Data Readiness for AI Projects

Data readiness is always judged against a specific use case. The same data can be ready for one project and useless for another. An audit usually covers five questions. Can it be reached? Find where the data lives, who owns it, whether it can be pulled out through an API or export, and whether the project team has permission. Data locked in a legacy system or a vendor's platform may exist but still be out of practical reach. Is it good enough? Check completeness (missing values), accuracy, consistency across systems, timeliness and duplicates. Quality only needs to be good enough for the task. Perfect data is rarely needed, but systematic errors are dangerous because the model learns them. Is it labeled? Supervised learning needs ground-truth labels, such as whether a transaction was fraud or which category a ticket belongs to. Labels should be defined consistently, and it is worth checking how often human labelers agree with each other. Is it representative? The data should cover the people, conditions and edge cases the system will meet. Selection bias, such as having outcomes only for approved loan applicants, can make a model look accurate in testing and fail in use. Is it legally usable? Check consent, contracts, licenses, confidentiality and privacy law. Under the EU's GDPR, the principle of purpose limitation means data collected for one purpose cannot simply be reused for an incompatible one without a valid basis. For generative AI with retrieval (RAG), readiness also means current, well-organized documents and retrieval that respects existing access permissions. A common misconception is that a large amount of data means you are ready. Volume does not fix bias, missing labels or lack of legal rights.