What is data governance?

Data governance involves defining policies, processes and roles for data handling, enhancing decision-making and ensuring regulatory compliance by managing data assets and enforcing data access. Effective data governance improves data transparency and reduces risks.

What is the difference between data governance and data management?

Data governance oversees data availability, usability, integrity, and security within an organization by setting policies and procedures for data handling. Data management involves storing, organizing and managing data, including data modeling and data warehousing.

What are the pillars of a data governance framework?

A data governance framework aligns organizational goals and challenges to achieve measurable, scalable results and is supported by four key pillars. The four pillars of data governance include distinct use cases connecting governance to business outcomes; quantifiable value for assessing success; targeted product capabilities empowering teams through data discovery, collaboration and access; and a delivery model that supports scalable service for all your data-intensive use cases.

Deliver automated, federated data governance

Operationalize data governance workflows and processes for greater understanding and reduced data risk.

Key features and capabilities

Drive a common, consistent understanding of business definitions

Define the terminology, rules and regulations with a business glossary for improved data usage and reduced data risk.

Enforce data access easily

Protect data assets from unauthorized access by assigning roles and responsibilities so the right data users can access the right data assets.

Manage and define data relationships that matter

Reconcile missing records and other inconsistencies between systems with reference data management for more accurate reporting and analysis.

Reduce the risk of compliance failures

Create, review and update data policies with centralized policy management to maintain compliance across your business.

Automate data governance processes

Easily design workflows to automate processes to deliver business-driven data governance.

Ensure data privacy

Reduce the cost and risk of compliance failure with automated enforcement of data policies.

Drive consistent terminology

Document metadata with details about its origin, format, use, structure and relationship to other data.

Respond to data issues easily

Raise, manage and resolve data issues with automated incident workflows.

Frequently asked questions

What is data governance?

Data governance involves defining policies, processes and roles for data handling, enhancing decision-making and ensuring regulatory compliance by managing data assets and enforcing data access. Effective data governance improves data transparency and reduces risks.
Learn more about the importance of data governance here
What is the difference between data governance and data management?

Data governance oversees data availability, usability, integrity, and security within an organization by setting policies and procedures for data handling. Data management involves storing, organizing and managing data, including data modeling and data warehousing.
What are the pillars of a data governance framework?

A data governance framework aligns organizational goals and challenges to achieve measurable, scalable results and is supported by four key pillars. The four pillars of data governance include distinct use cases connecting governance to business outcomes; quantifiable value for assessing success; targeted product capabilities empowering teams through data discovery, collaboration and access; and a delivery model that supports scalable service for all your data-intensive use cases.
Learn more about how to create a data governance framework here

Deliver automated, federated data governance

Key features and capabilities