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 problems are systemic in agriculture, the researchers note. Historical reliance on local practices, fragmented ...

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 ...

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 ...

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: ...

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 ...

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 ...

The marketing landscape is undergoing nonstop disruption. Rapid innovation and the rise and spread of automation have created an “always-on” culture that’s “connected everywhere” and “streaming ...

The true measure of an effective data warehouse is how much key business stakeholders trust the data that is stored within. To achieve certain levels of data trustworthiness, data quality strategies ...