What Is Data Governance?
Data governance is a structured approach to information management that promotes data security, quality, access and other core pillars. Proper control of institutional information and implementation of data governance methods can notably impact performance.
Data Governance in Simple Terms
Data governance involves certifying the safety, accessibility, usability, integrity and precision of your organization’s data. It is a holistic data management outlook that engages data. Governance aims to institute accurate policies, systems, technologies and actions to support the governance pillars throughout the evolution of your data from import to permanent deletion.
What Are the Pillars of Data Governance?
Data governance has several essential pillars for successful implementation in your organization. Although the extent of these critical areas can vary for different institutions and industries, they are all crucial for excellent data management. Eight fundamental pillars of data governance include:
- Security.
- Literacy.
- Integration.
- Life cycle management.
- Metadata control.
- Stewardship.
- Compliance.
Who Uses Data Governance?
Your institution’s governance framework defines the policies and responsibilities of various actors in fulfilling the pillars. Actors involved in direct data governance include:
- Data governance council, including C-level leaders, executives and other managers.
- Daily data stewards, such as departmental specialists, engineers and others, who manage regular and specific data.
- Data consumers and users like teams, workers and stakeholders.
The Benefits and Challenges of Data Governance
The advantages of data governance include:
- Better data integrity and quality.
- Enhanced business intelligence.
- Improved security and privacy.
- Clear data access rules.
- Better risk analysis.
- Improved management costs.
- Data accuracy.
Common implementation challenges include the following:
- Accessibility
- Systematic architecture
- Strong policy leadership
- Cohesive data storage formats
- AI complications and risks
Implementing Data Governance
Smart and strategic governance implementation can help you overcome these challenges and enhance the benefits. Explore examples of how you can introduce data governance to your organization.
Establishing Responsibilities and Sponsorship
Successful governance requires a clear commitment from all actors in your organization. Most importantly, you need sponsorship from institutional leaders like managers and executives to implement your governance strategy. Ensure that governance and data management become cultural facets of your organization so that staff, daily data users and stewards understand its importance for your company.
Creating Strategic Policies and Processes
Support your data governance efforts with standard policies and systems for everyone to follow. This includes establishing a data glossary with business-wide formatting structures, data concepts and terminology. Everyone should know how to classify data points, for example. A data catalog is another important system to establish for easy data access, data history tracking and data integration. Procedures and systems like a glossary and catalog streamline data application and institutional collaboration.
Introducing Data Governance Tools
Select data governance tools that maintain data quality and integrity, provide automated data control and workflows, promote integration and more. Anomalo’s data governance software is a comprehensive tool for data management. You can use it to:
- Improve data visualization and use with at-a-glance tables.
- Automate data validation and quality verification.
- Create data catalogs.
- Secure data and remain compliant with privacy laws.
- Keep data lineages and source information.
Reflecting and Improving
Monitor and enhance data management strategies for continual improvement. Use data reports, system analysis and assessments to pinpoint challenges and design better data governance procedures. Regular reviews can help elevate governance frameworks for better business intelligence and use.
