The Data Governance Framework also known as the Data Management Framework refers to a set of standards, guidelines, protocols, processes and rules that enable organisations to effectively manage their data. It basically serves as a foundation of a data governance program which provides a clear visualization of how to ensure the quality, integrity, security, discoverability, accessibility and usability of their data assets. Establishing a well-defined framework is important for Data Governance Initiatives.

Characteristics of the Data Governance Framework

• Gives priority areas like data quality, security and compliance;
• It clarifies roles, responsibilities, policies and standards for effective data management; and
• It enhances collaboration among stakeholders from various departments to achieve data governance goals.

Pillars of any Data Governance Framework

The Pillars of the Data Governance Framework can in other words be seen as its components since they all reflect the essence of governance for the modern data stack, making data flow traceable and data related processes transparent. This also enhances understandability of the one’s operations hence improving performance and goal achievement.

The Pillars include:

1. Governance entailing all data assets. For this particular pillar, you find that data assets refer to most things running from dashboards to code of data science models. The Data Governance Framework should always account for all data assets, that is, data and analytics governance.
2. A practitioner-led bottom-up approach. On this pillar we get to understand that the more the number of data users and consumers escalates up, the fewer the people (data stewards or engineers) accountable for data governance, which in return isn’t a sustainable approach.
3. Governance practices lodged with daily workflows. Data Governance has always been associated with compliance, control and risk management. However, we find that this remains a business function that can support strategic decision making by ensuring that everyone has access to accurate, relevant, high quality and trustworthy data. This gives emphasis as to why it should be embedded within the daily workflows of data practitioners.

Data Governance Framework Examples

There exist two categories of the framework:

1. The Traditional Framework which entails:

• The structured Approach;
• Clear roles and policies;
• Focus on consistency, reliability and compliance;
• Encompassing the view of Data Governance.

2. The Modern Framework entailing:

• Adaptation to Artificial Intelligence and new technology; and
• Agile and Flexibility in data management;
• Emphasizes quick adjustments and collaboration to align with strategic objectives;

Advantages of the Data Governance Framework

• It helps ensure that transparency becomes key on how one manages data;
• It gives a greater visibility of how to synergize your data governance efforts;
• One has the plan to control and have visibility of the entire data estate;
• A framework sets a strong foundation for regulatory compliance practices;
• Monitoring your data consumption is efficient since it follows the blueprint which in return helps prevent data misuse;
• It outlines an agreement on common data definitions;
• There is increased trust in data for analytics and decision making;
• It makes it easier to locate data making all data more available;
• It also gives improved data accuracy, completeness and consistency; and
• Enhances better acquiescence with data privacy laws and other governance regulations such as the EU General Data Protection Regulation (GDPR) and the US Health Insurance Portability and Accountability Act (HIPAA).

In conclusion, the Data Governance Framework is a vital strategical step in any organization. Of the areas it covers are accountability, standardized rules and regulations, data stewardship, Data Quality Standards and Transparency.