Implementing a successful data governance framework.
The data governance framework acts as a guide that underpins the program. The framework consists of rules, policies, technologies, processes and organizational structures that form a part of the program. The framework will also detail the mission statement and goals, as well as how the success of the program will be measured. Since the framework is intended to give clarity and direction to the business, it is usually shared internally so that all stakeholders can understand the scope, goals and plans for the program.
Technology plays a key role in data governance and the technologies used in the program should be made clear in the framework. Software implemented typically focuses on automating repetitive tasks or providing a higher degree of organization that helps support the programs. Examples include workflow tools, data integration tools, cataloging tools, data lineage tools and analytics tools.
IMPLEMENTING DATA GOVERNANCE IN ENTERPRISES
- Identify those responsible for overseeing siloed data within the business. This may be individual team leaders or individuals in charge of databases and managing data within applications (for example, a SharePoint lead).
- The CDO and data governance manager structures the programs and engages with the individuals from stage one. The data governance team is formed, and data stewards are established.
- Now that the structure of the program has been finalized, the heavy lifting begins. Policies are created and rules are made. Key decisions about the handling of data and who is authorized to access and handle certain data are decided. Ongoing auditing policies are created to ensure that teams within the organization are complying with internal policies as well as external regulation.
This is a very high-level overview of a data governance program and its implementation. Other elements need to happen to have an effective governance initiative. For example, data must be mapped and classified. A business glossary must be made to ensure that teams are on the same page when it comes to categorizing data. Data should also be cataloged and indexed for easy retrieval and increased oversight.
Access the full guide
Data governance is a complex topic that spans a wide range of industries, departments and stakeholders. In order to fully understand what it means, and how to practice good data governance, we have compiled a detailed and comprehensive guide answering the following questions:
● What is data governance?
● Why is data governance important?
● What are the aims and benefits of data governance?
● Who oversees data governance in an organization?
● What does a data governance framework look like?
● How can enterprises implement data governance?
● What are some data governance best practices?
● What can NodeGraph do to help?