AI has already begun to change the supply chain industry by providing visibility into all aspects of the industry and delivering powerful optimization capabilities. AI-based solutions can make an intelligent supply chain that can produce higher quality products, by taking care of safety, managing inefficiencies, and decreasing risks.

AI is going to become even more influential as time goes on. This is not just a solution for today, but a better future for your business. Cosmos Thrace enables the integration of AI to the specific needs of your company while ensuring ease and haste in the delivery. Get in touch to discuss how we can help.

Resource

How chatbots deliver a better customer experience

Category

Industry Solutions

Tags

Retail, Ecommerce, MLOps

Last Updated

23 Aug 2022

Summary

Customer experience has always been crucial, no matter what industry you are in. In 2021, out of necessity because of the pandemic, the world has

Customer experience has always been crucial, no matter what industry you are in. In 2021, out of necessity because of the pandemic, the world has fostered online environments that fully rely on customer experiences. So, how chatbots deliver a better customer experience?

So, why do customers value chatbots?

As we’ve said before. No beating around the bush from us. Chatbots are a key part of bettering customer experience these days. While some people desire human interaction when they face issues, most people desire a fast resolution to their questions even more. Chatbots, using NLP tech, have proven to be the saving grace of customers and businesses during the pandemic, when online traffic was higher than ever.

Got to go fast!

That is one of the most valued benefits of a Chatbot when it comes to customer experience. 37% of those asked, stated that Getting a quick answer in an emergency is why they use a chatbot. For companies with a massive customer base, or with limited resources, hiring enough staff to provide timely answers to customers is nigh-impossible. Chatbots, however, are the solution to bettering customer experience in this area.

That is another thing live agents can’t reliably provide – instant answers around the clock.

How important is 24/7 Support?

Around-the-clock agent support is expensive for a business, thus harder to get if you’re a customer. But once we get through the easy questions, once the issue seems to be more complex than what AI can handle, a chatbot can seamlessly escalate to a live agent, without wasting time. The chatbot can easily provide the agent with all information gathered from the customer before, during and after their query. This leads to a quicker issue resolution, and to a better customer experience due to cherishing the customer’s time.

Why customer experience matters:

Companies are truly missing out on sales by virtue of not having live chat support and chatbots. So, are you still wondering how chatbots deliver a better customer experience?

This is where Cosmos Thrace can help, with our timely integration and fine-tuning of AI. Chatbots are an extension of what we already have ample experience in developing. Get in touch to find out more.

Resource

What is the relationship between AI and 5G?

Category

Data Strategy

Tags

MLOps

Last Updated

23 Aug 2022

Summary

As technology continues to evolve, it’s having an increasing influence over our everyday activities, both professional and personal. Disruptive technologies such as Artificial Intelligence

Introduction:

As technology continues to evolve, it’s having an increasing influence over our everyday activities, both professional and personal. Disruptive technologies such as Artificial Intelligence (AI) and 5G are beginning to gain wider acceptance amongst businesses. Even activities that are now just seen as part of normal life, like using phone assistants such as Siri, rely more and more on those technologies. So, let’s explore what is the relationship between AI and 5G.

How are AI and 5G related?

The development of AI models requires large amounts of data. At the same time, 5G technology can collect all that data at a very high speed, from a huge number of devices and with a very low latency. However, collecting data at such a pace is of no use if we cannot analyze it. That’s where AI comes in.

To put it into perspective, 5G provides the infrastructure and massive amounts of data required for successful AI, while AI, driven by advances in machine learning, provides the ability to make sense of the chaos and complexity of 5G.

In a nutshell, AI and 5G are not only related to each other, they are dependent on each other to succeed and play a role in everyday life.

