Data management is a broader concept than data governance. It encompasses the entire process of collecting, processing, securing, and storing data to make strategic decisions. While data governance is included within this larger scope, data management also relates to other important areas such as data processing, storage, and security.
What is data governance?
Effective Data Governance Programs Address This Issue By:
- Improving data quality
- Breaking down data silos
- Ensuring compliance and security
- Managing data access
- Data governance and data management
Data management is a broader concept than data governance. It encompasses the entire process of collecting, processing, securing, and storing data to make strategic decisions. While data governance is included within this larger scope, data management also relates to other important areas such as data processing, storage, and security.
Effective data management relies on close collaboration between these two areas. Data governance sets the strategic direction and policies, while data management teams translate those decisions into practical processes and infrastructure.
Examples of This Collaboration Include:
- Data Integration: When data governance identifies similarities across different datasets, the data management team will determine the models and architecture to connect them.
- Data Access Control: Data governance policies define access limits (e.g., PII), while data management teams implement those restrictions through user roles and access mechanisms.
Benefits of Data Governance
Implementing a data governance framework will enhance the value of data within an organization.
Improved data accuracy leads to better data-driven outcomes, impacting both simple decisions and complex initiatives.
Key benefits include:
- Promoting Data Scale and Understanding: Data governance fosters collaboration across functions and creates a shared understanding of data through centralized standards and documentation. This enables self-service solutions like APIs and data linking.
- Ensuring Security, Privacy, and Compliance: Data governance policies and tools help meet regulatory requirements and prevent data breaches.
- High-Quality Data: Data governance improves the integrity, accuracy, completeness, and consistency of data, leading to better insights into business operations and reducing the risk of errors.
- Enhancing Data Analytics: Reliable data enables data science projects and advanced analytics, fostering trust and adoption.
Challenges of Data Governance
- Organizational Alignment: Achieving consensus on content, definitions, and key data formats across departments can be challenging, especially for non-customer data such as specific product information.
- Lack of Funding: Strong support from both the Chief Data Officer (CDO) and individual contributors (data managers) is crucial for communication, prioritization, and enforcement of data governance policies.
- Data Architecture and Processes: Inefficient data infrastructure hinders the success of the program. Companies may need to restructure data models, integrate storage systems, implement data catalogs, and establish metadata management practices.
