COMPEL Glossary / data-readiness
Data Readiness
Data readiness is an assessment of whether the data required for an AI initiative is available, of sufficient quality, properly governed, legally accessible, and representative of the populations the AI system will serve.
What this means in practice
Data readiness is the single most common cause of AI project failure: organizations commit resources to building models before verifying that the necessary data exists and meets quality requirements. A data readiness assessment evaluates data availability (does it exist?), accessibility (can the AI team access it?), quality (is it accurate, complete, consistent, and timely?), governance (is its use legally and ethically appropriate?), and representativeness (does it fairly represent all relevant populations?). In the COMPEL framework, Data Readiness Reports (TMPL-M-005) are mandatory Model-stage artifacts that force early confrontation with data realities before resources are committed.
Why it matters
Data readiness is the single most common cause of AI project failure. Organizations routinely commit resources to building models before verifying that the necessary data exists, meets quality requirements, and can be legally accessed. Early data readiness assessment prevents costly mid-project discoveries that force scope changes, timeline extensions, or outright project cancellation after significant investment has already been made.
How COMPEL uses it
Data Readiness Reports (TMPL-M-005) are mandatory Model-stage artifacts in the COMPEL framework that force early confrontation with data realities before resources are committed. These reports evaluate availability, accessibility, quality, governance, and representativeness. Gate passage from Model to Produce requires demonstrated data readiness, preventing the common anti-pattern of starting development before data is confirmed.
Related Terms
Other glossary terms mentioned in this entry's definition and context.