Machine learning readiness is the degree to which an organization can access, inspect, document, and safely use the data required for modeling. Without that understanding, project scope, cost, timing, and likely results remain uncertain. section 2.4
Assess readiness by turning unknowns into explicit project work:
- Determine how the required data can be extracted and who can provide reliable answers about it. section 2.4
- Document the data model and inspect the real characteristics of the available data. section 2.4
- Record unresolved access and data questions on the risk register or as contract assumptions. section 2.4
- Address privacy, retention, permitted use, security, and data-handling obligations before extraction. section 2.4
- Add time and funding contingency when data knowledge remains incomplete. section 2.4
Readiness assessment
When stakeholders cannot explain what data exists or how to extract it, propose a short readiness project before modeling. Use it to secure access, inspect the data, establish handling controls, and improve confidence in modeling outcomes. section 2.4
