Data quality is a fundamental challenge for downstream data mining tasks. While numerous studies have addressed data quality issues in various contexts, there is a notable lack of systematic research ...

The CLEAN framework is a structured, five-step methodology for data cleaning: Conceptualize, Locate, Evaluate, Augment, and Note, aimed at addressing data issues systematically and transparently.

A political push to pare back EU sustainability regulations must not mean losing information on material risks or force investors to rely more heavily on third-party data providers, Europe's main ...

We talk to Cody David of Syniti about how to ensure data quality in datasets for AI, why a ‘data-first’ attitude is key, and the quick wins an organisation can gain in data quality.

Everyone understands data is important, but many business leaders don’t realize how impactful data quality can be on day-to-day operations. In my experience, nearly all process breakdowns have ...

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