7 Crucial Data Governance Best Practices To Implement

What is Data Governance

Data governance covers processes, roles, policies, standards, and metrics that help an organization achieve its goals by ensuring the effective and efficient use of information. It sets up the processes and responsibilities necessary to maintain the data’s quality and security across the business.

Data governance manages the formal data assets of an organization. The key elements of data governance involve the following:

• Data categorization
• Clearly defined roles and responsibilities
• Data ethics

Data Governance Framework

A data governance framework specifies who can perform which actions on the data, under what circumstances, and by what methods.  It ensures consistent data policies and rules are applied, creating data reliability.

Building a solid data governance framework involves several key pillars. These pillars include;

• establishing data quality standards,
• integrating data from various sources,
• prioritizing data privacy and security, and
• defining a clear data architecture.

Data Governance Best Practices  

The success of a data governance framework relies on a bottom-up approach, where data creators and users share responsibility for governance. Organizations can create an effective strategy that empowers them to manage their data assets effectively by implementing these data governance best practices:

1. Set up Metrics to Measure Program Success

Track key metrics such as the number of users trained and data sources governed. This tracking demonstrates the program’s impact on improving data awareness, accountability, and overall quality.

2. Align Clear Roles and Responsibilities with Organizational Objectives

Effective data governance relies on a well-defined structure that assigns ownership and accountability across the organization. Here’s a breakdown of key roles important for a successful data governance program:

• Data Governance Council: This high-level body provides strategic direction for the program. The board ensures that data governance processes are implemented within everyday operations, promoting consistent departmental data management.
• Data Owners: Data ownership assigns clear accountability for specific data assets. Data owners are responsible for defining how their data asset is used, creating a sense of stewardship, and promoting responsible data practices.
• Data Stewards: Data stewards implement data governance processes and work closely with data users to identify and address data quality issues, ultimately improving the reliability of data-driven insights.
• Data Managers: Data managers oversee the technical infrastructure that stores and manages data assets. They design and implement database systems that meet the organization’s specific data needs and ensure data security protocols are followed.
• Data Users: The individuals who leverage data for analysis and reporting fall under this category. Data users are empowered to access relevant information within defined parameters. By understanding data governance policies, they can utilize data responsibly and contribute to data-driven decision-making within an organization.

3. Enforce Data Security Policies as Close to the Data Source

It minimizes unauthorized access and simplifies data governance throughout its lifecycle by limiting access points. For instance, implement access controls on customer data within the transactional system where it’s created. Security policies minimize the risk of unauthorized access and simplify data governance throughout its lifecycle.

4. Be Transparent About Your Data Privacy Practices

Being transparent prioritizes data privacy and empowers customers to control their information. This step can involve obtaining user consent for specific data uses and implementing technical safeguards to prevent unauthorized access. Building trust through robust data privacy practices creates customer loyalty and decreases potential legal risks.

5. Power Your Data Governance Strategy with Automation

Here’s how automation empowers organizations to manage their data governance strategy effectively:

• Automated Access Controls: Automated access controls leverage software to enforce granular permissions based on user roles and groups. This streamlines access management and reduces the risk of unauthorized data exposure.
• Automated Data Discovery: Automated data discovery tools can crawl through data repositories, enabling users to pinpoint the information they need easily. The automation eliminates time wasted searching for data across the organization.
• Automatic Data Lineage Tracking: Automation tools can eliminate the need for manual data lineage mapping by automatically capturing data movement throughout various systems. It improves data transparency and simplifies impact assessments when data changes are required.
• Data Enrichment with Metadata: Automation can streamline the process of attaching rich metadata to data assets. These tools ensure a common understanding of data meaning across teams by automatically capturing details about data origin, usage, and definitions, promoting consistent data management, and minimizing misinterpretations that can lead to errors in analysis.
• Automatic Business Glossary Creation: Automation can facilitate creating and maintaining a centralized business glossary. These tools ensure a standardized data language by automatically capturing and integrating definitions of key data terms throughout the organization. The clarity minimizes confusion and fosters more reliable data analysis across all departments.
• Automated Policy Propagation: Data governance policies must be consistently applied across all data assets. Automation can streamline this process by propagating data protection policies to all dependent data elements. For instance, automatically masking sensitive data in derivative reports ensures consistent privacy compliance.
• Automatic Audit Logging: Record-keeping is essential for data governance. Automated tools can capture every interaction with data, providing a detailed audit log. These tools facilitate security investigations and help organizations understand how data is being used. Organizations can streamline compliance efforts by automating audit logs and gaining valuable insights into data usage patterns.

6. Publicize the Program’s Goals, Processes, and Success Metrics

Integrate data governance awareness into employee onboarding programs and technical training sessions, foster a culture of data accountability, and empower them.

7. Schedule Regular Reviews of Your Data Governance Policies

Regular review, at least annually, ensures the policies remain relevant despite evolving regulations, technologies, and business needs. A consistent review process allows for continuous improvement and adaptation of your data governance framework.

Challenges with Data Governance 

Data governance, while crucial for managing an organization’s data assets, faces several hurdles that can hinder its effectiveness:

• Inconsistent Data Management: A single instance of poorly managed data can lead to security breaches or compliance issues. Organizations must strive for comprehensive data governance, incorporating all data sources and departments.
• Fragmented Data Definitions: Inconsistent terminology across various data systems can cause misinterpretations and hinder collaboration. Organizations must prioritize creating and maintaining a unified data dictionary to ensure everyone understands data definitions consistently.
• Undefined Data Stewardship: The lack of a designated data steward or the presence of multiple stewards can lead to a leadership vacuum or conflicting interpretations of data governance policies.
• Insecure Data Access Across Systems: Even with strong access controls in individual systems, data security risks can arise if all systems don’t adhere to consistent data governance practices. Organizations must ensure that all systems accessing governed data implement security measures.
• Operational Constraints: Operational limitations can impede data governance efforts. For instance, difficulty reconciling data across disparate systems can lead to workarounds compromising data integrity. Organizations need to streamline operations and eliminate data silos to ensure consistent data management practices across departments.
• Mergers and Acquisitions: Frequent acquisitions or mergers can create data management challenges. Integrating data from different companies can be complex, leading to inconsistencies and data quality issues. Organizations must have a plan for integrating acquired data while adhering to data governance principles.

Concluding Thoughts  

Data governance supports strategic decision-making through high-quality and well-managed data. Therefore, organizations must incorporate effective data governance best practices when implementing a framework to maximize data security, value, and compliance with standards.

Using the right tool is one of the most effective ways to improve data governance. LIKE.TG offers a comprehensive data management suite that simplifies the data governance process. With features like automated data profiling, the creation of a business glossary, and role-based access control, LIKE.TG empowers organizations to implement effective data governance practices.

Simplify your data management with LIKE.TG. Download a 14-day free trial or contact us to get started.