What are the Best Practices of Data Governance?
Data governance is a strategic management system that defines governance policies, enforces decisions, and creates the processes for data collection, regulation, storage, security, analysis, integration, sharing, accessing, and using as per compliance requirements without breaching data privacy and protection laws.
A scalable data governance framework guarantees data integrity, security, transparency, and availability. It also reduces data risk and indicates a better way to generate, manage, and consume data. In addition, data governance defines various layers of responsibilities in an organization. Data governance ultimately determines data architecture, data warehousing, and management alignment.
Consider data governance as a strategic roadmap that supports enterprise data management to achieve operational agility and governance goals. Flexibility is one of the significant features of a robust data governance framework. It means you can tailor it to meet the level of governance and regulatory compliance required by an organization. You get this flexibility because data is used for business intelligence which is dynamic and constantly evolving.
A strategic data governance framework encapsulates data management, metadata management, Master data management, risk management, project management, and program coordination. Here are the scope and goals of each of these aspects:
- Data Management
An administrative process involving how data is acquired, validated, stored, protected, and processed for highlighting the way of accessing such data, its level of reliance, and how timely it is for users.
- Metadata Management
Refining the process and setting strategic goals to collect, categorize, maintain, integrate, control, manage and deliver metadata.
- Master Data Management
To establish a non-invasive approach to address data privacy and drafting a policy around different aspects of master data governance such as data quality, privacy and protection, retention and deletion, and risk management.
- Risk Management
To plan and implement an actionable roadmap to integrate risk management components such as integrity, accuracy, completeness, consistency, effectiveness, efficiency, confidentiality, availability, compliance, and reliability in data governance regulations.
- Project Management
A practice that provides a structure for using data and making data-driven decisions based on the actionable insights to safeguard projects within a larger project.
- Program Coordination
It is a structure and framework for tactical planning, setting goals, making decisions for programs, and assigning responsibilities and roles. Program coordination also recognizes the subsequent stakeholders concerned and who is permitted to grant program events and priorities.