You already know how data quality can impact analytics and so-called “traditional” machine learning (ML) pipelines by causing flawed business decisions or missed opportunities. For example, out-of-date customer information resulting in the wrong products featured for upsell or cross sell, or a spreadsheet with low-quality data leading to erroneous conclusions.

But, as many organizations are currently finding out, data quality also plays a critical role in the success of Generative AI initiatives.