You're applying for a data governance job. Do you know enough about data governance frameworks?

2 Data Governance Components

Data governance frameworks are composed of various components that determine the roles, responsibilities, rules, and metrics of data governance. The data governance strategy outlines the vision and direction of data governance, in line with the business strategy and objectives. It also delineates the scope, priorities, and expected outcomes of data governance initiatives. Data governance policies contain the high-level principles and guidelines that govern how data is created, acquired, stored, accessed, and used. They also specify the standards and best practices for data quality, security, and ethics. Data governance roles assign specific functions and tasks to different data stakeholders such as data owners, data stewards, data custodians, data users, and the data governance council. Additionally, they define the authority and accountability of each role. Data governance processes are the workflows and procedures that execute the data governance policies and roles. These include data lifecycle management, data quality management, data security management, data access management, and data issue resolution. Data governance tools are the technologies and systems that support the roles and processes. Examples include data catalogs, dictionaries, lineages, quality tools, security tools, and a governance dashboard.

3 Data Governance Maturity

Data governance maturity is the measure of how well an organization implements and sustains data governance. It reflects the level of awareness, adoption, and performance of data governance across the organization. Data governance maturity can be assessed using various models and frameworks, such as the Data Governance Institute's Data Governance Maturity Model, or the DAMA International's Data Management Maturity Model. These models typically have five or six levels of maturity, ranging from ad hoc or nonexistent to optimized or visionary. Data governance maturity can help you identify the strengths and weaknesses of your current data governance practices, and set realistic and achievable goals for improvement.

5 Data Governance Best Practices

To realize the goals of data governance, it's necessary to follow some best practices and tips. You should begin with a clear and convincing business case, which should be based on the business needs and pain points as well as the anticipated benefits from data governance. Additionally, it's important to define and communicate the data governance roles and responsibilities, so stakeholders understand their part in data governance. Establishing and enforcing data governance policies and standards is also essential for defining the rules and guidelines for data quality, security, and ethics. Leveraging the right tools and technologies to support data governance processes is also important for enabling discovery, documentation, analysis, and reporting of data. Finally, it's important to review and improve the data governance practices and performance based on feedback, lessons learned, and best practices from both internal and external sources.