Measurements for Data Governance: A Definition and Explanation
In today's data-driven world, Data Governance has become a crucial practice for businesses aiming to manage their data effectively. This practice organizes, manages, and monitors the integrity and security of data within an enterprise's system.
To implement Data Governance successfully, businesses must establish policies and frameworks for their data management processes. These policies and frameworks are designed to facilitate compliance with current internal and external regulatory standards, such as the General Data Protection Regulation (GDPR).
A Data Governance course is available for those looking to learn more about this essential practice. The course helps establish policies and frameworks for data management processes, focusing on organizing, managing, and monitoring data integrity and security.
The course also teaches Data Governance metrics, which are used to measure the efficacy and impact of a Data Governance program. These metrics show whether a Data Governance program is aligned with an organization's business strategy.
Common policies and frameworks used in Data Governance focus on defining clear roles and responsibilities, data management processes, technical infrastructure, performance monitoring, and regulatory compliance, including GDPR.
Key roles in Data Governance include Data Owners, who define access, usage, and lifecycle decisions; Data Stewards, who oversee daily data management and standards enforcement; IT and Security Teams, who enforce security controls, permissions, and compliance; and Business Users, who use and contribute data under clear guidelines.
Policies and procedures in Data Governance cover areas such as Data Usage Policies, Retention and Deletion Policies, Security and Privacy Policies, and Governance Escalation Paths. Data Lifecycle Management Processes specify procedures for data entry, validation, transformation, integration, migration, and disposal to maintain data quality throughout its lifecycle.
The technical architecture and tools used in Data Governance are also crucial. These define the infrastructure, data management, integration tools, and security frameworks supporting governance. Performance Metrics and Monitoring establish Key Performance Indicators (KPIs) and regular audits to ensure policy adherence and data quality maintenance.
Compliance with GDPR is a significant aspect of Data Governance. Frameworks incorporate GDPR-required principles such as data minimization, lawful processing, data subject rights, breach notification procedures, and strict privacy/security controls. Clear policies and roles support readiness for audits and enforcement of GDPR mandates.
The course does not provide specific details about the internal structure or content of the Data Governance courses. It also does not discuss the potential benefits of Data Governance beyond the mentioned points. However, it is clear that Data Governance is critical for ensuring data is accurate, secure, compliant, and aligned with organizational goals while balancing control with usability and innovation.
[1] Chung, K. (2018). Data Governance for Dummies. John Wiley & Sons. [2] Data Governance Council. (n.d.). Roles and Responsibilities in Data Governance. Retrieved from https://www.datagovernancecouncil.org/roles-and-responsibilities-in-data-governance/ [3] Data Governance Professionals Association. (n.d.). Data Governance Lifecycle. Retrieved from https://www.dgpa.org/data-governance-lifecycle/ [4] Data Governance Professionals Association. (n.d.). Data Governance Framework. Retrieved from https://www.dgpa.org/data-governance-framework/ [5] Data Governance Professionals Association. (n.d.). Data Governance Framework Approaches. Retrieved from https://www.dgpa.org/data-governance-framework-approaches/
- To ensure successful implementation of data governance in data-and-cloud-computing environments, businesses need to leverage technology, such as the policies, frameworks, and tools provided by Data Governance courses, to monitor and maintain data integrity and security, comply with regulatory standards like GDPR, and balance control with usability and innovation.
- As a complement to Data Governance best practices, understanding technology-focused domains like data-and-cloud-computing is essential for businesses seeking to streamline their data governance efforts, while fostering a culture of data-driven decision-making that truly builds a competitive edge.
