Data governance is the planning, oversight, and control over management of data and the use of data and data-related resources , and the development and implementation of policies and decision rights over the use of data. It is the foundational component of an enterprise data management or enterprise information management program.
Data governance is not information technology (IT) governance. IT governance makes decisions about IT investments, the IT application portfolio, and the IT project portfolio. Data governance is focused exclusively on the management of data and information assets. Some enterprise issues can require the inclusion of data governance and IT governance professionals and practices (e.g., information security and privacy).
Data governance encompasses the people, corporate processes and procedures that ensure that organizations can deliver data value, data quality improvement, develop and maintain data and its metadata, and provide availability of the right data at the right time to the right people in the right format. Therefore, data governance can be seen as building standards and requirements for the collection, identification, storage, and usage of the asset called “data.” It is a long-term process; a successful, sustained data governance program does not happen overnight.
Fundamentally, all data governance programs share the same goals :
Enable effective and secure decision making concerning the management of data and information resources
Reduce operational friction caused by a lack of governance of data and process at all levels of an organization
Train management and staff to adopt and implement common approaches to data management
Build standards and processes that allow for common and operative management of data
Reduce the costs and increase the effectiveness of data management and usage
Ensure transparency of the process of data management and the governance of data
What is Data Governance?
Definition and importance of data governance
According to the Data Governance Institute , “Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” In essence, data governance is crucial for enterprise organizations due to the complexity and distribution of data assets. It ensures the quality, security, and accessibility of data, enabling organizations to make informed decisions and maintain a competitive business edge.
Effective data governance is essential for organizations to manage their data assets efficiently and effectively. It provides a robust framework for data management, ensuring that data is accurate, complete, and consistent across the organization. By implementing strong data governance practices, organizations can ensure that their data is secure and compliant with relevant regulations and standards. This not only helps in mitigating risks but also enhances the overall trustworthiness of the data, which is vital for strategic decision-making and operational efficiency.
Data Governance Benefits
Consistent and comprehensive management of knowledge through management of data and metadata (definitions, context of the data, etc.) in all areas of the organization.
Education, communication, and understanding of organizational objectives, especially those that concern the use of data and information, including the management and cataloging of an organization’s data assets to facilitate search and discovery.
Appropriate enterprise approach to data management by identifying all business areas that use information and capture their requirements.
Well-defined and executed processes for data capture, definition, and conflict resolution of data-oriented issues and challenges.
Providing an enterprise glossary and a data dictionary accessible to the entire organization to develop and maintain a common company vocabulary (metadata management).
Guidelines and criteria for transactional and enterprise definitions for data and their updates, sourced centrally and available across the organization.
Leveraging the value of existing data among teams through re-use of validated data.
Clear, practical, and actionable practices for the effective uses, care, and feeding of data to ensure proper training and continuation of knowledge transfer concerning data management.
Data Governance Frameworks
Overview of data governance frameworks and their components
A data governance framework is a structured approach to data governance, providing a set of principles, policies, and procedures for managing data assets. A typical data governance framework includes the following key components:
Data Governance Policies : These define the rules and guidelines for data management, including data quality, security, and accessibility. Policies ensure that data is handled consistently and responsibly across the organization.Data Governance Procedures : These outline the steps and processes for managing data, including data intake, storage, processing, and disposal. Procedures provide a clear roadmap for data handling, ensuring that all activities are performed in a controlled and standardized manner.Data Governance Roles and Responsibilities : These define the roles and responsibilities of individuals and teams involved in data governance, including data stewards, data owners, and data users. Clear role definitions help in establishing accountability and ensuring that data governance tasks are effectively managed.Data Governance Metrics and Monitoring : These provide a way to measure and monitor data governance effectiveness, including data quality, security, and compliance. Metrics and monitoring tools help organizations track their progress and identify areas for improvement.
