Page 52 -

The CSFs for Data Governance 31

Technology and Tools

The last element thatrequireshigh-levelconsideration istechnology. As ofthewritingofthis book, there is
not a clear-cut category or market for pure DG technology. Most efforts witnessed have assembled various
technologies in support of DG, using SharePoint, Word, and Excel, as well as adapting tools from other
disciplines, like data model or data dictionary tools. Specialty tools are evolving and, in general, you will
wanttoconsiderthefollowingcapabilities,butChapter14willcovertheapplicationoftoolsinmoredetail.
One aspect of tools to understand at this point is you should not feel compelled to buy data
governance tools just because you are doing data governance. By definition, a tool exists to improve
something you are already doing. If you are not doing formal data governance yet, or if you are doing it
poorly, then casting about for a tool to help you deploy DG is a waste of time. This flies in the face of
typical IT philosophy, where the tool is usually acquired first. This is a notoriously silly thing to do.
However, our work always has us putting the brakes on a tool selection project. It is easy to buy a tool
and install it. However, most of the time we witness new tools for data management sitting unused or
poorly deployed. This is because no one has mastered the process the tool is supporting.
As you roll out DG and begin to understand the various aspects of your particular program, you will
know immediately where you need a tool to “grease the skids.” Some features of DG tools that can be
considered are:

• Principle and policy administration
• Business rules and standards administration
• Organization management
• Work flow for issues and audits
• Data dictionary
• Enterprise search
• Document management
• Metrics scorecardddata gathering, synthesis, and presentation
• Interfaces to other workflows and methodologies
• Training and collaboration facilities

THE CSFs FOR DATA GOVERNANCE

Normally, critical success factors are left for last. Because DG is a business program in some ways, but
unique in others, we need to point out the CSFs early on in this book. Frankly, if one or more of the
CSFs presented next are totally unrealistic for your organization, you need to reconsider launching
a formal DG program as an approach to improving data asset management. Or, at the least, you should
call it something else.
1. Data governance is mandatory for the successful implementation of any project or initiative that
uses information. Any project requiring reports, business intelligence, cleaning of data, or
development of a “single source of truth” requires DG to be sustainable and successful.
2. Data governance has to show value explicitly. This means you cannot do data governance in
a vacuum. Something has to be governed, even if it is data quality and you implement data
governance as a means to improve data quality. Countless IT shops developed models,