The rush to AI may feel new, but data professionals have been here before. When organizations moved from transactional systems to reporting, analytics, and business intelligence, we discovered that data designed to run a business was not always ready to explain it.

Let's talk about what decades of experience in data warehousing, analytics, and data quality can teach us about preparing data for AI: the quality issues that create unreliable results, the risks of incomplete or biased information, and why responsible AI starts long before a model ever sees a row

Throw in the need to deal with bias, data security, and ethics and we have some work to do.

Good data does more than improve accuracy. It improves trust, transparency, accountability, and confidence in the outcomes AI produces.