Data quality has evolved from a “nice-to-have” to an essential and, in some cases, mission-critical part of a data operation. When AI started to gain traction, many data governance and data science leads saw the writing on the wall. The playing field is leveling, and those who can properly capitalize on their data will win with new use cases. Companies that underinvest in data quality are struggling to keep up.
Initially, AI was seen as a silver bullet, promising to automate complex processes and handle large volumes of data effortlessly….
