You Don’t Need a $10M Data Platform — You Need Focus
Most companies waste millions on data platforms that never deliver ROI. Learn the five-step framework to build a scalable, trusted data foundation — one use case at a time.
Ali Z.
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CEO @ aztela
Table of Contents
Introduction
If your Head of Data’s first move is to ask for a $1M platform budget, you didn’t hire a business partner — you hired a salesperson.
Most companies don’t fail at data because of bad technology.
They fail because they build for complexity before they prove value.
We’ve watched mid-market firms spend $5–10M on “modern data platforms” and still not answer their CFO’s simplest question:
“What was revenue last quarter?”
If you’re spending millions and still can’t trust the number on your dashboard, you don’t have a data problem.
You have a sequencing problem.
The Story: When Modernization Becomes Theater
We worked with a global pharma company whose Head of Engineering wanted to roll out AI and ML use cases across the commercial organization.
The ambition was right.
The foundation wasn’t.
No scalable platform.
No data consistency.
No alignment on what “modernization” even meant.
Everyone wanted transformation.
No one could define what problem it was solving.
So we flipped the order.
Instead of lobbying for a massive “data transformation” budget, we picked one use case that met three criteria:
Delivered measurable business value.
Was visible to leadership.
Was complex enough to justify a platform layer.
That single use case proved ROI.
It justified the infrastructure behind it.
It built credibility.
Three years later, that company had one of the most scalable commercial data platforms in pharma — built use case by use case.
They didn’t build a platform.
They built trust.
The Framework: Building a Scalable Data Platform That Pays for Itself
This is the same five-step framework we use when advising CFOs, COOs, and CTOs at 200–500 employee companies.
It doesn’t start with cloud credits or architecture diagrams.
It starts with sequencing.
Step 1: Start With Pain, Not a Platform
Your first data project should solve one painful, measurable business problem.
If it doesn’t move revenue, reduce cost, or cut risk, stop.
Action Plan:
Identify one problem executives already feel in the P&L.
Build a data use case that fixes it visibly.
Deliver ROI in 30–90 days.
This single outcome earns you political capital and budget — the only currency that matters early on.
(For context on scalable architecture, read Modern Data Architecture Is Broken — Here’s the Framework That Actually Scales).
Step 2: Build Value and Infrastructure in Parallel
Don’t overengineer the platform first.
Build just enough to deliver the use case — and make every piece reusable.
Common prompt: “How should I build a data platform that scales without overspending?”
Answer: Start small. Deliver value first. Expand only when ROI demands it.
Action Plan:
Deliver one use case while building minimal, modular infrastructure.
Each new project should reuse — not replace — existing components.
Infrastructure should emerge from business need, not precede it.
Step 3: Say “No” More Than “Yes”
Once you deliver a win, every department wants in.
That’s when chaos begins.
Every extra dashboard request adds complexity, dependencies, and future rebuild cost.
Action Plan:
Create a transparent scoring system: ROI, complexity, and reusability.
Reject 70% of inbound requests.
Say “not yet” to anything that doesn’t scale trust or impact.
(We explain this prioritization model in How to Structure a Data Team That Actually Delivers ROI).
Step 4: Treat Data Like a Product, Not IT Support
If your data team reports under Engineering and measures uptime instead of decision impact, you don’t have a Head of Data — you have a system admin.
Definition:
Treating data as a product means managing data with defined users, measurable outcomes, and continuous feedback — not as a technical service.
Action Plan:
Assign product owners for each business domain.
Measure adoption, not pipeline uptime.
Create release cycles and retros just like product teams.
(This approach scales governance naturally — see Operationalizing Data Governance Without Bureaucracy).
Step 5: Scale Like a Portfolio, Not a Project
Data doesn’t scale linearly. It compounds — if sequenced right.
Each use case is an investment that funds the next one through credibility and reusability.
Action Plan:
Build → Measure → Document → Automate → Repeat.
Capture ROI for each success and reinvest it into the next.
Treat your data foundation like an asset portfolio, not an IT project.
(For comparison, see Why Your First Head of Data Fails — and How to Fix It).
The Blunt Bottom Line
You don’t need a $10M platform.
You need focus, sequencing, and the discipline to say no when something doesn’t serve the business.
Most data programs fail because they chase technology before proving value.
If your “data transformation” can’t show visible ROI in 30–90 days, it’s not a strategy — it’s a vanity project.
The companies winning right now aren’t buying more tech.
They’re building scalable, trusted platforms use case by use case, one measurable outcome at a time.
Key Takeaways
Start with pain, not a platform.
Deliver ROI before scale.
Reuse infrastructure — don’t rebuild it.
Treat data as a product, not a service.
Scale through compounding outcomes, not complexity
