What are the key components and objectives of a data governance framework?

1 Data Governance Components

A data governance framework encompasses several components that cover the different aspects of data management. It should include a data governance strategy that outlines the vision, goals, scope, and principles of data governance, as well as the business case and benefits of implementation. Additionally, it should define roles and responsibilities of the data governance team, data owners, data stewards, and data consumers, as well as communication and collaboration mechanisms. Furthermore, it should outline workflows and procedures for data lifecycle management, such as acquisition, integration, transformation, quality control, security, distribution and archiving. Moreover, it should define rules and guidelines for data naming, definition, classification, modeling, documentation and metadata management. Finally, it should identify measures and indicators for assessing the performance and outcomes of data governance such as quality, availability, usability and value.

2 Data Governance Objectives

A data governance framework has several objectives, such as ensuring data quality through validation, cleansing, standardization, and enrichment. It also ensures data security by applying encryption, masking, anonymization, and access control techniques. Additionally, it ensures data compliance with legal, regulatory, and ethical requirements through retention, auditing, and reporting techniques. Moreover, it ensures data usability by cataloging, documenting, and managing metadata. Lastly, it ensures data value by leveraging data analysis, visualization, and storytelling techniques to support the organization's strategic goals.

3 Dimensional Modeling and Data Governance

Dimensional modeling is a technique for designing data warehouses and business intelligence systems that organizes data into facts and dimensions. Facts are numerical measures of business events or transactions, while dimensions are descriptive attributes that provide context and meaning to the facts. For example, a sales fact table may contain measures such as quantity, price, and revenue, while a customer dimension table may contain attributes such as name, address, and segment.

Dimensional modeling supports data governance in numerous ways. It simplifies data integration by applying common data definitions, classifications, and transformations. Additionally, it improves data quality by validating, cleansing, and standardizing the data. Moreover, it enhances data security by applying encryption, masking, and access control techniques. It also facilitates data compliance by implementing retention, auditing, and reporting techniques. Furthermore, it increases data usability through cataloging, documentation, and metadata management techniques. Finally, it maximizes data value by analyzing, visualizing, and storytelling techniques that support the business needs of end users.