Track data governance metrics in 2024
Data Management

The Essential Guide to Data Governance Metrics: What to Track and Why

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.

post

Author:

Rahul-Saini Rahul Saini

Date Published: 27th Mar 2024

Reviewed By:

Sameer_pawar Sameer Pawar

15 min read

Author

Rahul-Saini
Rahul Saini
Content Marketing Consultant
Rahul Saini is a published author of three books, brand storyteller, and marketing specialist with experience across multiple industries like manufacturing, IT, and publishing. He is an intellectually curious, and creative person who loves to tell stories, read books, and write fiction.

Ready to get started?

Increase your marketing ROI by 30% with custom dashboards & reports that present a clear picture of marketing effectiveness

Start free trail
subscription

Experience Premium Marketing Analytics At Budget-Friendly Pricing.

customer-care

Learn how you can accurately measure return on marketing investment.

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.

Your Gen-AI Marketing Data Assistant is Here—DiGGi-GPT. Get Access Today!