In a previous column, I talked about the foundational principlesand benefits of implementing a data governance initiative. I've received a few requests that I delve more deeply into the actual implementation process, and I'm more than happy to oblige.

I've always approached DG initiatives with a component-based approach. To implement this, it's crucial to have a project plan that's detailed but flexible. It's optimal if you can obtain the best resources from throughout the organization to be on the project team, because the DG initiative will, by its nature, involve the entire company.

The most effective approach consists of the following components:

* Organization and policies, principles and standards;

* Processes and practices;

* Governance metrics;

* Technology; and

* Data architecture.

The first component of an effective DG initiative is to create and fill the role of the chief data officer. The CDO should be a member of the executive management team who is tasked with setting the direction of the DG initiative, aligning business and technology goals, and managing corporate data as a strategic asset. The CDO should also chair a DG council that develops data governance policies, principles and standards and enforces them, along with identified data owners. The policies, principles and standards that the DG council develops will be the foundation for all subsequent policies and standards created. The ultimate goal of this CDO component should be to define and direct how data should be used, managed and monitored across the enterprise.

Once data policies and standards have been developed, the next component is to design data processes and practices that will govern how data is collected and disseminated across the enterprise. This begins with gathering the existing data collection, transformation and dissemination processes used by the business functions. These existing processes will act as guiding principles for developing a set of synthesized, standardized processes that can facilitate the delivery of accurate data across the enterprise.

The next component or activity in implementing a DG initiative is to define a set of specific, functional metrics to track the effectiveness of data governance across the enterprise. Input from management as well as knowledge workers should be obtained to develop the metrics. Using these metrics, the DG council and executive management should have a clear picture of how well the organization is meeting its DG objectives. It will also, vis-à-vis timely, accurate performance information, facilitate a shift from a reactive to a proactive data management style. And, as a bonus, it should lead to the development of a culture of continuous improvement of enterprise data. The outcome of this component should be a clearly defined, published set of DG performance metrics that are continuously monitored and updated when necessary.

The next component in implementing an effective DG initiative is more technical. Once the DG policies, procedures and processes have been established and metrics have been put in place to monitor their effectiveness, it is essential to use these processes and metrics to create a centralized data architecture to support effective DG. Building this architecture begins with a current-state assessment of enterprise metadata and data quality management capabilities. It continues with a gap analysis to identify architectural needs in current IT projects. It culminates in gathering architecture requirements across business functions and lines of business, and using those requirements to develop a comprehensive, future-state DG architecture and enterprise information model.

The final component of an effective enterprise DG initiative is to use the future-state DG architecture as a baseline to establish technology standards and acquire data management toolsets that will support effective DG across the enterprise. The goal of this component is to install data management technologies that will enable the execution of DG processes, as well as the establishment of previously defined, critical data architecture concepts. If current in-house technologies do not meet data management requirements, it's crucial to conduct a technology needs/gap analysis and identify vendors that can help address any needs uncovered.

Data governance is not rocket science. It is more like a journey of discovery down a road that you've traveled before but didn't really notice how you got where you are. The road to effective DG starts with choosing to formalize the practice of managing corporate data. It continues by identifying and documenting how your company manages its data, and then developing metrics to monitor the effectiveness of those management practices. Next, the journey turns down a technical path by identifying the IT architecture and IT toolsets that need to be in place to facilitate effective DG, and by building that data architecture and an enterprise information model. It will be a long, involved journey, but you will find it to be well worth the trip.

Deloitte is not, by means of this article, rendering business, financial, investment, or other professional advice or services. This article is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business.

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