Is Data Governance Part of Data Management?
As organizations acknowledge the importance of effective data handling, understanding the relationship between data governance and management is essential for handling data effectively. Many professionals in the fields of information technology and data management often find themselves asking: Is data governance part of data management? This blog aims to clarify these concepts and explore how they work together to optimize data use.
What is Data Management?
Data management and data governance are closely linked yet serve distinct purposes. Data management encompasses the broad spectrum of practices, architectures, policies, and procedures for managing enterprise data assets throughout their lifecycle, including collection, storage, processing, and distribution, ensuring data is accessible, reliable, and secure. According to the International Data Management Association (IDMA), effective data management includes “the development of a data architecture, data modeling, data warehousing, and database management.”
Enterprise data management is led by the Chief Data Officer (CDO), supported by the Data Management Team (database administrators, data engineers) and IT. The CDO aligns data strategies with business goals, ensuring integration, storage, and accessibility. Key roles include Data Architects (system design), Database Administrators (infrastructure maintenance), Data Engineers (pipeline building), and Data Quality Analysts (accuracy assurance). They manage database operations, data integration, security, backup, recovery, and master data management, implementing tools necessary for effective data handling, like ETL processes, data quality solutions, metadata management systems, data recovery procedures, and security measures to maintain system performance and data integrity.
What is Data Governance?
While closely related, data governance functions as a critical component within the larger data management framework. It provides the strategic oversight, policies, and standards that ensure data is managed effectively, securely, and in compliance with regulations. It encompasses aspects such as data quality, security, compliance, and the roles and responsibilities of those who handle data.
Data governance is led by the CDO and the Data Governance Council, a cross-functional team that includes stakeholders from various departments, such as IT, compliance, and legal. The Council establishes data policies, standards, and procedures to maintain data quality, integrity, and compliance. The governance team establishes data definitions, ownership, access rights, quality metrics, and compliance requirements. Data Stewards manage specific data domains to enforce these standards, while the Data Privacy Officer (DPO), along with Compliance and risk management teams, ensures compliance with data protection regulations, such as GDPR and CCPA.
Understanding the Relationship
As noted by industry experts, governance sets the “why” and “what” of data handling, focusing on strategic oversight and policy development. However, data management answers the “how” of data handling—implementing the guidelines established by governance to ensure that data serves its intended purpose effectively.
Simply put, Data governance is the blueprint for a building; data management is the physical construction. Without governance, management efforts may lack direction or compliance with regulatory standards. Think of data management as the overarching umbrella under which various data-related functions operate:
Data governance is the foundational element that guides and influences all other data management components. It sets the course and guidelines for data management and provides the necessary oversight to ensure data practices align with organizational goals and compliance requirements.
Why This Relationship Matters?
Understanding the relationship between data governance and data management is crucial for organizations that want to maximize their data assets’ value, security, and usability. As discussed, data governance sets the rules, policies, and standards for handling data, while data management puts these guidelines into practice across data lifecycle processes. This relationship helps assign roles and responsibilities clearly, ensuring accountability and minimizing overlaps. Additionally, it enforces data quality and consistency, reducing errors and duplications while enhancing the trustworthiness of data used in decision-making.
Data Governance Best Practices
Organizations looking to enhance their data governance and management practices should consider adopting several best practices. These strategies can help establish a strong framework for data handling, ensuring better compliance, security, and overall data quality.
- Establish Clear Policies: Develop and document data governance policies that outline the processes for data management, including data classification, retention, and access controls.
- Define Roles and Responsibilities: Assign clear roles for data governance, such as data stewards and custodians, to promote accountability and ensure that data is managed effectively.
- Utilize Data Governance Tools: Invest in data governance tools that support data lineage, metadata management, security, privacy, and data quality monitoring.
- Conduct Regular Audits: Schedule periodic audits of data governance practices to assess compliance with policies and identify areas for improvement.
Metrics for Measuring Effectiveness
To ensure that both governance and management are functioning optimally, organizations should track specific metrics:
- Data Quality Metrics: Assessing accuracy, completeness, consistency, and timeliness can provide insights into how well governance initiatives improve data quality.
- Compliance Rates: Monitoring adherence to regulatory requirements can help gauge the effectiveness of governance policies.
- Maturity Models: Utilizing frameworks like the Data Management Maturity (DMM) model can help organizations evaluate their progress in both governance and management practices over time
Bottom Line
Data governance is unequivocally a vital component of data management, functioning as its guiding framework and control mechanism. Rather than viewing them as separate entities, organizations should recognize their symbiotic relationship and work to strengthen their integration. This understanding is crucial for enterprise leaders seeking to build robust, compliant, and effective data management strategies. As data continues to grow in volume and complexity, this integrated approach becomes not just beneficial but essential for sustainable data management.
Learn more: “The Essentials of Sensitive Data Discovery and Security-Compliance Tools” – Unlock the knowledge you need to protect sensitive data and ensure compliance with the latest security tools and strategies. Read it now!
About the author
Vishnu Jayan is a tech blogger and Senior Product Marketing Executive at Solix Technologies, specializing in enterprise data governance, security, and compliance. He earned his MBA from ICFAI Business School Hyderabad. He creates blogs, articles, ebooks, and other marketing collateral that spotlight the latest trends in data management and privacy compliance. Vishnu has a proven track record of driving leads and traffic to Solix. He is passionate about helping businesses thrive by developing positioning and messaging strategies, conducting market research, and fostering customer engagement. His work supports Solix’s mission to provide innovative software solutions for secure and efficient data management.
