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.

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

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.

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

Data quality isn’t the only concern. Other things that worry data professionals include ambiguous data ownership, poor data literacy, integrating multiple data sources, and documenting data products, ...

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 ...

Companies are making critical mistakes with how they collect and use customer data, from chasing quantity over quality to ...

Qlik announced its sixth recognition as a Leader in the Gartner Magic Quadrant for Augmented Data Quality Solutions.

A new study released Thursday by research group Epoch AI projects that tech companies will exhaust the supply of publicly available training data for AI language models by roughly the turn of the ...

The integrity and reliability of U.S. economic data have come under increased scrutiny following a series of recent policy ...