Data governance is fundamentally about managing data’s availability, accuracy, and relationships within an organization, emphasizing that it is a business responsibility rather than solely an IT function. Successful data governance initiatives require a clear vision and a well-defined data governance policy communicated across the enterprise, ensuring that all stakeholders understand and support the governance framework to achieve a unified approach to data management.
Introduction to Data Governance: A Business Responsibility
At the highest level, is concerned with the management of data – its availability, currency, usefulness, accuracy and relationships with other enterprise data. Governance of data is not an IT function, although many technical products and tools are used to administer governance. Data governance is a business responsibility, shared with IT but “owned” by the business entity and instituted across the enterprise. Like any other enterprise effort, successful data governance involves people, processes, tools, standards and activities that are managed at both strategic and operational levels. And, like any other successful enterprise initiative, data governance starts with a vision, which is communicated and sustained by the enterprise through well-defined data governance policies.
Webster defines “vision” as: a thought, concept, or object formed by the imagination; unusual discernment or foresight; the act or power of seeing. With this definition as a basis, we can say that a “vision” for data governance would include the articulation of what the organization thinks that concept should entail for them, what they “see” as the state to be achieved by the act of governing data. To achieve that end-state, it is imperative that the organization communicates a compelling vision for change, setting achievable targets and contributes sufficient enterprise resources to develop the vision / concept. To be successful, this vision must be commonly understood and supported by the senior management and business sponsors of the data governance initiatives.
Challenges of a Fragmented Approach to Data Governance Framework
Many organizations launch data governance efforts as part of a business-unit or division-level project, and do not acknowledge the need for an enterprise approach to managing the common asset, data. This project-oriented approach to overarching programs such as data governance can cause the development of multiple initiatives, each with its own set of missions, standards, procedures, policies and activities, creating a “Tower of Babel” and not a unified view of data governance. When the organization finally recognizes the need for an enterprise view of data and the need for enterprise data governance, all of these disparate data governance efforts must be dismantled and replaced, causing confusion and conflict within the affected areas due to unclear data governance roles.
Establishing a Common Vision for Data Governance
The first step in every successful data governance effort is the establishment of a common vision and mission for data and its governance across the enterprise. This vision should be supported by clear data governance metrics to track progress and success. The vision articulates the state the organization wishes to achieve with data, and how data governance will foster reaching that state. Through the skills of a specialist in data management and data governance and using the techniques of facilitation, the senior business team develops the enterprise’s vision for data and its governance. All subsequent activities of any data governance effort should be formed by this vision.
Key Objectives of Data Governance: Data Quality Management, Usability, and Compliance
A well-defined data governance vision centers on achieving key objectives like data quality, availability, usability, and compliance. High-quality data ensures that all stakeholders, from data owners to business users, have access to accurate and reliable information, supported by effective data governance tools. An effective data governance framework can enhance organizational productivity by streamlining data governance processes that allow for efficient master data management (MDM) and adherence to data security protocols. By mitigating the risks associated with poor data quality, governance reduces compliance issues, ensuring that data privacy and security standards are upheld. Additionally, with common data definitions and clearly defined ownership responsibilities, organizations can foster a shared understanding of how data is managed, supporting better decision-making across the enterprise. A data governance committee led by a chief data officer (CDO) plays a vital role in shaping and enforcing these best practices, ensuring that data projects are aligned with business objectives while safeguarding the integrity and availability of information assets.
Understanding the Data Governance Framework
A data governance framework is a structured approach to managing data across an organization. It provides a set of guidelines, policies, and procedures for ensuring the quality, security, and availability of data. A well-defined data governance framework is essential for organizations to make informed decisions, improve data quality, and reduce data-related risks.
Components of a data governance framework
A data governance framework typically consists of several components, including:
- Data governance policies: These are high-level statements that define the organization’s approach to data governance, including data quality, security, and availability.
