How to Scope a Databricks PoC or MVP That Actually Gets Funded

If you are asking yourself, "how do I scope a Databricks proof of concept?" or "why did my data project get cancelled after the MVP?" or even "what should I include in a Databricks PoC to convince the CFO?", you are not alone. These are some of the most common questions data engineers and architects wrestle with when starting a new Databricks initiative. This article gives you a practical, field-tested framework for scoping Databricks PoCs and MVPs so they survive the executive boardroom and make it to production.

TL;DR: How to Scope a Databricks PoC or MVP That Actually Gets Funded

• 80% of data projects fail before reaching production, almost always because they cannot prove return on investment, not because of technical issues.
• Most teams no longer need a PoC. Databricks is a proven technology. What you need is an MVP that demonstrates business value.
• Use the 3-question framework to scope every project: (1) What value are we proving? (2) What technical risks are we validating? (3) What does success look like?
• Always start with governance. In the AI age, well-governed data is a competitive advantage.
• Get executive sign-off on your success criteria before you write a single line of code.
• Avoid the three project-killing mistakes: skipping CI/CD, rushing the timeline, and not securing upfront alignment.

Before scoping anything, understand who you are building for. Every project has at least two types of stakeholders, and your MVP needs to satisfy both.

• The budget holders: the CFO, the director, whoever approves funding. These people need to see value in terms they care about: faster compliance reports that reduce the risk of fines, removal of vendor lock-in that makes costs consumption-based and flexible, or dollar savings compared to the old system.
• The infrastructure owners: your DBAs, DevOps engineers, the people who will manage the solution day-to-day. They need to see that the new system is more stable, generates fewer support tickets, and reduces the chance of overloaded systems crashing under pressure.

If you scope your MVP to address both audiences, your chances of getting to production with the next phase increase significantly. As Digiqt notes in their analysis of Databricks ROI, projects that quantify value for both technical and business stakeholders are far more likely to secure continued investment.

Question 1: What Value Are We Proving?

Stop thinking about Spark performance and Delta table optimization for a moment. Answer this first: how much money is this project saving or generating for the company?

Maybe you are cutting costs by replacing an expensive legacy system. Maybe faster reports mean the compliance team avoids regulatory fines. Maybe you are laying the data foundation for an AI strategy that makes the company more competitive. Whatever it is, you need to express it in dollars. If you cannot show financial value, the project stops here, and it should, because you would be building something nobody can justify funding.

Question 2: What Technical Risks Are We Validating?

The biggest mistake here is trying to validate everything at once. Do not scope an MVP that proves fifteen different technical capabilities. Pick one or two pieces that represent the highest risk and the greatest importance for your specific organization.

Maybe it is compliance, and you need to demonstrate that Unity Catalog is a significant improvement over your current governance setup. Maybe it is processing speed, and you need to show that Spark handles your workloads faster and more reliably. The specifics change from project to project, but the principle stays the same: identify the riskiest technical assumption and make sure your MVP directly addresses it.

Question 3: What Specifically Does Success Look Like?

This is where most teams get vague, and vague gets you defunded. Answers like "we will migrate the data from the old system" are not specific enough.

Specific looks like this: "100% of streaming data is captured and available in the gold layer within five minutes of receipt."

When you define success in measurable, concrete terms and get those criteria signed off by the executive board before development starts, you create a contract. If you deliver on those terms, they have no reason to cut your funding. You will also find that specific criteria keep the team focused during development.

Why ROI Matters More Than Technical Brilliance

Engineers tend to think their Databricks Lakehouse is competing against the old SQL data warehouse. It is not, at least not in the executive's mind.

Your project is competing against every other project in the company. The marketing team is pitching a new content generation platform that will produce 100 more leads this quarter and bring in $50,000 in additional revenue. The R&D team wants a new development platform. Every department has a project they believe deserves the budget.

If the marketing team presents a clear dollar figure and your team presents "we will migrate the data to the new system in a month," the CFO's choice is obvious. As this enterprise scoping guides point out, projects that fail to articulate ROI in the language of the business are consistently deprioritized regardless of their technical merit.

Think in survival mode. If you do not put a price tag on what your project delivers, the project dies.

Put Governance First! It's Your Competitive Advantage

Start with governance. Do not leave it for a later phase.

The regulatory landscape has intensified. GDPR, DORA, and industry-specific requirements mean your company must demonstrate that data is protected, policies are enforced, and there are no leakages. For example, Databricks' own governance documentation makes clear that Unity Catalog is designed to be the foundation layer, not an afterthought.

But governance is no longer just about compliance. In the AI age, it is a competitive advantage. Proper data descriptions, lineage tracking, and discoverability through Unity Catalog mean that AI agents can navigate your data lake efficiently, surfacing context that would otherwise be impossible to find. Companies that govern their data well will move faster with AI than those sitting on an undocumented data swamp.

Do not underestimate this. Start with governance from day one of your MVP, and you will set yourself apart.

Mistake 1: Skipping Version Control and CI/CD

Your MVP is not a throwaway prototype. In most cases, it becomes the foundation of the production system. If that foundation has no version control, no CI/CD pipeline, and no use of Databricks Asset Bundles, you are building technical debt into the project from the start. Follow operational best practices from day one. It costs less time now than fixing it later.

Mistake 2: Not Giving Your MVP Enough Time

MVPs are not quick-and-dirty technical demos. You need six to eight weeks to cover enough scope to demonstrate real value. Rushing the timeline means cutting corners on the exact things that prove ROI to the business. Build it properly or do not build it at all.

Mistake 3: Not Getting Executive Sign-Off Upfront

When you answer Question 3 with specific success criteria, take them to the executive board and get them approved before development begins. This is your insurance policy. If the leadership agreed to those targets at the start, they cannot reasonably pull funding when you deliver on them. Without this sign-off, you are building on hope instead of commitment.