Once you have a shortlist of high-value datasets to upgrade into data products, the next step is to make sure they are actually usable for the people and systems that depend on them. Selection creates focus. It does not create value by itself.

A data product becomes valuable when it fits a real workflow, answers real questions, and can be combined with other data without constant back-and-forth.

Why many data products fall short

Many teams still build data products in isolation. The work is technically “done,” but the product is hard to use because:

• The intended users were never clearly defined.
• Requirements were captured as feature requests, not as decision and workflow needs.
• Data is delivered in a tooling-centric way (for example, “it’s in Airflow”), instead of a consumer-centric way.
• The data cannot be joined, compared, or reused reliably across teams.

The failure mode is usually not missing pipelines. It is missing understanding.

Talk to users, then dig deeper

Start with direct conversations with the primary user groups: product managers, analysts, data scientists, and operational teams.

The goal is not to collect a list of requests. The goal is to understand:

• What decisions users are trying to make.
• What actions follow from those decisions.
• What “good enough” looks like in their context.

Users often show up with a predefined solution and a narrow scope. The data team’s job is to unpack that: identify the underlying problem, the constraints, and the adjacent needs that will appear once adoption grows.

A useful rule: if you only build what users explicitly asked for, you often build something that fits today but breaks tomorrow.

What to capture as “requirements”

User requirements for data products usually fall into a few recurring dimensions. You can use these to make conversations structured and to keep the result skimmable.