Data Understanding

Data understanding is the audit before the build. The team inspects available sources, checks definitions, looks for missingness, and asks whether the data can answer the business question honestly.

This phase is not just technical profiling. It is also domain work: a “view” may mean a trailer start, a full episode, a household profile, or a bot-like event unless someone checks.

The useful output is a map of available evidence, known weaknesses, and gaps that must be accepted, fixed, or reframed before modeling starts.

• Assuming a field name tells the whole story.
• Ignoring missing data because the dashboard still renders.
• Confusing data that is available today with data that would have been available at prediction time.