The Office for National Statistics (ONS) has published guidelines to improve the quality of data in the public sector. The Government data quality framework, developed by the Government Data Quality ...

To establish a consistent approach to assess, manage and improve data quality across the data lifecycle, covering a wide spectrum of data types, and taking into account the blurred line between data ...

Data quality management is important for enterprise data accuracy and integrity. These frameworks can help you identify and fix problems before they impact your business. While companies may share ...

An article recently published in Nature proposes a new way to evaluate data quality for artificial intelligence used in healthcare. Several documentation efforts and frameworks already exist to ...

The Office of the National Coordinator for Health IT published the Patient Demographic Data Quality Framework of best practices for data management processes that enable hospitals to more effectively ...

The European Medicines Agency (EMA) has finalized a document with recommendations on using the European Medicines Regulatory Network (EMRN) Data Quality Framework (DQF) when submitting premarket ...

We developed a framework of five data quality dimensions (DQD; completeness, concordance, conformance, plausibility, and temporality). Participants signed a consent and Health Insurance Portability ...

After years of experimentation, AI adoption is at the forefront of enterprise strategies in 2025. According to a recent market study on Enterprise Data Transformation by the Intelligent Enterprise ...

Data is essential for the success of any artificial intelligence (AI) project, but understanding what makes data beneficial—or harmful—for AI is crucial. At a high level, machine learning (ML) and AI ...

We cover the seven leading data quality solutions that simplify the work of data management and help turn all those cell values into something that can be used for business decisions. It can be tough ...