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 ...

Data quality in the modern economy, where data-driving action is critical to business success, can no longer be perceived as mere tech detail. Business leaders increasingly use data to make strategic ...

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 ...

We’re just starting to tap the potential of what AI can do. But amid all the breakthroughs, one thing is fundamental: AI is only as good as the data it was trained on. Unlike people, who can draw on ...

Many organizations nowadays are struggling with the quality of their data. Data quality (DQ) problems can arise in various ways. Here are common causes of bad data quality: Multiple data sources: ...

Data-driven decisions require data that is trustworthy, available, and timely. Upping the dataops game is a worthwhile way to offer business leaders reliable insights. Measuring quality of any kind ...

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

1. The Data Quality Assessment Framework (DQAF) was developed to address the Executive Board's interest in data quality as expressed during the December 1997 discussion of the Progress Report on the ...

Data quality is paramount in data warehouses, but data quality practices are often overlooked during the development process. The true measure of an effective data warehouse is how much key business ...