No matter how sophisticated the AI model, its power depends on the quality, structure and context of the data beneath it.

Data quality is a complex and context-dependent concept often misunderstood across business, technology, process, and data science domains, with each attributing different issues to it.

Some of the best thinking on new AI-native systems contemplates the quickness of technology generations, and what it means for design.

Poor data quality and integrity compounded with data silos, lack of integration, and a skills gap make the problem more profound.

Key findings show organizations averaging just 42/100 on data trust maturity, with the lowest scores in areas such as remediation workflows, policy enforcement, and reference/master data quality.

It’s time for the music industry to shift from endless data clean-up to a strategy of quality at the source, and transform data from a liability into a reliable asset. The following comes from Natalie ...

PHILADELPHIA, PA — Qlik® has once again been named a Leader in the 2025 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions, marking its sixth recognition in the highly regarded ...