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30 CHAPTER 3 Overview of a data governance program

imply a “where,” and that is an operational level of detail that evolves. Some functions will be visible
in the DG management framework as stand-alone processes as well as the day-to-day activities that
carry out governance. There is no need to design your DG functional model from scratch; we will have
a list later in the book. However, recognizing that there will be a formal set of functional requirements
(to be manifested as processes) and that they will be executed all the time is a key element to the
success of DG. The functions perform two roles. One, they point out what someone has to actually do.
Second, reviewing the functions required for your organization usually aids in determining which
areas or individuals would bear accountability and responsibility.
The DG area will need to consider other business areas where there will be interaction and
collaboration, such as:

• Human resources
• Compliance and/or legal
• Risk management
• Large-scale integration projects, such as ERP

The bottom line for this element of DG is that you need to formally consider and build the DG
processes and function. They are not instinctive. There is a complete list of sample functions in the
appendices.
The process or functional model for DG needs to specify how the Voperates. There are processes to
develop and deploy DG functionality. In essence, DG has to define the “right things to do.”
DG will identify those processes for “doing things right” as welldthat is, the hands-on information
management activities (the right side of the V).
Metrics

You cannot manage what you do not measure. Over time, your DG program will need to evolve
a means to monitor its own effectiveness. Without it, the DG program will certainly fade away. At the
outset, the metrics will be hard to collect. After all, you have not been managing data very well, so
there is no infrastructure to install a metric. Eventually, the metrics will evolve from simple surveys
and counts to true monitoring of activity. Here is a list of common metrics:

• IMM IndexdReport on information management maturity stated on a scale from 1–5, calculated
based on survey and assessment of various elements of the data governance and data
management program.
• DG Stewardship ProgressdReport on counts of individuals trained on DG, counts of specific
projects governed, and a count of issues elevated and/or resolved.
• DG Stewardship EffectivenessdAlternatively to progress, an effective metric can be based on
counts and resolution of issues submitted to data governance bodies.
• Data QualitydData profiling results calculated into a DQ index that represent an average of all of
the data-quality profiling measures.
• Business ValuedWe will dive into the business case and business value more in the next chapter,
but you can never go wrong with tying the application of DG and data management to business
success. Quantifiable and intangible benefits resulting from successful efforts that were
governed, or through use of governed and well-managed data should always be reported.