Adaptive Data Governance: The AT-EASE Change Management Approach - Data Governance: Creating Value from Information Assets - page 161

ENHANCING AT-EASE APPLICATION
WITH SYSTEMS THINKING
Emerging data governance models are complex and encompass a wide
range of issues. For example, the Data Management Body of Knowledge
(DMBOK) consists of 10 data management functions and 7 environmen-
tal elements (Moseley, 2008). It uses graphical representations to orga-
nize these components. DMBOK provides context diagrams showing the
detailed steps in each of the data management areas. The implementation
of any of these 10 areas involves considerable complexity. Thus, organiza-
tions may need to prioritize and address a few data governance issues at
any given time. While DMBOK uses graphical representations to organize
information, these representations are geared toward conveying the com-
prehensive nature of the standards themselves rather than to serve as a
tool for organizations to develop their own models.
Tailoring the general graphical representations in existing data gover-
nance approaches to the organization's specific situation can enhance the
use of representations for communication and problem solving. Given the
complexity of data governance issues, no one model is likely to completely
meet an organization's needs at all times. Systems thinking and associated
diagramming techniques can help adapt guidance from various standards
to a particular organization, incorporate situational factors, and integrate
other elements (e.g., change management or other issues not addressed
in-depth in a particular models) to develop customized visualizations
for a specific context. This section shows how using a systems thinking
method to examine data governance approaches can set the stage for
developing flexible graphical representations that support adaptive data
governance. The following section continues this discussion to show how
the AT-EASE elements provide a structure to create systemic models for
data governance that can integrate information from various sources.
Recent data governance approaches recognize the need for systemic or
holistic approaches that consider the complex interconnections between
different elements in implementing data governance. Broadly, systems
thinking can be seen as a language (Goodman, 1991) that allow us to create
simple, yet rich, descriptions of complex, interconnected realities based
on multiple perspectives (Imam, LaGoy, and Williams, 2007). Fi g u r e 7. 2
uses the AT-EASE elements as building blocks for a systemic view that
helps stakeholders visualize how people influence each other to learn and