The benefits of the relationship between AI and 5G

So, how can businesses and people make practical use of these advanced technologies? One example is that smart, AI-based devices can be installed almost anywhere and linked to each other. They can then collect data, analyze it and make near real-time decisions. To put this into a perspective, consider the following industries:

Industry 4.0:We are at the edge of the fourth industrial revolution and 5G and AI play big part in its development. Sensors can be installed in machines that form part of assembly lines. This allows vast amounts of real-time data to be extracted through 5G technology. Those sensors are then connected to each other and allow AI technology to make fast decisions based on real-time data. Simply put, operators will know when a machine isn’t working when a machine is being overloaded, or isn’t effective enough. Based on that information they will be able to keep better track of production times.
HealthcareThe potential applications are nearly limitless for the healthcare industry. For example, AI can make medical research faster and more precise due to its ability to process large amounts of data. Moreover, 5G aligned with AI can enable the monitoring of patients in real-time, wherever they are located. This is enabled through the use of IoT devices, such as bands or watches. This can help improve their medical condition and allow behaviors to be tracked and analyzed and inform decisions about their health and treatments.

Embracing the potential

Managed effectively, AI and 5G can benefit each other and work in harmony to enhance applications. On one side, 5G offers the capacity to support low latency and very high speeds, massive numbers of devices and traffic from a diverse and demanding suite of applications. On the other, AI complements this by learning from complex patterns to provide scope for autonomous operation, to transform 5G into a scalable real-time network that is data-driven.

Book a free call with us, if you want to learn more about those technologies. You can also take a look at our full portfolio of services.

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Resources

11Databricks

10Data Strategy

14Technical Guides

22Industry Solutions

16Company News

Showing all 69 resources

Databricks Lakehouse Architecture Evolution: 7 Strategic Updates Every Data Leader Must Understand

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Last Updated

Summary

7 Strategic Updates That Redefine Data Lakehouse Economics

1. Serverless Compute Expansion: Production-Grade JAR Support Transforms Cost Structure

2. Attribute-Based Access Control: Governance Architecture That Scales to Enterprise Reality

3. Research Agent: Business Intelligence Architecture That Actually Reasons

4. Cross-Cloud Data Sharing: Monetization Architecture Without Migration Overhead

5. Real-Time Collaboration: The Productivity Multiplier in Data Infrastructure

6. Revamped SQL Alerts: Proactive Operations vs. Reactive Fire-Drills

7. Foundation Model Expansion: Optionality in AI Infrastructure Architecture

Conclusion

Engineering Velocity as Competitive Advantage: Inside Databricks’ New IDE for Data Engineering

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Last Updated

Summary

From Sequential Deploys to Continuous Validation

Unified Development Surface Eliminates Context Switching

Selective Execution Transforms Iteration Speed

The Strategic Question

From 40 Hours to Zero: How Leading Enterprises Automate Revenue Recognition at Scale

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Last Updated

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The Strategic Architecture of Automated Revenue Recognition

From Data Silos to Strategic Capacity

The Implementation Framework

Conclusion

From Permission Bottlenecks to Scaling Data Governance: The Shift to Attribute-Based Access Control

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Last Updated

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The Cost of Governance Fragmentation

Elevating Access Control to Policy Intelligence

From Reactive Compliance to Proactive Protection

The Competitive Stakes

From Attendee to Sponsor: What We Learned at Databricks Data + AI Amsterdam

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Last Updated

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The Real Conversations Happening at Booth 12.19

Three Technical Shifts Worth Watching

What Databricks Data + AI Amsterdam Did for Us

Next Stop: Databricks Data + AI Stockholm

Beyond Compliance: Reimagining Data Governance as Your Enterprise AI Accelerator

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The Evolution from Defensive to Enabling Governance

Decentralizing Governance for AI Scale

From Static Rules to Dynamic, Context-Aware Governance

Conclusion: Governance as AI Accelerator

Lowering TCO and Future-Proofing Data Strategy: The Hidden Advantages of Databricks SQL

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Introduction

The Real Cost Problem with Traditional Architectures

Databricks SQL Platform: One Engine, Many Workloads

One Copy of Data

One Compute Layer

One Governance Model

One Platform for BI and AI

Lowering Total Cost of Ownership (TCO)

11
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10
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14
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22
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