The Goals of the Data Governance Framework
You can effectively manage things when you name and define them explicitly. The way you define your respective programs will influence your ability to effectively manage it and keep all your participants in sync, focus and strive towards your goal.
Definition of Data Governance – Data Governance is all about the process and procedure enterprises use to protect, utilize and manage their data. Defining what data matters and means to an enterprise is vital. Data Governance is a system of accountabilities and decision making for data and information related processes that are executed as per the agreed-upon models that defines and describes what actions can be taken with what type of information and when and in what type of circumstances by using what type of methods. Data Governance encompasses processes, technology and people that are required to protect and manage data assets.
What is Data Governance Framework – Data Governance Framework helps to create a single set of processes and rules for collecting, storing and using data. The ideal framework makes it simple, helps to scale and streamline governance processes that enables you to democratize data, maintain compliance, promotes collaboration,supports scalability and is ready to deal with huge tons of data no matter how rapidly the volumes of your data may grow. With a data governance framework you can ensure the rules, policies and definitions are applied to all your data across your entire enterprise. Right from business or management leaders to data developers and data stewards you can provide trusted data.
The Goals of Enterprise Data Governance Framework
- Improve external and internal communication
- Implement compliance requirements accordingly
- Establish internal rules for data use
- Minimize risks
- Reduce costs and grow the value of data
- Through optimization and risk management you can focus to ensure effective compliance management.
By Implementing successful data governance framework in your enterprise the following knowledge areas can be well focused:
Metadata – Collecting, integrating, categorizing, maintaining, managing, controlling and delivering metadata
Data Quality – To define, monitor and maintain data integration and to improve data quality.
Data Warehousing and Business Intelligence(BI) – It is all about managing analytical data and its processing to enable the access to support data for analysis and reporting.
Data Design and Modeling– It includes building, design, testing, analysis and maintenance of data
Data Operations and Storage – It focuses on the physical data assets that are structured for storage management and deployment
Data Security – Ensuring appropriate access, confidentiality and privacy
Data Interoperability and Integration – It includes extraction, acquisition, movement, transformation, replication, federation, virtualization, delivery and operational support
Content & Documents – Storing, indexing, protecting providing access to the data that is found in the unstructured sources and making this data readily available for interoperability and integration with the structured data.
Reference Data Management and Master Data Management – Managing shared data to reduce redundancy and ensure better quality through use of data values and standardized definition.