Learn the definition of data quality and discover best practices for maintaining accurate and reliable data. Data quality refers to the reliability, accuracy, consistency, and validity of your data.

In the fast-changing field of artificial intelligence (AI), the importance of high-quality data can’t be overstated. AI models are only as good as the data they’re trained on, and so poor data can ...

Unlock the power of your data with an effective data governance framework for security, compliance, and decision-making. Data governance frameworks are structured approaches to managing and utilizing ...

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 root ...

In this podcast, we talk with Cody David, solutions architect with Syniti, which is part of Capgemini, about the importance of ensuring data quality for artificial intelligence (AI) workloads. Being ...

A company’s content lies largely in “unstructured data”—those emails, contracts, forms, Sharepoint files, recordings of meetings and so forth created via work processes. That proprietary content makes ...

For Eric Hirschhorn, data quality is important, but in his view, the conversation about data quality focuses on the wrong things. “We woke up one day and someone sent me a feed of customer records, ...

Data observability firm Monte Carlo Data Inc. is turning its attention to unstructured information, introducing a new capability that will allow enterprises to monitor the enormous volumes of text, ...