Review Data Readiness

Data readiness is the degree to which data is available, understandable, governed, accessible, reliable, and appropriate for a defined use.

A team needs more than a large quantity of data. It must know what the data means, where it came from, who may use it, how current it is, and whether it represents the task.

AI projects often stall because the data path, ownership, quality, permissions, or interpretation is unresolved even when the model or tool is available.

Before building a support assistant, a company inventories policy documents, owners, access rules, versions, gaps, and update processes.

Clean data is not automatically ready data. Readiness is specific to the intended use and includes governance and meaning.

Ready data is fit, understood, permitted, and maintainable for this use.

* [[input|Input]] * [[data-provenance|Data Provenance]] * [[evidence-selection|Evidence Selection]] * [[ownership-gap|Ownership Gap]] * [[current-state|Current State]]

* [NIST AI RMF — How AI Risks Differ from Traditional Software Risks](https://airc.nist.gov/airmf-resources/airmf/appendices/app-b-how-ai-risks-differ-from-traditional-software-risks/) — official guidance on whether data represents the intended context and on data-quality risks. * [NIST AI RMF Playbook](https://airc.nist.gov/docs/AI_RMF_Playbook.pdf) — official implementation guidance addressing data completeness, representativeness, balance, quality, provenance, and suitability for evaluation.

* Start with [[current-state|Current State]], ownership, and [[data-provenance|Data Provenance]].