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 ...

Introduction Data quality in electronic health records (EHRs) is central to data-informed healthcare. Health professionals play a key role in ensuring data quality yet the complexities of clinical ...

Bad data is costing Australian firms about AUD A$493,000 a year and slowing decisions in mid-sized businesses.

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 ...

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 ...

Unlock the power of your data with an effective data governance framework for security, compliance, and decision-making. Data governance frameworks are structured approaches to managing and utilizing ...

Through literature review and collaborative design, we propose the Focus, Activity, Statistic, Scale type, and Reference (FASStR) framework to provide a systematic approach to health care operation ...

Utilities are becoming increasingly skilled at adapting to changes brought on by the digital age: pressure from automation, disruption from new technology, and challenges with how to ingest, manage, ...

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 ...