Data Governance versus Data Quality

Data quality and data governance both strive to optimise data and information to meet business needs.

Simplistically, however, where Data Governance deals with the definition of, and responsibility for, data management standards, Data Quality deals with the practical implementation, monitoring and enforcement of these data management standards for individual platforms and systems. Both data governance and data quality require a balanced combination of process, people and technology in order to be successful.

If data governance asks “What must we do and who is responsible?“, data quality answers “How will we do it?

Data Governance provides focus for Data Quality effort

While data quality can deliver value at a single project level, it is best delivered as part of an overall data management strategy, owned by the Data Governance function. Effective data governance must foster business involvement and responsibility by emphasising the business impact of poor governance. Similarly, enterprise data quality initiatives must nurture business involvement.

For this reason, a strategic data quality platform must:

  • Support collaboration between large data management teams, ranging from business data stewards, to data scientists, to the technical application integration teams, in order to enable the complete data management life cycle – from Data Governance definitions, to Data Quality deployment, to Data monitoring and Issue Remediation.
  • Provide rapid time to value through the leveraging of inbuilt data quality intellectual property that can give value off the shelf and help your data management team to deliver based on data quality best practice.
  • Have strong, certified support for all major enterprise application and platforms to ensure a consistent application of required data quality standards across the enterprise via reusable data quality services and processes

Data quality is the result

Data quality is frequently driven by data governance.

Data quality tools tend to be more technical than data stewardship and data governance platforms.

Data quality rules defined by business should be implemented using a data quality platform – both for automated cleansing, enrichment and matching, and to track non-compliant data rows that can be fed into the data help desk for manual resolution.

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