- Data governance processes: These are the procedures and workflows that support data governance, including data creation, storage, access, and usage.
- Data governance roles and responsibilities: These define the roles and responsibilities of individuals and teams involved in data governance, including data owners, data stewards, and data analysts.
- Data governance metrics and monitoring: These are the metrics and monitoring mechanisms used to track data quality, security, and availability, and to identify areas for improvement.
Structured approach to managing data
A data governance framework provides a structured approach to managing data, which includes:
- Data classification: Classifying data into different categories based on its sensitivity, criticality, and business value.
- Data quality management: Ensuring that data is accurate, complete, and consistent across the organization.
- Data security: Implementing measures to protect data from unauthorized access, theft, or damage.
- Data availability: Ensuring that data is available to authorized users when needed.
Developing a Collaborative Visioning Process
Visioning offers the widest possible participation for developing a long-range plan, especially in enterprise-oriented areas such as data governance. It is democratic in its search for disparate opinions from all stakeholders and directly involves a cross-section of constituents from the enterprise. Developing a vision helps avoid piecemeal and reactionary approaches to addressing problems. It accounts for the relationship between issues, and how one problem’s solution may generate other problems or have an impact on another area of the enterprise. Developing a vision at the enterprise level allows the organization to create a holistic approach to setting goals that will enable it to realize the vision, supported by clear data governance standards.
Best Practices for Implementing a Data Governance Strategy
Implementing a data governance strategy requires careful planning, execution, and ongoing monitoring. Here are some best practices to consider:
Clear roles and responsibilities
- Define data ownership: Clearly define data ownership and accountability across the organization.
- Establish data governance roles: Establish data governance roles, including data stewards, data analysts, and data quality managers.
- Define responsibilities: Define the responsibilities of each role, including data creation, storage, access, and usage.
- Establish a data governance committee: Establish a data governance committee to oversee data governance initiatives and ensure alignment with business objectives.
By following these best practices, organizations can implement an effective data governance strategy that supports business objectives, improves data quality, and reduces data-related risks.
Bridging the Gap Between Vision and Implementation
To bridge the gap between vision and implementation, it is crucial to outline the specific objectives of data governance. These objectives include ensuring data quality, availability, usability, and compliance. A robust data governance program addresses these by creating clear frameworks for data management practices and processes, ensuring that high-quality data is consistently available for business use. Through data governance, organizations can foster compliance with regulatory requirements and enhance decision-making by ensuring that data is accurate and efficiently managed. Moreover, effective governance promotes the availability and integrity of data across the organization, empowering stakeholders with access to reliable and actionable information. As data stewards and business leaders collaborate, they ensure that the organization meets its business objectives while maintaining the security and privacy of its data assets.
Creating a vision is a specific step in the planning process, and should not be overlooked or shortened. Scheduling the visioning step should incorporate sufficient time for framing issues, eliciting comments through surveys or meetings, recording statements from participants, and integrating them into draft and final documents. Preparation for visioning is crucial and touches on many complex issues. Advance work is essential to give time for staff to prepare the data governance vision meeting purpose and agendas, questionnaires, and methods of presentation and follow-up. The visioning program should be carefully scheduled to maximize senior management input and response time and sufficient time for revisions to draft vision statements.
The Path to a Successful Data Governance Program
To ensure that all data governance efforts are shaped by the organization’s vision, communication of this vision by the data governance committee is essential. Every person responsible for creating, managing or using any data must understand and support the governance vision. Data governance activities should be part of all projects, and measurement of a project’s success should include how well the project achieved the organization’s governance vision as well as whether the project’s timelines were met. Periodic refinement of the vision is an important step, so that the enterprise continues to follow the best data governance path as conditions change and new situations develop.
Achieving Success with a Clear Vision for Data Governance
In the final analysis, the best data governance programs are those that begin with a clear and achievable vision for data governance, one that is uniformly communicated to the organization, refined as necessary and incorporated in the enterprise’s data governance approach.