Choosing the right framework for your organization
Choosing the right data governance framework for your organization depends on several factors, including the size and complexity of your organization, the type and volume of data you manage, and your specific data governance needs. Some popular data governance frameworks include:
COBIT (Control Objectives for Information and Related Technology) : A widely used framework for IT governance and management, including data governance. COBIT provides a comprehensive approach to managing and governing enterprise IT environments.ISO 27001 (Information Security Management System) : A framework for information security management, including data security and governance. ISO 27001 helps organizations establish, implement, maintain, and continually improve an information security management system.Data Governance Institute (DGI) Framework : A framework specifically designed for data governance, providing a structured approach to data management and governance. The DGI Framework offers detailed guidance on establishing and maintaining effective data governance practices.
When choosing a data governance framework, consider the following factors:
Alignment with Your Organization’s Goals and Objectives : Ensure the framework supports your strategic goals and can be integrated into your existing processes.Scalability and Flexibility : Choose a framework that can accommodate changing data governance needs and can scale as your organization grows.Ease of Implementation and Maintenance : Consider the resources required for implementation and ongoing maintenance. A framework that is easy to implement and maintain will be more sustainable in the long run.Cost and Resource Requirements : Evaluate the financial and human resources needed to adopt and sustain the framework.Industry Recognition and Adoption : Choose frameworks that are widely recognized and adopted within your industry, as they are likely to be more robust and reliable.
By carefully selecting a data governance framework that aligns with your organization’s needs, you can establish a solid data foundation that supports effective data management and governance.
Master Data Management in Data Governance
Master Data Management (MDM) is essential for creating a unified, accurate version of truth within an organization. MDM centralizes data assets, linking disparate sources and removing redundant data entries, which ensures data consistency across departments and systems. Key outcomes include:
Data Accuracy and Consistency : MDM practices govern data by establishing consistent standards for data entry and data lineage, ensuring trustworthy data across the organization’s systems.Enhanced Organizational Insight : With a centralized master data repository, business stakeholders gain transparent access to the organization’s data assets. This level of data integrity enables informed decision-making and supports strategic business goals.Support for Digital Transformation : An effective MDM strategy integrates seamlessly with digital tools, facilitating machine learning, data visualization, and analytics initiatives, thus positioning companies to innovate and adapt quickly.Improved Collaboration : MDM encourages data teams and different departments to collaborate effectively, sharing accurate and complete data for better outcomes, whether in data-driven business intelligence projects or personalized customer experience initiatives.Regulatory Compliance and Security : A robust MDM system includes access controls and security measures, allowing companies to comply with regulatory requirements for sensitive data, improving overall quality and data security standards.
Implementing master data management within a data-driven culture transforms how an organization manages, shares, and utilizes its data. This foundation supports scalable analytics, drives reduced costs through efficient data management and enables better decision-making across departments.
Data Foundation Benefits
A strong data foundation lets organizations leverage emerging technologies more effectively, as well as scale and adapt to evolving data needs. Optimized data storage is crucial for establishing architecture and infrastructure that supports scalability, performance, and security. This foundation enables streamlined processes and improved operational efficiency, allowing teams to work seamlessly with accurate, trusted data across departments. Key benefits include:
Enhanced Operational Efficiency : A robust data foundation optimizes workflows, improving data processing speeds and increasing accuracy, thus reducing redundant efforts and operational costs.Scalability and Adaptability : A solid data foundation accommodates increasing data volumes and complexity. It is essential as the business grows or integrates new data sources.Proactive Issue Detection : With efficient data integration and lifecycle management, organizations can detect and resolve data inconsistencies promptly, minimizing risks, and supporting proactive decision-making.Improved Reporting and Insights : A robust foundation allows organizations to quickly generate accurate reports, helping decision-makers rely on up-to-date information.Continuous Improvement : A well-maintained data foundation evolves over time, integrating new data governance tools and strategies to ensure ongoing relevance and effectiveness.
Data Governance Principles
Data Governance vs. Data Stewardship
Data governance provides the principles , processes and organizational definition to preserve and enhance the data asset. This function is performed by data management / data governance professionals.
Data stewardship enables the organization to make sound, consistent decisions using the knowledge of the business people about the data they use daily within the guidance of industry standards and best, proven practices for data management, supported by the guidance of data management and data governance professionals.
A data steward is a person who is responsible for implementing the policies and standards that are developed by the data governance organization. Usually a data steward is a business staff member with deep experience in a specific subject or functional area (customer data, product data, accounting data, etc…). Business data stewards work to improve the quality of data (content) and metadata (context) for the organization’s critical data, following the principles and standards identified by the data governance program. Data Governance and Data Stewardship are not synonymous; they are complementary.
