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Data Governance: A Strategy for Success, Part 2

Published
  • August 01 2005, 1:00am EDT

Since my last column on data governance (DG) was published (see the June 2005 issue of DM Review), I have had several people ask me to write a column on how to develop a DG methodology. As much as I'd like to, I can't. Why? Because every business is unique, and I don't believe there should be a single, one-size-fits-all methodology for developing and implementing DG standards and policies. What I can do, however, is give you an outline of the structural elements that any comprehensive DG methodology (or strategy, or plan or whatever you wish to label it) should contain.

Leadership

Any DG methodology should stress strong leadership and commitment by senior management. C-suite commitment is critical to achieve high-quality data and financial reporting mandated by the 2002 Sarbanes-Oxley Act; executives now must attest to the accuracy of those reports. However, a good DG methodology will turn those requirements into an asset by instilling a culture of data quality throughout the organization. The best way to achieve and maintain leadership and commitment to enterprise-wide data quality and governance is to form a data management governance council (DMGC) comprising data owners throughout the organization, as well as representatives from IT.

The responsibility of the DMGC is to coordinate, manage and monitor the development of enterprise-wide audit and control procedures and data standards and policies. One critical focus area will be managing meta data standards, because meta data underpins the organization's ability to define and manage its data sources, definitions, uses and relevance. Nonetheless, all data standards and policies should be under the purview of the DMGC. That way, DG policies are standardized, and changes can be easily and accurately propagated throughout the organization when necessary.

Process Definition

Process definition is concerned with how you implement the standards and policies - such as audit and control, meta data management procedures and data standards - developed by the DMGC. I can't stress enough how important it is to have well-defined processes to follow when implementing DG standards and policies. Politics come into play here. Cries of, "But we've always done it this way!" will be plentiful. Answering those often pitiful pleas will be much easier if you can point to a set of processes that all departments must follow to confirm that standards and polices are effectively implemented.

A good DG methodology will not define those processes for you. Instead, it will give you guidelines for defining your own, based on your organization's structure and needs. Again, there is no one-size-fits-all methodology out there; the same is true for DG implementation policies. The key is to develop a set of standard processes and to be consistent in implementing them.

Technology

The final, and perhaps most important, foundational element of a well-rounded DG methodology is an emphasis on selecting and employing software to facilitate whatever DG initiative you create for your organization. The array of software needed to implement a DG initiative can be overwhelming: ETL and EAI tools, data quality software, multidimensional and relational databases, BI tools and meta data management software. You'll need the most help in developing a tool evaluation and/or selection matrix based on your data needs and structures.

Because most of the tools are probably already in place in your architecture in varying degrees, you shouldn't have to spend massively to acquire software you don't own. There is a caveat here, however: to implement a top-notch enterprise-wide DG program, you'll need tools that are best-of-breed - loaded with functionality and geared toward enterprise-wide application. A good DG methodology will help you understand how all these tools facilitate data movement and management. It will also help you develop policies and procedures for integrating and implementing the various technologies.

Let's recap. A top-notch data governance methodology should do three things:

  1. Enable you to gain C-suite commitment to DG leadership and a culture of data quality.
  2. Provide you with guidance in developing and implementing processes to implement an enterprise-wide DG initiative.
  3. Help you in the selection and integration of appropriate technologies to implement the DG initiative.

A good DG methodology will also be flexible and provide an outline to help you do things for yourself. Shy away from any vendor that says, "This prepackaged DG methodology will fit your organization, no problem." It won't. Because your organization is different from any other, your strategies for effectively governing your organization's data must also be different. A good methodology will be a guide - an outline - for developing your own standards. It will be completely customizable for your completely custom organization. Stick to one that contains the three elements I've outlined, and you'll have a running start at success.  

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