Choosing which datasets become true data products is a portfolio decision. If you promote too much, standards become theater and teams burn out. If you promote too little, you miss the few datasets that could unlock real reuse and business impact.

The goal is to keep the list small and outcome-driven: upgrade data into products when it reliably enables strategic decisions, operations, or automation across more than one team.

Start with strategic intent (not datasets)

Begin by forcing clarity on the why behind the use case. The strongest candidates are tied to business pain points that teams already feel, and outcomes leaders already care about.

A few questions make “strategic intent” real:

• What decision or bottleneck is this meant to improve?
• What outcome would change if the data was better (speed, conversion, cost, risk, reliability)?
• Who is the primary user, and how will they use it in their workflow?

Map strategic use cases to the data

Once pain points and outcomes are clear, map them to the data.

A dataset becomes a strong candidate for product treatment when it sits at the center of a high-value use case and would otherwise be rebuilt (or inconsistently redefined) by multiple teams.

Typical strategic use case types include:

• Analytics: core dashboards, KPI logic, recurring reporting, executive decision support
• AI and machine learning: training data, features, evaluation datasets, monitoring inputs
• Operational: near real-time decisioning, automation, customer-facing workflows

What matters is not the number of use cases. It is the strategic significance and the reuse potential.

Keep the candidate list deliberately small

A short list forces trade-offs and makes success measurable.