Critical Success Factors for Successful Data Governance and Data Stewardship
Successful implementation of data governance and data stewardship at any organization is not a foregone conclusion. Following are some factors that are critical to the long-term success of any data governance program and the data stewardship function:
Strong and continuous executive management understanding, attention, involvement, and support for an enterprise data management and data governance initiative, including enforcement for compliance of data-centric policies, and methods for data management
Strong and continuous systems management understanding, attention, involvement, and support for alignment of data governance / enterprise information management with the rest of information systems and technology management
Data planning must be closely linked to organizational and information technology planning
Development and sustainment of a formal data governance program office, sufficiently staffed by experienced data management and data governance professionals
Planning for growth of the Data Governance program staff must be performed periodically and additional data governance professional specialists must be added to support program growth and sustainment
Data stewards’ placement in the organizational structure must be re-evaluated periodically, with growth in the number of stewards accepted and included in planning
Sufficient resources (people, time, tools and other technologies) are provided by the organization to adequately perform the data stewardship tasks
Sufficient authority level is provided to data stewards and their governance bodies to carry out their tasks
Data policies and standards are developed, maintained and adhered to consistently by the organization
Effective training programs must be established to educate not only the data stewards, but also everyone with whom they interface
Organizing the Data Governance Function
Figure 1: Sample Basic Data Governance Organizational Structure Every data governance program needs an organizational structure that is aligned with the structure of its enterprise; one that fits its organization’s culture as well as one that serves the industry standards and proven practices of data governance. Doing all that may sound like an impossible task, but it is not, since there are some common data governance organizational formats that have been shown to be effective in organizations of every size, industry, and level of maturity .
EIM and Data Governance Teams
The development of an Enterprise Information Management Department to manage the operations of the EIM program is the cornerstone for all successful enterprise data / information management initiatives. This team is the foundational unit around which all activities for enterprise information management are coordinated and managed. Forming such a unit to manage the operations of EIM and providing oversight to certain components is considered essential to the success of enterprise information management (EIM) by all the experts and thought leaders in the field of information management.
In addition to the EIM team, organizations should create a dedicated Data Governance unit as part of the EIM Department. The data governance team would start with the creation of crucial positions specifically dedicated to enterprise data governance: a full time Data Governance Manager and at least two (2) experienced full time Data Governance Specialists. Additional Data Governance Specialists will be needed to support the enterprise program once it grows to encompass more than half the subject areas in the organization.
Business Data Stewards
It is important to note that not all data in any organization is subjected to data governance or to data quality management, only the data that is considered to be critical / essential / important to the organization’s operations or decision making capabilities. In most organizations, the mission-critical subset of data is approximately 20%-30% of the total data stored. However, the governed data must be managed with consistent methods so that the enterprise can apply standards and metrics to that management for evaluating the program’s success. The business data stewards are subject matter experts in the data for a particular business area (or subset of an area). They provide the knowledge of the data, its business meaning / definition / usage patterns / etc., enabling the data governance professionals to establish the policies and standards for governing the critical data.
A data steward has or shares responsibility for a particular domain of the enterprise, and knows who does what with data within that domain, i.e., he or she has local knowledge from the business perspective (not technical).
A data steward coordinates the promotion of good data governance practices within their domain.
A data steward assists in getting resolution to cross-organizational data-related issues.
Most data stewards are not full-time positions; they are designated as stewards in the course of their regular jobs (senior claims analyst, lead financial services representative, senior compliance officer, lead underwriter, etc…) and have received specific training as a business data steward.
Policies and Standards in Data Governance
In most organizations, the Data Governance function is responsible for establishing and maintaining the policies and standards that apply to all data used by the enterprise. These policies and standards enable the company to define the quality of its data, and allow the organization to perform its operations and make decisions secure that its data and information are accurate, complete, valid, consistent, etc.…
Data Policies are the general business rules and processes that an enterprise uses to provide guidance for the management of data and information. Policies might include directives on conformance of data to business rules, statement providing guidance for protection of privileged or confidential data, or documents defining enterprise data management functions, and other directions for how the organization should manage its data.
