AI doesn’t have to fail on a global scale to cause enormous harm—to individuals, businesses, and societies. Models often make mistakes, hallucinate, drift, and can collapse. Good AI comes from good data, but data quality is a huge problem (and opportunity) across the organization, and yet most companies have overlooked it. Companies need to understand the nuances of the problem they’re trying to solve, get the data right (both by having the right data for that problem and by ensuring that the data is error-free), assign accountability for data quality in the short term, and then push data quality efforts upstream in the longer term.
