Several factors, like consistency, accuracy, and validity, contribute to data quality. When left unchecked, businesses that utilize inconsistent, inaccurate, or invalidated data can lead to poor ...

It can be tough to manage data manually, and doing so can sometimes lead to errors or inefficiencies. Spreadsheets can get overly complex, and data quality can suffer. This has become a large enough ...

Gartner released its new Magic Quadrant for Data Quality earlier this month, with SAS/DataFlux, Informatica, Trillium Software, SAP and IBM placing in the leader’s quadrant. But after talking to data ...

eSpeaks host Corey Noles sits down with Qualcomm's Craig Tellalian to explore a workplace computing transformation: the rise of AI-ready PCs. Matt Hillary, VP of Security and CISO at Drata, details ...

The marketplace depicted in this year's "Magic Quadrant for Data Quality Tools" from industry research firm Gartner presents little upheaval from last year's results -- the same five companies are ...

Dr. Chris Hillman, Global AI Lead at Teradata, joins eSpeaks to explore why open data ecosystems are becoming essential for enterprise AI success. In this episode, he breaks down how openness — in ...

Quality data is the cornerstone of good business decisions. To ensure your data is high quality, it must first be measured. Organizations struggle to maintain good data quality, especially as ...

Data quality — the practice of testing and ensuring that the data and data sets you are using are what you expect them to be — has become a key component in the world of data science. Data may be the ...

Data quality is a top priority for financial firms and it has only grown in importance because of regulation and the need for better operational efficiency. Data quality is hard to measure in the ...

Tools that clean or correct data by getting rid of typos, formatting errors, and unnecessary and expendable data are known as data quality tools. These tools help organizations implement rules, ...