The authors distilled the terms down to 15 considerations, or “dimensions” that they say healthcare entities should use to determine the quality of their data.

Once you determine what dimensions are to be used and set a target of quality that makes the data fit for purpose, consider ongoing profiling and presentation of data quality levels.

As a component of data governance and management, data quality is a measure of a dataset’s overall integrity with the goal of consistency. In a recent blog post from Great Expectations, author Sam ...

We need AI-specific data quality solutions that can address a complex array of over 70 data quality dimensions, including representativeness and completeness.

This article is one of a series describing how to build out a solid foundation for agile data warehouse development, focusing on data quality.

Four dimensions of data quality Your recent Data Analytics Survey (Data Strategy, June), found that nearly four in ten organisations do not have data quality programmes in place.

Electronic health record (EHR)–based real-world data (RWD) are integral to oncology research, and understanding fitness for use is critical for data users. Complexity of data sources and curation ...

When measuring the data quality in data warehouses and databases, intrinsic data quality dimensions such as consistency are independent of the use cases, whereas extrinsic ones such as reliability ...