'A well-planned data governance framework covers strategic, tactical, and operational roles and responsibilities. It ensures data is trusted, well-documented, and easy to find within your organization, and that it's also

A well-planned data governance framework covers strategic, tactical, and operational roles and responsibilities. It ensures data is trust, well-documented, and easy to find within your organization, and that it's also kept secure, compliant, and confidential. Some of the most important advantages the framework provides include: A consistent view of and business glossary for data, while allowing appropriate flexibility for the needs of individual business units A plan that ensures data quality, accuracy, completeness, and consistency An advanced ability to understand the location of all data related to critical entities, making data assets discoverable, usable, and easier to connect with business outcomes. A "single version of the truth" that keeps critical business entities aligned across the enterprise. Well-defined methodologies and best practices for data assets and data management that can be applied across the organization Easily accessible data that's kept secure, compliant, and confidential according to the demands of legal or regulatory requirements The three pillars of data governance framework are as under: Governance including all data assets: Everything from dashboards and code to data science models is a data asset. The data governance framework should take into account all data assets, i.e., data and analytics governance. A practitioner-led, bottom-up approach: As the number of data users and consumers keeps rising, making a few people (data stewards or engineers) accountable for data governance isn't a sustainable approach. A decentralized, bottom-up data governance framework that makes every data creator responsible for data governance is the way forward. Governance practices embedded within daily workflows: Data governance has always been associated with compliance, control, and risk mitigation. However, it is a business function that can support strategic decision-making by ensuring that everyone has access to accurate, relevant, high quality, and trustworthy data. That's why it cannot be an afterthought. Instead, it should be embedded within the daily workflows of data practitioners.