AI Data Readiness

Why Data Readiness Matters for AI Success

Achieving meaningful AI outcomes requires more than algorithms—it demands comprehensive data readiness. A well-structured approach to data management encompasses proper inventory and cataloging, understanding data lifecycles, and ensuring appropriate data usage. This foundation transforms raw information into actionable intelligence that enables your organization to build trustworthy AI systems that provide real business value.

Data Inventory and Cataloging for AI

Effective AI needs access to both human-centric data definitions and also well-managed raw metrics. Without this low-level data, your models are unlikely to teach you anything new about your business. Similarly, the predictions and classifications produced by AI should be input into inventory and governance programs so that the business can make use of and trust those predictions in decision-making.

Understanding and Managing Data Lifecycles

Traditional machine learning (ML) and GenAI use cases have differing dependencies on data lifecycles. Like most traditional reporting, you need to ensure that GenAI is using the latest, trusted, and published versions of input documents. For many ML use cases, however, the ability to predict future outcomes depends on both information that is accurate now, and historical data that was accurate at the time it was collected.

Appropriate Use of Data

Beyond typical risk factors, AI governance should also ask whether the data is appropriate for the use case. That answer should be evaluated on multiple dimensions.

Building on traditional data quality, AI use cases must also consider accuracy, completeness, and consistency. Data should have the volume and breadth to represent various audiences and scenarios to avoid bias and make predictions more accurate. With remote GenAI services, privacy takes a bigger role in appropriate use assessments.