Explore

Models are only as good as the data underneath them — and most organizations discover their data problems after committing to an AI project. Explore moves that discovery to the front, where it changes decisions instead of derailing them.

What happens in this phase

• Map the data landscape. Identify candidate data sources, assess their quality, and understand their limitations before any model is selected.
• Hunt for bias early. Real-world data reflects real-world bias. This phase identifies it and plans mitigation — cleansing, augmentation, or rebalancing — before it becomes model behavior.
• Form the hypothesis. Explore is where the project’s core hypothesis takes shape: what signal do we believe exists in this data, and what would validate it?
• Establish data governance. Security, privacy, and ethical ground rules are set now and enforced for the life of the project — not retrofitted after an incident.

Watch out for

• Falling in love with a use case before confirming the data exists to support it.
• Treating data quality assessment as a one-time gate rather than a running discipline.
• Governance as paperwork. If the rules don’t change day-to-day data handling, they aren’t governance yet.

Method spotlight

Explore shares Lean Innovation and MVP Experimentation with the Research phase — cheap, fast validation of the data hypothesis before scale-up. See the diagrams on the framework page.

Go deeper

Chapter 17 (“Data Drives Decisions”) and the data pipeline chapters cover Agile data management in depth — free here from September 2026, or in the full edition on Amazon today.