Frequently Asked Questions

Data modernization projects typically range from €75K for focused initiatives (single data source integration, governance framework) to €400K+ for enterprise-wide transformations (multiple legacy systems, full pipeline architecture, BI implementation).

We start every engagement with a transparent cost breakdown showing infrastructure, migration, training, and support costs separately.

Your CFO receives ROI projections based on measurable outcomes: reduced manual processing time, infrastructure cost savings (typically 30-40% moving to cloud), and compliance risk mitigation.

Enterprise data projects typically span 4-8 months from discovery to production, depending on data volume, system complexity, and organizational readiness.

We use a phased approach: 4-6 week pilot validates architecture and builds stakeholder confidence, then incremental rollout with weekly progress dashboards showing exactly what's migrated, tested, and production-ready.

This prevents "big bang" failures and lets your teams adapt gradually rather than facing a single high-risk cutover weekend.

We use multi-layer validation at every migration phase: pre-migration data profiling, row count verification, checksum validation, and reconciliation reports comparing source and target systems.

Your CTO reviews our rollback procedures before any cutover, and we maintain parallel systems during transition so legacy data remains accessible if issues arise.

Monthly data quality reports show completeness, accuracy, and consistency metrics your compliance team can audit.

We design governance frameworks from day one rather than adding compliance as an afterthought.

This includes data classification, role-based access controls, audit logging, and automated data lineage tracking that satisfy regulatory requirements.

Your security team defines policies, your compliance officers track them through monthly governance reviews, and your business users follow documented procedures they actually understand, preventing the "security vs usability" conflict that derails many governance initiatives.

Most enterprises adopt a hybrid model: consultants handle specialized expertise (migration methodology, architecture design, compliance frameworks) while your internal teams manage ongoing operations and retain institutional knowledge.

We explicitly transfer knowledge through role-specific training, your data engineers learn platform management, your analysts learn self-service tools, your executives learn KPI interpretation.

This prevents consultant dependency while letting you access expertise that would take years to build internally.

We offer tiered support options from basic monitoring (monthly health checks, performance reports) to full managed services (24/7 support, continuous optimization, governance reviews).

During the transition period, we provide 3-6 months of hands-on support as your teams gain confidence managing the platform independently.

Monthly reports track system performance, data quality metrics, and usage statistics in business terms your executives understand, not just technical metrics.

Implementation costs vary based on your data volume, complexity, and migration scope.

We start every project with a transparent assessment that defines costs upfront, breaking them down by phase so your CFO sees exactly what you're investing in at each stage.

Typical enterprise implementations range from €50K for focused use cases to €300K+ for full-scale migrations with AI enablement.

Most Databricks migrations take 3-6 months from discovery to production, depending on data volume and legacy system complexity.

We use a phased approach with weekly progress updates, starting with a 4-6 week pilot to validate architecture before full rollout.

This reduces risk and lets your stakeholders see working results early rather than waiting months for a "big bang" launch.

Enterprises typically achieve three major benefits: 10-100x faster query performance compared to legacy warehouses, 30-60% reduction in infrastructure costs through optimized compute and storage, and unified data governance through Unity Catalog that makes compliance straightforward.

The lakehouse architecture also eliminates data silos, enabling AI and machine learning at scale without building separate infrastructure.

Databricks provides enterprise-grade security through Unity Catalog, which centralizes data governance with role-based access controls, automated data lineage tracking, and audit logging.

We implement governance frameworks where your security team defines policies, data stewards manage access, and business users follow clear rules they actually understand.

This makes compliance audits straightforward and gives executives full visibility into who accesses what data.

Databricks is ideal when you need unified data and AI capabilities in one platform, especially if you're dealing with large-scale data processing, real-time analytics, or machine learning workflows.

Companies typically migrate from Snowflake when they need better AI/ML support or want to reduce costs, and from legacy warehouses (Teradata, Oracle) when they need modern performance and cloud scalability.

If you're only running simple BI dashboards with no AI plans, Snowflake or traditional BI tools might be more cost-effective.

We define success metrics with your executives before any development begins, then track them through weekly dashboards your CFO can access directly.

You see concrete results, e.g. churn prediction model saved €240K in Q1 by retaining 15% more customers, rather than vague "improved accuracy" claims.

Monthly reports compare AI predictions to actual outcomes in business terms, showing exactly which models deliver value and which need adjustment.

This transparent measurement approach prevents the common problem where AI pilots look impressive but fail to demonstrate real business impact.

We implement governance frameworks from day one with model versioning, audit trails, access controls, and data lineage tracking built into the infrastructure, not added as an afterthought.

Your security team reviews and approves architecture before any deployment, your compliance officers receive automated audit logs for every AI decision, and your legal team can demonstrate regulatory compliance through documented lineage that satisfies GDPR, EU AI Act, and industry-specific requirements.

This governance-first approach prevents compliance issues before they start rather than scrambling to fix them after deployment.

Every model we deploy includes clear documentation showing how predictions are made, which data drives decisions, and confidence levels for each output.

Your business users see simple explanations ("this customer is high-risk because payment history shows 3 late payments in 6 months"), while your data scientists can access full technical details including feature importance, SHAP values, and model architecture.

We also document when AI recommendations should be trusted versus when human judgment is needed, ensuring your teams understand both the capabilities and limitations of each model.

We build your team's independence through knowledge transfer at every phase, not consultant dependency.

Your data scientists receive hands-on MLOps training during development, your IT teams get deployment guides they can follow independently, and your business users learn to interpret AI outputs through role-specific workshops.

We provide 3-6 months of hands-on support as your teams gain confidence, then offer tiered support options from basic monitoring (monthly health checks) to full managed services.

Enterprise AI projects typically run 4-8 months from discovery to production, and we strongly recommend starting with a 6-12 week pilot that validates one critical use case before full commitment.

The pilot proves your approach works with real data, demonstrates measurable business value to executives, and identifies any data quality or integration issues early, preventing the "big bang" failures where organizations invest heavily only to discover fundamental problems during deployment.

Weekly progress dashboards show exactly what's built, tested, and production-ready at each phase, so you're never wondering where the project stands.