Before defining complex measures or building pipelines, every successful Data Product must master the intersection of the person using it, the job they need to do, and the exact information required to do it.
A data product is only as good as its alignment across these three elements. Miss one, and the product will fail to drive adoption.
Who is consuming this data? You must deeply understand their technical proficiency, their daily workflows, and how they prefer to consume information.
What is the "Job to be Done"? Data is useless unless it is applied to a specific action, decision, or automated process that drives the business forward.
What exact information is required to support the Task for the User? This dictates the schema, the latency, the history, and the quality SLA required.
The answers from the User, Data, and Task triad completely dictate how you engineer your Output Ports (how the data is delivered). A single dataset might need multiple output ports to serve different triads.
If you design a beautiful real-time API (Data) for a CFO (User) who just wants a monthly PDF report (Task), your product will fail.
User: CMO (Non-technical)
Task: Monthly Budget Allocation
Data: Highly aggregated, historical
→ Build a curated BI Dashboard View.
User: Recommendation Engine (Machine)
Task: Serve live product suggestions
Data: Granular, sub-second latency
→ Build a high-performance REST API.
Start defining your data products by focusing intensely on the User and their Task, before you write a single line of SQL.