How to choose your first data product use case.
A practical framework for picking a first use case that proves value, earns trust, and creates the reuse that makes the second and third data products much cheaper than the first.
Executive Summary
The first one shapes everything that follows
The first data product an organization ships sets the tone for the entire program. Choose well, and the second, third and fourth get cheaper, faster and more trusted. Choose badly — too vague, too political, too disconnected from a real decision — and the program stalls before it has a chance to compound.
A good first use case is not necessarily the biggest. It is the one most likely to ship, be adopted, and generate reuse.
What Good Looks Like
Five tests for a first use case
- Valuable. Clearly connected to a decision, workflow or outcome that matters.
- Measurable. You can describe what "better" looks like.
- Achievable. Data exists, ownership is willing, no unresolvable blockers.
- Reusable. The data product will support more than one decision over time.
- Owned. A named business owner is genuinely committed.
Common Misconceptions
Where first use cases go wrong
MythStart with the biggest, most visible problem.
RealityStart with a problem that is specific enough to prove value and broad enough to create reuse and momentum.
MythStart with AI ambition.
RealityStart with a decision or workflow that matters. AI value comes later, on top of trusted data products.
MythStart with whatever the data team can deliver fastest.
RealityStart with a use case the business will actually adopt — speed without adoption is wasted.
Practical Guidance
How to run the selection conversation
Anchor to a real decision
Choose a use case where a named business team makes a recurring decision today and would benefit from better, faster or more trusted inputs.
Test decision value
Is the decision frequent enough, valuable enough, and risky enough to justify a managed data product?
Test data readiness
Is the underlying data accessible, reasonably understood, and not blocked by unresolvable quality, ownership or compliance issues?
Test reuse potential
Will the same data product support related decisions, dashboards, workflows or future AI use cases — or is it a dead-end?
Test ownership willingness
Will a named business owner step forward and stay engaged? Without that, the use case will stall regardless of technical quality.
Avoid the worst patterns
Avoid vague transformation ambitions, pet projects without consumers, and use cases that require organizational change before any value is delivered.
Questions to Ask Internally
Use these in a first workshop
- Which recurring decision would benefit most from a more trusted, governed data product?
- Who owns that decision today, and would they sponsor the work?
- How many other decisions could reuse the same data product?
- What would we measure to know it worked?
- What would have to be true for this to ship within one quarter?
Where Data Tiles Fits
Latttice for first use cases
Latttice helps business teams stand up a first trusted data product without large platform programs, because governance, ownership and trust are part of the way the product is created — not bolted on later. That is what makes a first use case a foundation rather than a one-off.
Key Takeaways
What to remember
Key Takeaways
The best first use case is valuable, measurable, achievable, reusable and tied to a real decision.
Avoid starting with vague AI ambition or top-down transformation language.
Business ownership willingness is as important as technical feasibility.
Reuse potential matters more than initial size — it determines whether the program compounds.
Momentum beats ambition. A first use case that ships and is adopted is worth more than a perfect one that stalls.
Pressure-test your first use case
Use the Data Tiles assessments to evaluate decision value, data readiness and ownership before you commit.
Start AssessmentDM Cameron for an executive deep dive, a discussion of the possible, or a general chat about where your data and decisions are heading.
DM John to discuss moving to a decision-driven organization — from where you are today to measurable outcomes.
Cameron writes on decision-driven data, trusted data products, active governance, and AI readiness — and how enterprises move from data ambition to business outcomes.