Track data governance metrics in 2024
Data Management

Data Governance Metrics: Track and Improve Data Quality

Data governance metrics are tools that show how well your program is doing. They give you clear insights into what's working and what needs improvement. With so many metrics to choose from, it can feel overwhelming. This blog will help you understand and use data governance metrics effectively.

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Author:

Rahul_sachdeva Rahul Sachdeva

Date Published: 27th Mar 2024

Reviewed By:

Arpit_srivastva Arpit Srivastava

Published On: Mar 27, 2024 Updated On: Jun 24, 2025

Author

Rahul_sachdeva
Rahul Sachdeva
Sr. Director - Analytics
Rahul Sachdeva is a seasoned data analytics leader with over 14 years of experience across marketing, sales, and fintech industries. Specializing in data engineering, cloud architecture, business intelligence with marketing analytics, he empowers organizations to optimize their marketing performance and maximize the return on their marketing investments. Recognized as an Icon of Analytics for his contributions to the analytics community, Rahul's leadership and technical expertise enable companies to make data-driven decisions that drive significant business impact.

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Additional Resources

FAQ's

In governance, a Key Performance Indicator (KPI) is a measurable value that demonstrates how effectively an organization is achieving its key objectives and goals. Specifically in data governance, KPIs are used to assess the performance and effectiveness of data governance initiatives, providing insights into areas such as data quality, compliance, security, and overall data management.

Evaluating data governance involves assessing various aspects of the data management process to ensure that it aligns with organizational goals and requirements. This can be done through a combination of qualitative and quantitative methods, including:

The three pillars of data governance are typically: People: This pillar focuses on establishing clear roles, responsibilities, and accountability for data management within an organization. It involves defining data stewardship roles, establishing governance committees, and fostering a data-centric culture. Processes: This pillar involves developing and implementing formalized processes and procedures for managing data throughout its lifecycle. It includes activities such as data classification, metadata management, data quality management, data access controls, and data lifecycle management. Technology: This pillar encompasses the tools, technologies, and infrastructure needed to support data governance initiatives. This may include data governance software, data cataloging tools, master data management systems, data quality tools, and security solutions.

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