(synopsis of: 7 Stages for Effective Data Governance by Martha Dember)
There is a tendency in organizations to be complacent about data quality and integrity issues (which could compromise credibility of the organization's information).
- is the development and integration of a set of rules/policies, guidelines, and standards - for managing data (not just a collection of ad-hoc data quality projects)
- provides the framework for IT and business to worktogether to establish confidence and credibility in the enterprise's information.
- is implemented by a data governance management team, of IT and business associates, unified by a common goal i.e. to ensure: data is what it is supposed to be (Data Quality); data is in the correct context (Data Integrity); data and its associated metadata are accessible (Data Usability)
- ensures the authority to manage data is properly delegated from the senior-most levels, and that parties are held accountable for executing governance policies as required by their respective mandates.
- has policies and procedures that balance effective information access with appropriate use of the information.
- is a programme (i.e. is not an application, that can be purchased, installed, and implemented with a specified end date) but a process that, over time, affects the culture and the way an organization conducts business.
1. Strategy and Framework
The Strategy identifies data issues, their causes and effects, and methods to solve the issues.
The Framework defines the roles/responsibilities of the data governance team and the relationships and dependencies between the data governance team and the data architecture.
2. Scenarios and Validation
Tests the data governance strategy and framework e.g. takes a data issue (reactively) and determines the cause/effect of the issue, and propose a solution to: refine the processes/framework; determine the communications (how best to implement the steps, and who should play roles).
Benefits: PoC using industry best practices that can be adapted to the organisation
Stage 3: Formalized Organization and Responsive Process Rollout
Formally define the roles (e.g. job descriptons)
Benefits: Accountability for establishing and maintaining data quality.
Stage 4: Proactive Process Rollout
Identify business events or activities that causes problems (data issues)
Benefits: Proactive processes improvements, better communication between business and IT (people can collaboratively manage data)
Stage 5: Expanded Business Involvement
Explicit buy-in from key stakeholders and executive management in the data governance program. Standards compliance monitoring is incorporated as a part of performance measurement; and data-specific technology, processes, and organizational components are aligned with the company's most important business objectives.
Benefits: Continuous improvement efforts are measured and monitored (metrics);Associated processes (e.g. SDLCs) can be improved; Data governance efficiency is improved (the knowledge base is used to reduce future project effort/duration)
Stage 6: Stewardship Culture
Governance across the Enterprise reconciles priorities, expedites conflict resolution, and builds cooperation in support of data quality as a common objective. Data quality education and awareness programs are an integral part of the on-going employee training programs.
Benefits: There is a common focus and delivery and everyone acts as a data steward (extending to business partners/channels)
Stage 7: Strategic Governance
Data governance and compliance becomes real-time, change-driven, on-demand process that continually assess risks, update policies, and manage resources across the enterprise. People, processes, and technology working together organically and autonomically that result in an effective data governance program.
Benefits: Utility of information can now drive flexibility and agility of the organization and result in a streamlined/efficient organization.