Examples:
Data Classification Policy
Data Sharing Policies (internal and external sources)
Data Governance Policies
Data and Information Security Policies (compliance, regulatory requirements, internal corporate requirements, etc.)
Data and Information Privacy Policies (compliance, regulatory requirements, internal corporate requirements, etc.)
Data Standards are defined as the precise criteria, specifications, and rules for the definition, creation, storage, and usage of data within an enterprise. Data standards include basic context items for data objects such as naming conventions, character length, value ranges and classification types. Data Stewardship teams may dictate specific quality measures, retention rules, and backup frequency separately from enterprise data standards, for use within a particular domain.
Examples:
Data Format Standards, such as Name and Address
National / International and Industry Data Standards (e.g.,HL7, ISO)
Data Quality Standards, based on the data quality dimensions (accuracy, completeness, validity, consistency, relevance, timeliness, etc.)
Meta Data Standards (ISO 11179 as foundation)
Data Model Standards
Policies and standards should be written using templates, so that each policy and each standard follows a similar format. Doing so makes it easier for writers to include the relevant material (and refrain from including unnecessary details), and to allow all stakeholders to understand and implement the policies and standards consistently.
Challenges in Data Governance
Every organization faces a variety of challenges in designing and implementing a data governance program. Most enterprises report the following similar challenges that can affect successful implementation of a data governance initiative.
Lack of management support for data governance initiative: The most common challenge faced by organizations attempting to develop a data governance program is the lack of interest or support from executives and managers. This reluctance stems, usually, from the organization’s historical neglect of data’s value as an asset, and executives’ failure to recognize the need to implement a data governance program that will establish policies, standards and controls that manage data throughout the organization to provide business value.
Lack of enterprise data strategy that recognizes value of data and its management as an asset: Most organizations have a business strategy that outlines the goals and objectives the enterprise plans to achieve. However, very few organizations have a companion data strategy, a document that takes the goals of the business strategy and applies them to the data and information assets. Without a data strategy, organizations cannot define how their data and information serve them as resources, and without this knowledge, the value of managing that data through data governance is lost, making it difficult to justify a data governance initiative.
Lack of understanding of purpose of data governance: Many organizations misconstrue the term “data governance” with “data management,” or with “data controls.” As Gwen Thomas, in an interview with Tech Target stated, ” they confuse little g governance” – policies and controls that are embedded in processes, systems, data stores and data flows to ensure that data meets user expectations – and Big G Governance – the highly political negotiations, decision making and policy setting that informs and supports little g governance.”
Lack of data governance framework or methodology: Successful data governance programs start with a framework or methodology for data governance that is based on industry standards and best practices, with an approach that has been proven through many implementations. Failed data governance programs are implemented without the use of a proven framework or methodology and rely on anecdotal approaches to data governance rather than empirical, evidence-based methods and processes.
Lack of formal Data Governance Office or team for program management: Research by several organizations, including EWSolutions , indicates a higher success rate in data governance implementation for organizations that have a formal Data Governance Program Office or team than enterprises that do not include this staff. A small, dedicated, experienced team of data management and data governance professionals can provide a variety of services. They can serve as the knowledge resource for data governance, manage the variety of projects that arise as part of the initiative, educate the organization in data governance best practices, customize a framework or methodology to fit the organization, and serve as champions for sustaining data governance throughout the organization.
Lack of sustained funding for data governance effort at enterprise level: Many organizations continue to fund data governance at the project level, refusing to accept the enterprise nature of the discipline. As a result, the sustainable, organization-wide benefits of data governance are never realized, and the program withers and dies slowly. Organizations that fund data governance at the enterprise level reap the cross-department, company-wide benefits of data rationalization and usage, improved data management and policies, with common standards and controls, along with improved data quality and enhanced metadata management.
Conclusion
Data Governance is the cornerstone function in an enterprise data / information management program, without it no organization can manage its data effectively. Data Governance combines management of metadata and the development of policies and standards by which data quality is measured. It includes the development and implementation of processes for managing organizational use of data, making data governance one of the most important activities an organization can execute to ensure effective use of its data and information resources.
