Research · Data for AI

Data Quality & AI Readiness

AI readiness is not a general property of your data. It depends on the use case. Data that is fine for monthly reporting may be too incomplete, inconsistent or poorly labeled for an AI system that makes decisions or answers questions.

Dimensions to assess

• Availability: can the AI system reach the data it needs, when it needs it?
• Quality: accuracy, completeness, consistency and timeliness for the specific use.
• Representativeness: does the data reflect the population and conditions the AI will face?
• Labeling: are labels and outcomes reliable enough to learn from or evaluate against?
• Structure and context: are documents current, deduplicated and tagged with useful metadata?
• Permission: is the organization allowed to use the data this way?

Unstructured data

Generative AI relies heavily on documents, emails, tickets, transcripts and web content. These sources often contain outdated versions, duplicates, conflicting guidance and sensitive details. Preparing them for AI means curating authoritative content, removing what should not be used and tagging material with owners, dates and sensitivity.

Why it matters

Poor data leads to wrong answers, biased outcomes and projects that stall after the pilot. Fixing data problems after deployment is more expensive and more visible than addressing them first.

Common pitfalls

• Declaring the organization “AI ready” without reference to specific use cases.
• Feeding entire document stores into AI without curation.
• Fixing data in the AI pipeline instead of at the source.

How to get started

• Assess data readiness for your top three use cases, not in the abstract.
• Assign owners for the content AI will rely on.
• Set quality thresholds and monitor them.
• Remove or archive outdated content before indexing it.