An Introduction to Data Governance

An Introduction to Data Governance

Understand the root of efficient data management with our guide to data governance, exploring its principles, benefits, and differences between other key terms.

Table of Contents

          Data governance definition

          Data governance ensures an organization has efficient data integrity, security, and quality.

          It addresses questions such as:

          • Who is responsible for data-related decisions?
          • What are the policies and standards for data quality, security, and privacy?
          • How should businesses store, classify, access, and share data?
          • What are the processes for data governance, including stewardship and lifecycle management?

          These questions help form data governance frameworks—rules and guidelines for organizations to follow.

          With the frameworks, businesses can avoid poor-quality data, lack of regulatory compliance, and worthless insights. Data governance helps organizations realize the value of their data while managing information and mitigating threats.

          Data governance and data management: What is the difference?

          Data governance and data management are related but individual concepts. Although these concepts are interconnected and often work together, they focus on different aspects of data within an organization.

          Data governance focuses on establishing a framework for making decisions about data, defining roles and responsibilities, and enforcing policies and standards relating to data management.

          On the other hand, data management focuses on implementing the strategies defined by data governance. It covers the technical and operational aspects of data handling, concerned with how data is acquired, organized, stored, integrated, and used.

          Essentially, data governance provides the frameworks, while data management encompasses the practical execution of those rules. They work hand-in-hand to establish effective data use and decision-making and ensure data quality and compliance.

          Master data management and data governance: differences explained

          Master data management (MDM) is a subset of data governance. It focuses on the most essential parts of an organization’s data, known as master data.

          Depending on the business, master data could include information on customers, products, suppliers, employees, and other critical reference data which an organization shares across multiple systems and applications.

          While data governance focuses on managing and controlling data assets, MDM is concerned only with core business entities. It aims to establish a single, authoritative, and consistent version of master data across the entire enterprise.

          MDM usually focuses on:

          • Data standardization
          • Data integration
          • Data governance alignment
          • Data quality management
          • Data access and distribution

          Data governance extends beyond MDM, encompassing the broader framework and practices for managing data, including master data. It also addresses more aspects, such as data strategy, stewardship, policies, and compliance.

          Data stewardship vs. data governance

          Part of data governance involves defining the roles and responsibilities of data stewards. These individuals or teams are accountable for managing and overseeing specific data aspects. They act as data custodians, ensuring data quality, integrity, and proper usage within their domain.

          Critical aspects of data stewardship include:

          • Data quality management
          • Data documentation
          • Data policy enforcement
          • Data access and sharing

          Data governance provides the structure and guidelines in which data stewards operate, while data stewardship executes the operational aspects of this framework.

          Data governance frameworks explained

          Frameworks are essential for establishing effective data governance. Teams use data governance frameworks to implement overall data standards and processes. Corresponding resources and training ensure everyone understands the organization’s approach to data quality, security, and integrity.

          Data governance frameworks don’t need to be complicated. For best results, a structured and intentional method is encouraged. The frameworks for product analytics usually focus on three key areas (or pillars)—education, instrumentation, and maintenance.

          To explore the three pillars in more detail, check out our guide to implementing a successful data governance framework. Here, we’ll take you through how to build and establish a data governance program and what those key pillars should address.

          What are the eight data governance principles?

          Data governance principles help organizations come together to address data-related needs and conflicts. They are effectively a business's goals and objectives, used to create a guiding set of principles.

          Depending on the type of organization, you may establish tailored data governance principles that meet specific business needs. However, most companies will follow eight universal governance principles—the Data Governance Guiding Principles from The Data Governance Institute.

          These principles focus on the most common data governance objectives and guide effective practices.


          Data governance should ensure data accuracy, consistency, and reliability throughout its lifecycle. It should implement processes and controls to maintain data integrity, prevent data corruption, and address data quality issues promptly.


          Practices should foster transparency, ensuring processes, policies, and decisions are well-documented, communicated, and understood by stakeholders. They should provide visibility into data sources, flows, and usage.


          Any decisions, controls, and processes should undergo auditing to ensure they are traceable and accountable. Data governance requires comprehensive documentation to accompany these activities to meet compliance requirements.


          The frameworks should establish clear roles, responsibilities, and accountability for data management. It should define data ownership and assign stewards responsible for data quality, integrity, and proper usage.

          Checks and balances

          This principle establishes mechanisms that verify and balance the actions and decisions in data management processes. It should ensure no conflicts of interest or that anyone has unchecked process control.


          Data governance should establish and enforce consistent data definitions, formats, and standards across the organization. The framework ensures data is used and interpreted consistently across different systems and departments.

          Change management

          This framework establishes how and when data should change—especially important for master data and metadata. Data governance recognizes some data changes frequently, while other information should alter only after considerable review and impact analysis.

          Next steps in data governance

          With nearly all business departments using data, data governance is crucial. Frameworks, principles, and processes help organizations understand how to use and share information. Adopting a company-wide approach to data governance enables users to store, clean, update, and delete data per their policies.

          Data governance enables an organization to enjoy more effective and faster decision-making thanks to better-organized and managed data. Users can be more confident the data they use for analysis is reliable and accessible to them, streamlining insights and business actions.

          Learn more about Data Governance and sign-up with Amplitude.

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