Data governance is the overall management and oversight of data assets within an organization. It establishes standards, policies and procedures around data to ensure it remains high quality, consistent, accessible and secure. Data governance follows a lifecycle that can be broken down into 4 key phases:
Discovery
The first phase in data governance is discovery. This involves identifying what data assets an organization has, where they reside, who is responsible for them and how they are used. Key activities in the discovery phase include:
- Cataloging data sources – Creating a comprehensive list of all structured and unstructured data assets. This includes databases, files, applications and more.
- Profiling data – Gathering details on data such as volume, format, usage, originating system and modified dates.
- Classifying data – Categorizing data based on sensitivity, criticality to operations and other factors.
- Identifying data owners – Determining who is responsible for managing each data asset.
- Documenting data flows – Mapping how data moves throughout the organization from origination to destination.
The data discovery phase aims to locate all data, profile its characteristics and dependencies, and organize it based on relevance and importance. This provides the foundation for all other data governance activities.
Planning
After performing data discovery, the focus shifts to planning data governance. This involves defining policies, standards, processes, metrics and organizational roles to manage and govern data effectively. Key activities in the planning phase include:
- Establishing data principles – Defining overall guidelines for how data should be managed based on business needs.
- Creating data standards – Documenting rules for data entry, storage, integration, quality, metadata and more.
- Designing data architecture – Planning optimal structures for storing, integrating and accessing data across systems.
- Developing data security – Instituting access controls, encryption and other measures to protect data.
- Defining metrics – Identifying KPIs to measure data quality, availability, lineage and value.
- Clarifying roles – Designating people to serve in data governance oversight roles.
The planning phase establishes policies, rules, metrics and accountability required for ongoing data governance.
Execution
With the groundwork in place, the next phase involves executing on data governance across the organization. Key activities in the execution phase include:
- Implementing standards – Putting defined data standards and policies into practice across information systems.
- Integrating governance into processes – Incorporating data governance steps into ETL, application development, business intelligence and other data-related workflows.
- Training personnel – Educating staff on their roles and responsibilities within the data governance program.
- Measuring compliance – Using identified metrics to monitor conformance with policies and track data quality.
- Enforcing controls – Imposing security protections and access rules defined for sensitive data.
- Managing issues – Identifying, reporting and resolving exceptions or deviations from defined data governance standards.
The execution phase puts data governance into action, driving adherence to policies while also continuously improving standards and processes based on practical feedback and issues.
Oversight
The final phase of data governance is oversight. This involves monitoring the program on an ongoing basis and adjusting as needed. Key activities in the oversight phase include:
- Tracking metrics – Monitoring key data governance metrics over time for changes and trends.
- Reporting program status – Keeping stakeholders informed through regular reports on data governance progress and effectiveness.
- Performing risk assessments – Periodically evaluating risks associated with non-compliance and ungoverned data.
- Tuning standards – Reviewing and updating data policies and standards to support changing needs.
- Assessing technology – Evaluating tools that can help automate and improve data governance processes.
- Updating roles – Adjusting data governance team members and responsibilities as the program evolves.
Ongoing oversight ensures data governance remains relevant, monitors compliance, communicates progress and continuously enhances the program over time.
Conclusion
Data governance follows a continual lifecycle across four core phases: discovery, planning, execution and oversight. Discovery provides an inventory of data assets to govern. Planning establishes policies and standards for management. Execution implements governance across data processes and systems. Oversight monitors the program and tunes it based on results and changing needs. Following this lifecycle allows organizations to effectively manage data as a strategic asset by putting in place comprehensive governance.
