Data quality management is a crucial part of any data integration process. It may be considered the first step to the integration process, as quality data is the key to achieving profitable insights.

The next step is data stewardship. This is about onboarding people – ideally the lines of business that deal with the data at an operational level – into the data quality process and turning it into a ...

Once applied, this process can become a foundational component of any IT structure, allowing companies to address the quality of data throughout the enterprise.

Data quality testing platform Soda Data NV today announced the launch of SodaGPT, a data management platform that uses generative artificial intelligence to help users define data quality ...

Everyone understands data is important, but many business leaders don’t realize how impactful data quality can be on day-to-day operations. In my experience, nearly all process breakdowns have ...

Quality data underpins companies’ success. Data quality is central to taking advantage of the new technologies and initiatives that businesses adopt to drive competitive advantage.

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Timeliness: Current data must be available. Because colleges and universities deal with so much data, most choose to implement data quality tools — see Gartner’s 2019 Magic Quadrant for top vendors — ...

Here's how to create bulk rules that let you streamline and automate data quality processes in your organization.

A rigorous and scalable QA process for GENIE BPC was accomplished by a multipronged process targeting a subset of records for the most time-intensive QA processes (20% source data verification; 5% ...