Data governance is critical in managing an organization’s data assets. It is a long-term operation that distinguishes data governance from data management, highlighting that governance encompasses the policies and standards for data usage, while stewardship implements these practices within data governance frameworks, and provides a list of best practices to guide organizations in developing effective data governance programs.
Governance defines the way we manage, monitor, and measure different aspects of an organization. There are governance programs for managing information technology, governance programs for managing people and other tangible resources, and there is data governance.
Dictionaries define governance in the following ways:
n 1: the persons (or committees or departments, etc.) who make up a body for the purpose of administering something;
n 2: the act of governing; exercising authority;
There are many definitions of data governance. Some include:
- Data Governance is the process by which companies govern appropriate access to their critical data by measuring and mitigating operational and security risks associated with access to data. (Source: Crowdstrike)
- Data governance is the practice of making enterprise-wide decisions regarding an organization’s information holdings. (Source: Data Management Study, Federal Enterprise Architecture Program Management Office)
Therefore, data governance can be seen as building standards and requirements into the collection, identification, storage, and usage of the asset called “data”. It is a long-term process, and a successful, sustained governance program does not happen overnight. Data governance is a program, not a project, and all effective data governance initiatives are started, managed, and sustained with a permanent view. Just as an organization would not consider its accounting department to be temporary, any organization that wants to be lasting and successful should plan for a permanent data governance program, staffed by data governance professionals.
Data governance encompasses the people, corporate processes, and procedures that ensure that the organization can provide the right data to the right people at the right time for the right purpose in the right format with the right usage controls. Doing this requires that data governance be aligned with metadata management, data quality management, data/information security, data integration, and analytics – all at the enterprise level.
What is Data Governance?
Definition and Importance
Data governance is the set of processes, policies, and standards that ensure the quality, security, and integrity of an organization’s data assets. It is a critical aspect of modern businesses, as it enables organizations to make informed decisions, improve operational efficiency, and reduce risks. Effective data governance is essential for organizations to maintain trust with their customers, partners, and stakeholders.
Data governance is important because it helps organizations to:
- Improve data quality and accuracy
- Ensure data security and compliance with regulatory requirements
- Enhance decision-making processes
- Reduce risks and costs associated with poor data management
- Improve operational efficiency and productivity
By implementing a robust data governance program, organizations can ensure that their data assets are managed effectively, leading to better business outcomes and a competitive edge in the market.
Data Governance Framework
Components and Principles
A data governance framework is a structured approach to managing an organization’s data assets. It consists of several components, including:
- Data governance policies and procedures: Establishing clear guidelines for data management.
- Data governance roles and responsibilities: Defining who is responsible for various aspects of data governance.
- Data quality and security standards: Ensuring data is accurate, complete, and secure.
- Data architecture and infrastructure: Designing the technical foundation for data management.
- Data governance tools and technologies: Utilizing software and tools to support data governance activities.
The key principles of a data governance framework include:
- Accountability: Clear roles and responsibilities for data management.
- Transparency: Open communication and visibility into data management processes.
- Consistency: Standardized processes and policies for data management.
- Integrity: Ensuring data accuracy, completeness, and reliability.
- Security: Protecting sensitive data from unauthorized access and breaches.
By adhering to these principles, organizations can create a strong governance framework that supports effective data management and ensures the integrity and security of their data assets.
Data Governance vs. Data Management: Impact on Data Quality
There can be confusion between the terms data governance with data management. Data management is the collection of components that allow an organization to manage data and information as assets. Sometimes, “data management” is called “enterprise data management,” or “enterprise information management” – but the terms all mean the same thing. Data management has several parts, and data governance is one of them. Data Management and Data Governance are not synonymous – and many challenges and issues can arise when people confuse one term with the other.
A well-defined data governance strategy is essential for establishing a framework for managing data effectively within an organization.
Data governance runs horizontally across the entire enterprise; it is an enterprise function. Data is everywhere, and access to data should not be controlled or measured within a business unit; data should be able to be shared across the enterprise, except where security prevents that access.
The companion function to data governance is data stewardship. Data stewardship is the role that implements the policies, standards, practices, and procedures that are developed by the data governance professionals. Data stewardship works with data governance to ensure that data is managed according to proven practices and in accordance with the organization’s requirements, since most data stewards are subject matter experts and not data governance professionals.
The alignment of data governance with metadata management should not be optional. Effective data governance is a process by which organizations control the definition, usage, access, and security of the information they own and manage; much of that information is contained in the metadata that explains the raw data. So, data governance must be connected to metadata, with its registries, taxonomies, and ontologies, and repositories of the context that defines the content.
Key Differences
Data governance is often confused with data management, but they are distinct concepts. Data management refers to the technical processes and tools used to manage data, while data governance refers to the policies, procedures, and standards that ensure the quality, security, and integrity of data assets.
Data governance is also distinct from data quality, which refers to the accuracy, completeness, and reliability of data. While data quality is an important aspect of data governance, it is not the only consideration.
In summary, data governance is a critical aspect of modern businesses that enables organizations to make informed decisions, improve operational efficiency, and reduce risks. A data governance framework consists of several components, including policies, procedures, roles, and responsibilities, and is guided by key principles such as accountability, transparency, consistency, integrity, and security.
Best Practices for Data Governance Framework
There are many things that can be called “Best Practices” for Data Governance, including effective data governance practices. The following list is not exhaustive or complete, but it is representative of many proven applications for data governance at successful organizations.
Adhering to core data governance principles is essential for establishing a robust governance framework.
- Clearly defined and communicated vision, objectives, processes, and metrics of the Data Governance Program
- Program Sponsorship
- Program Charter
- Program Scope
- Scope for Each Project
- Single, enterprise-level data governance effort
- Well-understood escalation/issue resolution process
- Well-defined change management process
- Well-defined, actionable roles and responsibilities for all data governance roles
- Data governance processes integrated with existing methodologies
- Use of a proven data governance methodology
- Business and technical data stewards and data owners assigned for ALL subject areas
- Functioning Data Governance Council
- Data governance training
- Enterprise standards library
- Participation in enterprise data architecture reviews
- Defined, approved, and published data standards
- Managed metadata environment established and sustained
- Communities of practice for data governance, data stewardship, information management, etc.
- Rewards for good data governance behavior
- Success metrics are defined and measured for data governance
A robust data governance strategy plays a crucial role in improving data quality, ensuring teams can easily access the data they need for informed business choices.
As each organization develops, implements, and sustains its data governance program, it will review the list of best practices and adapt it, ensuring strong data governance practices are followed. Do not neglect best practices, since they have become “best practices” through the struggles of those who have gone before you.
