- What is Data Governance? [2]
Data governance is the system of policies, procedures, standards, and roles used to manage data throughout its entire lifecycle. It encompasses the people, processes, and technologies required to ensure that an organization’s data is understandable, correct, complete, trustworthy, secure, and discoverable.
- Key Objectives The primary goals of implementing data governance include [4]
- Minimizing Risks: Identifying and mitigating risks related to data breaches and non-compliance.
- Increasing Data Value: Ensuring data quality so it can be used effectively for decision-making.
- Compliance: Adhering to legal and regulatory frameworks regarding privacy and security.
- Cost Reduction: Optimizing management costs by avoiding inefficiencies and leveraging synergies.
- Key Components Effective governance relies on several core components [4]
- Data Ownership and Stewardship: Assigning accountability (Owners) and day-to-day management responsibilities (Stewards),.
- Policies and Procedures: Documented rules governing how data is handled, shared, and retained.
* Data Quality Management: Monitoring accuracy, consistency, completeness, and timeliness. - Data Security: Protecting data from unauthorized access through technical and administrative controls.
- Compliance & Risk Management: Ensuring adherence to laws like GDPR or local regulations.
- Data Lifecycle Management (DLM) [5]
DLM is the systematic management of data from its inception to its final removal. It ensures data remains accurate and secure throughout five key stages
- Creation/Acquisition: Generating internal data or acquiring it from external sources.
- Storage: Placing data in secure environments like databases or cloud platforms.
- Usage: Accessing and analyzing data to support business operations and decisions.
- Archiving: Moving inactive data to long-term storage for historical or legal purposes.
- Deletion/Disposal: Securely removing data that is no longer needed to reduce liability and storage costs.
- Levels of Data Governance [3]
Data governance operates across three interconnected levels to ensure alignment with business goals:
* Strategic Level: Focuses on defining the high-level vision, goals, and policies. It aligns governance with the broader business strategy and secures executive sponsorship.
* Tactical Level: Translates the strategic vision into actionable plans. This involves defining roles, establishing workflows, and creating data standards,.
* Operational Level: Involves the day-to-day execution of governance practices. This includes running data quality checks, enforcing access controls, and resolving data issues as they arise,.
