5 pillars of data quality management

Explore the five pillars of data quality management: team composition, data profiling, data quality, data reporting, and data resolution and repair. Learn how they ensure data integrity.
June 3, 2024

What are the Five Pillars of Data Quality Management?

The five pillars of data quality management are team composition, data profiling, data quality, data reporting, and data resolution and repair. These pillars form the foundation for maintaining and improving the quality of data within an organization.

  • Team Composition: This pillar emphasizes the importance of having a dedicated team to manage data quality. The team should consist of data analysts, data stewards, and data scientists who are well-versed in data management principles and practices.
  • Data Profiling: Data profiling involves examining the data and collecting statistics or informative summaries about said data. It helps in understanding the quality of data and identifying any issues that need to be addressed.
  • Data Quality: This pillar focuses on ensuring the accuracy, completeness, consistency, reliability, and timeliness of data. High-quality data is crucial for making informed business decisions.
  • Data Reporting: Data reporting involves presenting data in a structured and meaningful way to facilitate understanding and decision-making. It includes creating dashboards, reports, and visualizations.
  • Data Resolution and Repair: This pillar involves identifying and fixing data quality issues. It includes processes such as data cleansing, data enrichment, and data validation.

Are there other Pillars of Data Quality Management?

Yes, some other pillars of data quality management include people, measurement, processes, framework, and technology. These pillars, while not part of the original five, are also crucial for maintaining and improving data quality.

  • People: The people pillar emphasizes the importance of having skilled and knowledgeable individuals who understand the importance of data quality and are committed to maintaining it.
  • Measurement: This pillar involves quantifying the quality of data using various metrics and indicators. It helps in tracking progress and identifying areas for improvement.
  • Processes: The processes pillar focuses on the methods and procedures used for data management. It includes data collection, data processing, data storage, and data usage processes.
  • Framework: This pillar involves having a structured approach to data management. It includes having policies, standards, and guidelines in place to guide data management activities.
  • Technology: The technology pillar emphasizes the role of tools and technologies in managing data. It includes data management software, data analytics tools, and data storage technologies.

How does Data Quality relate to Data Integrity?

Data quality is one of the pillars of data integrity, along with integration, data enrichment, location intelligence, and data governance. Data integrity refers to the overall completeness, accuracy, consistency, and security of data throughout its lifecycle.

  • Integration: This involves combining data from different sources and providing a unified view. It helps in ensuring consistency and accuracy of data.
  • Data Enrichment: Data enrichment involves enhancing, refining, and improving raw data to make it a valuable asset for business.
  • Location Intelligence: This involves the use of geographic data to make informed business decisions. It helps in understanding spatial relationships among data.
  • Data Governance: This involves the overall management of the availability, usability, integrity, and security of data used in an enterprise.

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