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Data Governance Roles, Responsibilities and Results

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I'm not a really a "word" guy. I do not sit around thinking about the nuances in the definitions of words, and I do not usually enter into semantic discussions. I think function is more important than nomenclature. Nonetheless, there should be a common, agreed upon language - a system of classification and identification if you will - to have a fruitful discussion on any topic.

In my experience, data governance is no exception. It is a broad topic with significant importance for any company trying to manage its data effectively to monitor and manage business performance and sustain regulatory compliance. However, setting up a smoothly functioning data governance organization (DGO) can often be crippled by problems with defining its myriad roles and responsibilities.

Therefore, even though I don't usually engage in semantic debates, I am going to throw in my two cents' worth. I will start with my informal, working definition of data governance: data governance is the process by which a company manages the quality, consistency, usability, security and availability of its data.

There are likely as many ways to implement a DGO as there are companies, but I think there are four data governance roles that are common to most, if not all, DGOs. My titles for them are:

  1. Data steward
  2. Data owner
  3. Data manager
  4. Data user

Figure 1 represents these typical data governance roles.

Figure 1: Typical Data Governance Organization Roles

The first three titles - steward, owner and manager - are often considered to be synonyms, but as you will see, they are not. Data users are often also called "the user community," "information consumers," "internal customers," etc. The list of names is as varied as the companies that use them. Perhaps these names seem synonymous, but they denote four very distinct sets of responsibilities, regardless of what you call them.

Data stewards are policy people. Stewards have several crucial responsibilities, including defining data governance policies and advising data owners and managers on the implementation of those policies. Stewards also have a hand in high-level information requirements definition. They develop and monitor control policies for data, and they serve as overall coordinators for enterprise data delivery efforts.

Stewards also define performance measures to help determine how well the data governance effort is working. Finally, they work with data owners, managers and users to continually improve corporate data flow. The role of data steward can be played by one person or multiple people working individually - depending on the size of the governance effort - or it can be played by a stewardship committee. Either way, the responsibilities are the same.

Data owners, working with the data steward, carry the primary responsibility for defining corporate information requirements. There will be data owners in every business function throughout the company. Data owners develop standards for the storage, retention and disposal of corporate information. They also work to help ensure information quality and availability.

Data owners control access to information for change management purposes; if requirements change, data owners oversee changes to data definitions required by the changes in requirements. Finally, the data owners work with data managers to actually deliver data to the company.

Data managers, also sometimes called custodians, work closely with the data stewards and data owners to implement data governance policies and carry out the data delivery function. There will also be multiple data managers throughout the company. Data managers work with the user community to share information about current applications and technology, and they provide a conduit for process improvement ideas.

On the technical side, data managers capture, store, retain and dispose of enterprise information in accordance with policies defined by data owners. Data managers also design the technical infrastructure to meet data owners' information requirements.

Data users, or the user community, are not considered by some to actually be part of the formal DGO. I think they are. To me, the user community is a linchpin in the DGO because all the policies, requirements, delivery mechanisms and technical architecture designs are created to meet the needs of the user community. Without the data users there would be no need for information to be governed!

Date users must understand their information requirements sufficiently to assist in the data governance function and comply with information management policies. Because they are on the frontline, the data users can play a critical role in communicating how information is used and how information processes can be improved. They also work with data managers to share information about applications and technologies - what works and what does not. In short, the needs of the user community drive the need for data governance.

Once you have defined the roles and responsibilities of your DGO, it is important to give the organization "teeth" to function and carry out its mission. There are several significant factors that can determine how well your DGO functions. First, in my experience, it is imperative to give the DGO a clear charter and mandate and to clearly define roles, responsibilities, processes and activities. It is important to communicate that charter, as well as the importance of the DGO itself, to the entire company.

It is also important to properly and adequately staff the DGO. The right mix of resources and skill sets is required to effectively carry out the DGO's charter. Speaking of effectiveness, bogging down the DGO in layers of corporate bureaucracy increases the likelihood that it will be ineffective. The data stewards, or stewardship committee, should report directly to the C-suite to help minimize the potential for miscommunication of objectives and policies.

Finally, I think it is important to tie compensation to the results of the data governance effort. To that end, it is important to develop and monitor data governance performance metrics. The metrics should be as quantifiable as possible, and meeting the metrics should be a part of each DGO resource's performance review process. When people understand that their money is tied to their performance, they are more likely to give a top-notch effort.

There are many ways to design and implement a DGO. It is unimportant what you call the various functions within the DGO; it is how you define and implement those functions that matters. If you can clearly define and properly implement your DGO, you will be well along the path to data governance effectiveness.

Rich Cohen is a principal in Deloitte Consulting LLP's Information Dynamics practice where he is responsible for the strategy, development and implementation of data governance, data warehousing, decision support and data mining engagements to support the emergence of world-class business intelligence applications.

This article originally appeared on DMReview.com.

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