Clear definitions and detailed steps can enable any organization to demystify the concepts in data governance and data stewardship
With so much written and so much debate around data governance it would be normal to wonder why this discipline is still a puzzle to many people. With a wide variety of definitions juggling for a position, the whole topic can appear to be organized confusion.
Therefore, it is essential to have clear narratives and steps that are consumable to business audiences in to support successful adoption of this essential function.
What is “Governance”? What is “Data Governance”?
The word “governance” means to exercise a directing or restraining influence over; guide; to hold in check; control something with agreed processes.
Data Governance can be defined as a discipline of formalized oversight of accountability with established controls that enable an organization to manage, optimize, protect, and leverage its data asset for multiple needs.
At the heart of data governance is a “community of people” with assigned roles and responsibilities developed to achieve the stated purpose. Successful data governance programs have a formal data governance program team, responsible for the development of the policies and processes and the identification of standards for data management. The business role responsible for implementing the data governance processes is called “data stewardship”. Data governance defines what is managed, data stewardship drives how the policies, practices, processes are implemented across the organization.
Why Governance Around Data?
Governance around data is established to address, mediate and resolve questions and issues around enterprise data (e.g., ownership, metadata, data sourcing, data lineage, data quality standards) that affect the integrity of the enterprise data.
The primary goal of data governance is to ensure the highest degree of data integrity across the enterprise’s data assets. Achieving this goal will help an organization fully harness the potential in their data assets. Using enterprise data governance, the organization will create a trusted, business-ready data environment to help optimize and resolve all current and future competing data demands. The organization will be positioned for greater success by lowering the operational cost of data corrections and cleansing, infusing quality around the enterprise data value chain.
Role of Tools and Technology
Data governance and data stewardship are supported by tools and technologies to accelerate the data governance journey. They do not replace the data governance professional team, or the community of data stewards assigned to responsibilities for the enterprise’s data assets. It is important to equip and empower the data governance team and the data stewardship teams with the right tools and technology to help accelerate the journey. However, engagement of tools and technology must happen after the organization has successfully defined “what” with is to be governed and implemented the teams. Tools and technologies should not drive the data governance program.
Steps to Successful Data Governance Adoption
The following activities are essential for every organization’s success with data governance:
- Engage and secure active sponsorship from senior executives and provide the right level of expectations and support needed from them to achieve success in the organization’s data governance program.
- Define the Data Governance Framework, identifying competing data needs, and resolve the scope of data governance.
- Assess the current scope of any data governance and data stewardship efforts using an established assessment method.
- Develop standards, policies, and procedures for data governance adoption.
- Establish oversight committees for data governance (executive council, data governance committee, etc.).
- Identify subject areas / data domains and start building communities of data stewardship around enterprise data based on these subject areas.
- Identify roles and responsibilities to help realize enterprise data governance expectations.
- Hire, train, and support key resources into their roles and responsibilities
- Activate data stewardship to fulfill the defined data governance mandate:
- Ownership has been established by subject area / data domain in each community to ensure ethical use of data and ensure data is fit for use, managed to the defined standard.
- Both technical and business metadata have been defined to help articulate the use and purpose of data. These have been documented and enriched in appropriate dictionaries and business glossaries to establish a common language across the enterprise.
- Data lineage has been mapped to show how and where data originates, is transformed, and is consumed. Successful data mapping provides total transparency for enterprise data.
- Data quality rules and thresholds have been established to set parameters for data control, data access and ethical use of data.
- Continuous monitoring of enterprise data quality rules is implemented.
- Data quality issues management process has been defined, is being tracked and is monitored for faster resolutions.
- A change management process has been created and implemented to manage continuing changes around governed data.
Conclusion
Following these definitions, guidelines, and steps will set any organization on the way to a successful cultural transformation in a governed environment for data and information.
