Data Governance vs. Data Management: What’s the Difference?

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In today’s data-driven world, effectively governing and managing data is critical for organizations. But what exactly is the difference between data governance and data management? In this post, we’ll break it down.

Data Governance: The “What” and “Why”

Data governance focuses on the big picture strategy for data in an organization. It defines the policies, rules, roles, and procedures for how data should be handled.

Some key aspects of data governance include:

• Defining data ownership and stewardship
• Setting policies for data retention and deletion
• Establishing data security and privacy standards
• Outlining requirements for data quality and integrity
• Providing guidelines for data access and permissions based on roles

In essence, data governance deals with the “what” and “why” for an organization’s data. It involves high-level decision-making to align data initiatives with business objectives and risk management.

Data governance requires participation from stakeholders across the organization, including executives, legal/compliance, IT professionals, and data owners. It answers questions like:

• What data do we collect and store?
• Why do we need this data?
• Who can access specific data sets and for what purposes?
• How long is data retained?
• How do we ensure data accuracy and consistency?

Data Management: The “How”

Data management focuses on the tactical implementation of data governance policies. It deals with the daily tasks of actually acquiring, validating, storing, protecting, and processing data.

Key data management activities include:

• Building and maintaining data storage systems like data warehouses
• Developing data pipelines and ETL (extract, transform, load) processes
• Implementing data quality checks and cleansing routines
• Providing tools/dashboards for data access and analysis
• Backing up and restoring data
• Masking or anonymizing sensitive data

While governance defines the rules, management executes and operationalizes them. Data management is usually carried out by technical teams like data engineers, DBAs, developers, and data analysts. It answers questions like:

• How do we build data storage and infrastructure?
• How is data validated, cleansed, and processed for analytics?
• How can we make data easily available for business users?

Aligning Governance and Management

To get the most value from data, organizations need to align governance and management efforts:

• Governance policies should inform management programs and systems.
• Management capabilities should enable governance policies to be followed.

With proper governance guiding the management activities, organizations can effectively collect, organize, protect, and utilize data to drive business value. Though their focuses differ, governance and management work together to ensure data helps achieve strategic goals.

TLDR;

• Data governance deals with the strategy — the big picture “what” and “why” for data.
• Data management focuses on the implementation — the tactical “how” of managing data.
• Aligning governance and management creates an effective data environment and delivers business value.

Proper data governance and management is key for organizations striving to be data-driven. Understanding the difference between the two lays the foundation for success.

Real World Examples

Customer Data

Data Governance:

• The marketing team defines a policy that customer email addresses can only be used for sending promotional content if the customer has explicitly opted-in to the mailing list.

Data Management:

• The dev team builds subscription management functionality into the website so customers can opt-in or opt-out of marketing emails.
• The DBA implements database triggers to scrub email lists and remove addresses that have opted-out.

Financial Reporting

Data Governance:

• The finance department mandates that all revenue-related data used for financial reporting must retain source metadata and maintain an audit trail.

Data Management:

• Data analysts building financial reports use ETL tools and scripts that preserve metadata as raw sales data is extracted from source systems, transformed, and loaded into the data warehouse.

Application Testing

Data Governance:

• The security team defines a policy for production data to be de-identified before copying it over to non-production environments used by developers for application testing.

Data Management:

• The DBA runs masking routines each night to scramble sensitive fields like names, account numbers, etc. in the non-production database copies.
• The test data is provisioned to developers through virtualized, access-controlled environments per the security policy.

By implementing data management processes that directly enable data governance policies within the same contexts, organizations can ensure adherence to important standards for security, compliance, and quality.