A data quality score is a quantifiable metric that assesses the completeness, accuracy, consistency, and timeliness of a dataset or specific data points.
A data quality score is a numerical representation of the overall health and reliability of data within a system. It is typically calculated by evaluating various dimensions of data quality, such as completeness (how much data is missing), accuracy (correctness of data), consistency (uniformity across systems), uniqueness (absence of duplicates), and validity (conformance to defined rules). This score provides a clear, objective measure that helps organizations identify data issues, prioritize data governance efforts, and track improvements over time.
In e-commerce, a high data quality score for product information directly translates to a better customer experience and improved business performance. Poor data quality leads to incorrect product descriptions, missing attributes, or outdated pricing, causing customer frustration, increased returns, and lost sales. By actively monitoring and improving data quality scores, e-commerce businesses can ensure accurate product listings, enhance search engine visibility, and build customer trust, ultimately boosting conversion rates and operational efficiency.
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