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 ...

Pharmaceutical and biotechnology companies generate mountains of data that need to be interpreted into information to make decisions. Knowledge about product quality and patient safety need to be ...

A key element of information stewardship is data-quality management – that is, ensuring that the mission-critical data within an enterprise is reliable, accurate and complete. DQM is increasingly ...

DQM is becoming a core capability for organizations that need to make better decisions with data. What are the responsibilities of different roles in DQM? Image: WrightStudio/Adobe Stock Data quality ...

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.

While the datasets are useful tools for training AI models, they do come with their own risks, from regulatory risks to ...

Universities must tighten the quality of the data entered into AI models to improve the output generated by tools such as chatbots. Universities have been cautious adopters of artificial intelligence.

In 2025, enterprises are leveraging AI capabilities to enhance data management. Just like 2023, 2024 was a dynamic year for enterprise data management, and 2025 is shaping up to bring even more change ...