What’s the difference between Data Governance and Data Management?

Data governance and data management are two essential components of effective data strategy, but they serve distinct purposes and focus on different aspects of data management within an organization. Understanding the difference between data governance and data management is crucial for implementing comprehensive data initiatives that support organizational goals. Here's a breakdown of the key distinctions between these two concepts:

Definition

• Data Governance: Data governance refers to the overarching framework, policies, processes, and controls that govern the collection, storage, use, and distribution of data across an organization. It encompasses the strategic direction, decision-making authority, and accountability structures related to data management.
• Data Management: Data management encompasses the operational activities and practices involved in managing data throughout its lifecycle. This includes tasks such as data collection, storage, organization, integration, quality assurance, analysis, and dissemination. Data management focuses on ensuring data is accurate, accessible, secure, and compliant with organizational standards.

Scope

• Data Governance: Data governance addresses the strategic and organizational aspects of data management, including defining data policies, establishing data ownership, setting data quality standards, and ensuring regulatory compliance. It governs how data is used, shared, and protected to support business objectives and mitigate risks.
• Data Management: Data management focuses on the day-to-day operational activities involved in managing data assets. It includes tasks related to data acquisition, storage, processing, analysis, and reporting. Data management activities are executed by data professionals and technology systems to ensure data is managed efficiently and effectively.

Objectives

• Data Governance: The primary objectives of data governance are to establish accountability, transparency, and trust in the organization's data assets. It aims to ensure that data is used responsibly, ethically, and in alignment with organizational goals. Data governance seeks to improve data quality, integrity, and reliability while mitigating data-related risks and ensuring regulatory compliance.
• Data Management: The main objectives of data management are to optimize data processes, enhance data usability, and maximize the value of data assets. It focuses on improving data accuracy, consistency, and completeness to support business decision-making, operational efficiency, and innovation. Data management aims to streamline data workflows, reduce data redundancy, and improve data accessibility for users.

Roles and Responsibilities

• Data Governance: Data governance involves defining roles and responsibilities for data stewards, data owners, and other stakeholders who are accountable for managing and governing data assets. Data governance roles typically include establishing policies, defining data standards, resolving data-related issues, and monitoring compliance with data policies.
• Data Management: Data management involves executing specific tasks and activities related to data collection, storage, processing, and analysis. Data management roles may include data engineers, data analysts, database administrators, and data scientists who are responsible for implementing data management processes, maintaining data infrastructure, and generating insights from data.

While data governance provides the strategic framework and governance structure for managing data assets, data management focuses on the operational activities and practices involved in executing data-related tasks.

Both data governance and data management are essential components of a comprehensive data strategy, working together to ensure that data is managed effectively, securely, and in alignment with organizational objectives.

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Series:

1. What is Data Governance?
2. What goes into Data Governance?
3. What are the business benefits of Data Governance?
4. Is Data Governance a program or a project?
5. How do I help business managers understand the importance of a Data Governance initiative?
6. How do you implement Data Governance?
7. How do you measure Data Governance success?
8. Why does Data Governance fail?
9. What’s the difference between Data Governance and Data Management?
10. What is a Data Owner?
11. What is a Data Steward?
12. What is the difference between a Data Owner and a Data Steward?
13. What is Data Quality and how is it measured?
14. What is Data Maturity and how do you measure it?
15. What is Data Lineage?
16. What is a Business Glossary?
17. What is the difference between a Business Glossary and a Data Dictionary?
18. How do I build a Business Glossary?
19. How do I prioritize Critical Data Elements?
20. When should I buy a tool to help govern my data?