What it means
Data readiness describes how prepared an organisation's data is for a specific AI use: whether the right data exists, is accessible, is of sufficient quality and structure, is legally usable, and is maintained. It is assessed for a particular use case, not in general.
Real-world example
A museum wants an AI assistant for its collection. Data readiness checks find that 30% of object records lack descriptions, provenance information is held in a separate spreadsheet, image rights are unclear for older photographs, and some records contain outdated or offensive terminology that needs contextualising before AI uses it.
Why it matters for AI adoption
Data problems are one of the most common reasons AI projects are delayed or fail. Assessing data readiness early shows realistic timelines and costs, and data improvements often deliver value even beyond the AI project.
Questions to ask
- Do we have the data this use case needs, in sufficient quality, and are we allowed to use it?
- Who owns this data, and who will keep it up to date?