5 Steps to Data Governance

   Mike Cochrane, CIO, Palladium Group, Inc.

As systems mature and data volumes explode, the ability to control the flow and integrity of data becomes costly and cumbersome to manage. Developing a data governance program may be the most effective way to address these issues and ensure data quality across the enterprise.

The most common definitions of data governance focus on achieving data quality through a combination of transparent data stewardship processes, a cross-functional hierarchy of committees, corporate policies and enterprise technology.

This is all relatively straightforward, but challenges surface when trying to find the appropriate balance of data scope, executive leadership, cross-functional representation, business process reengineering, and key initiatives.

A method to reach that balance can be achieved by following a set of comprehensive steps in developing your data governance program.

1. Start with a working group. As with any major initiative, you need to determine your strategy and goals while building a business case to obtain funding. Establish a small working group that represents multiple business perspectives, and outline the key operational and cost benefits of the program. This group will vary in size depending on your initial goals, and it should not exceed more than a few individuals, with one person tasked in leading the program. Assume that less is more in this situation.

2. Develop an operational framework. Your data governance program needs an operating framework that clearly defines how the program works and how the pieces logically fit together. I use a five-component framework that outlines how the governance program works within a closed-loop functional model. These five components are:

• Strategy and planning. This is where envisioning and strategy creation happens. Once the program's mission and goals are clear, the focus shifts to planning and scoping the first iteration or pilot program.

• People. To effectively execute against your governance plans, the right set of people needs to be in place. This is accomplished by formulating a data governance council and an ongoing data stewardship competency.
 
• Integrated processes. Once a working data governance council is identified, the key processes for how the group works together must be established. In this area, the communication protocols are defined, roles and responsibilities are established and accountability is set through the declaration of decision rights and controls.

• Data policies. Data standardization, compliance regulations and quality controls will be major areas of focus for driving overall data quality. Performance metrics should then be introduced to measure the overall effectiveness of the program.

• Technology enablement. Many data governance programs originate as part of a technology implementation such as a data warehouse, BI or MDM solution. These implementations are often used in the same phrase as data governance, and while appropriate, it is important to understand the role technology will play in your data governance program. For instance, an MDM implementation will provide the tools that enable data stewards to better manage reference data in a centralized location. Without clear data stewardship processes and accountability, the technology tools may be misused, resulting in further data quality issues. To achieve success you must place equal emphasis on what processes technology is enabling, who will use the tools and how they will use them.