Bad data is expensive for AI and for the business. Gartner estimates poor data quality costs organizations an average of $12.9 million per year. This guide explains where those losses come from and the practical steps leaders can take to strengthen data quality in AI.

AI won’t fix bad data, it exposes it. This post outlines the foundations that create data quality for AI: authoritative sources, consistent naming, standardized capture, and clear ownership. Put the basics in place to get accurate insights and dependable AI outcomes.