Data quality is the measure of how well suited a data set is to serve its specific purpose. Measures of data quality are based on data quality characteristics such as accuracy, completeness, consistency, validity, uniqueness and timeliness. Data quality is essential to operate a successful data pipeline and enable data-driven decision-making. This interesting information came to us from Tech Target in their article, “7 data quality best practices to improve data performance.”
Data is of high quality when it satisfies the requirements of its intended use for clients, decision-makers, downstream applications and processes. The quality of the data is an important attribute that could drive the value of the data and, hence, impact aspects of the business outcome, such as regulatory compliance, customer satisfaction, or accuracy of decision making.
Data quality is essential for any analysis or business intelligence. At the end of the day, content needs to be findable and, that happens with a strong, standards-based taxonomy. Data Harmony is our patented, award winning, artificial intelligence (AI) suite that leverages explainable AI for efficient, innovative and precise semantic discovery of your new and emerging concepts to help you find the information you need when you need it.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
