Data quality is a measure of the condition of data based on factors such as accuracy, completeness, consistency, reliability and whether it’s up to date. Data quality issues significantly impact thousands of organizations every day. Solutions Review brought this interesting information to us in their article, “What is Data Observability and How Does it Improve Data Quality?”
Bad data can have significant business consequences for companies. Poor-quality data is often pegged as the source of operational snafus, inaccurate analytics and ill-conceived business strategies. Low data quality can break automated reports with figures that do not add up, or they can lead to machine learning models producing poor recommendations. These result in poor user experiences or wrong business decisions including investment in the wrong sectors, reducing production or capacity rather than increasing it and a multitude of other issues that come with high costs.
Maintaining data quality is important as is accessibility. Making data accessible is something we know a little about. A controlled vocabulary is needed to ensure that the machine-assisted or fully automated indexing is comprehensive, regardless of what you are indexing. Access Innovations is one of a very small number of companies able to help its clients generate ANSI/ISO/W3C-compliant taxonomies to make their information findable.
Melody K. Smith
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
