In the context of MDM, data quality is a measure of whether enterprise data is fit for its intended use or purpose or if it correctly represents the real-world construct it describes. Organizations often ensure enterprise data is free of duplicates, is consistent, conforms to various standards/formats and accurately meets specific requirements.

Data quality is usually evaluated across a range of dimensions, which include the data’s uniqueness, consistency, precision, conformity, timeliness and accuracy, validity and integrity. Issues with any of these dimensions would constitute a data quality problem. For example, if a customer’s address is outdated, you might send important correspondence to the wrong address. Likewise, if a customer’s address does not conform to the post office’s record of their address, mail intended for them might be delivered to someone else.

Bad data quality is not very healthy for business, and there are countless benefits of improving data quality. Some examples include being able to better predict fluctuations in sales throughout the year, reduce procurement costs from suppliers, maintain compliance with privacy regulations like GDPR and CCPA, and more effectively manage risk when making short- and long-term decisions.

Data validation is the process of ensuring that data is accurate, complete and consistent. It involves checking data for errors, identifying anomalies and confirming that it meets specific criteria or standards. Data validation is important for several reasons, including ensuring data accuracy, maintaining compliance and improving operational efficiency.

Profisee MDM offers a number of features to help organizations improve and maintain data quality. Features like match, merge and survivorship let users break down data silos and enrich records, and a native, bidirectional integration with Microsoft Purview supports unified data governance by enforcing governance standards and remediating deficient data. Profisee also supports integrations with services like Google and Dun & Bradstreet for data enrichment and validation.