How to Evaluate AI Data Readiness

Data and analytics leaders must provide clear guidance on the key criteria for assessing if data is ready for their specific AI needs. At the same time, differing modes of AI implementation have emerged, ranging from AI proofs of concept to full production. This research proposes an evaluation method to verify AI-readiness of data for each specific use case, establishing what prior context from existing uses of data may be applicable and proposing mitigation strategies where needed.

Insights from Industry Research

A recent industry report highlights key considerations for organizations to evaluate their data AI-Readiness:

Four Modes of AI-Ready Data
“AI solutions proceed from proof of concept (POC) to multicontext testing, and are then rationalized to identify where “real” (integral capture) data can replace calculations to operationalize them”*
Align Data Context With AI Utilization
“Metadata is the best representation of how human users interact with data and can be used to “teach” AI to discern appropriate data management practices from hallucinations or overfitting problems.”*
Enhancing Metadata Utilization
“Data readiness for any use case is always assessed by context, at a specific point in time and based upon any revisions that are required. The same is true with AI.”*

*Source: Gartner, How to Evaluate AI Data Readiness, Mark Beyer, Ehtisham Zaidi, Roxane Edjlali, 31 January 2025

Take the Next Step in Your Data Journey

Dremio enables organizations to implement an open and scalable lakehouse architecture designed for performance, governance, and AI-ready data. Learn how organizations are optimizing their data environments by accessing this industry research.

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