Originally published February 15, 2005
In last month’s article, we began discussing an organization’s path toward building an enterprise data hub. A critical part of this endeavor is the framework upon which an enterprise data governance and quality effort should be built—a data governance entity that is empowered by senior management, funded, accountable and “closed loop” (e.g., data quality issues are resolved at the source, not just cleansed via the process). The following chart depicts this framework at a high level.
Some points to highlight about this framework:
Let’s start our look into data governance and data quality at the top with the data governance entity. To be successful, data governance has to be more than a collection of ad-hoc data quality projects. It is, therefore, imperative that a data governance structure be formed that will ensure that authority is delegated from the senior-most levels of the firm to the appropriate parties and that these parties be held accountable to execute against their respective mandates. While there are several possible governance structures, a common theme that runs across them is the segregation of activities and responsibilities into layers—strategic, tactical and execution.
Ratify/modify data management principles
Ensure on-going funding is available
Identify opportunities and issues
Understand costs and benefits
Execute to priorities of the strategy
Ensure the availability of processes and infrastructure
Focus on coordinating tactical delivery
Leverage existing implementation efforts or initiate separate projects
Manage and report opportunities and issues
Analyze costs; monitor, track and report on progress against goals and objectives
Implement projects as defined by tactical component
Educate developers, end-users, etc., on data standards and the importance of data quality
Audit (sampling and monitoring) of data quality to ensure compliance against standards for both internal and external data
Participate in system-related projects to ensure standards (data model, metadata, etc.,) are incorporated in development/enhancement
The following is but one example of an organization structure. It is presented here not as a recommendation for all institutions, but as an illustration of how a data governance organization structure can appropriately involve executive management.
Sample Governance Entity
Since the concept of enterprise data governance is new to many organizations and since key components of data management are not well established, questions abound regarding the structure and responsibilities of the data governance execution layer highlighted above. One of the most common questions is, “How does real work get done within this structure?”
Let’s look at one segment of data. Enterprise risk management is emerging as a major issue within most financial institutions and is VERY data-centric. Let’s use this issue as an example to demonstrate both structure and responsibilities. The following is a generic functional organization chart and key responsibilities for such a group:
Risk Data Management Group
(Sample organization/responsibilities at the data governance execution layer)
Key activities/responsibilities of the risk data management group would include:
- Data warehouse architecture
- Business intelligence (BI) and reporting architecture, and
- Quality assurance and release management
- Includes technical and business definitions of risk data and processes
- Problem identification/resolution
- End-user support, including data access, BI tool support, etc.
- Teach and preach
- User certification
- Periodically review standards, best practices and aid in establishment of enterprise-wide risk data policies and procedures
- Perform periodic data quality impact analysis
- Develop data quality solution proposal and ROI business case for the proposed solution alternatives
The risk data management group would serve as the execution arm in the data governance structure proposed earlier. The head of the risk data management group would serve on the data council.
By forming a risk data management group like the one above, the necessary focus would be put on the effort to build an enterprise risk data environment. By placing this working group within the overall data governance entity discussed earlier, all of the appropriate resources of the institution can be brought to the effort. Resources and priorities can be set and managed with the advice and counsel of executive management. Such a structure neatly “attaches” the working groups with executive management and overall institution strategies.
In next month’s article, we will address practical approaches to data quality and discuss how to achieve measurable results.
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