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There are two kinds of organizations: those that have implemented a master
data management (MDM) solution, and those that haven't but undoubtedly will
in the near future. Despite where each organization may be in their MDM effort,
what each have in common are questions about achieving better business processes.
Companies planning an MDM implementation tend to have important questions regarding
vendors, strategies and creating road maps to ensure success and a healthy return
on investment. Fortunately, these companies can learn and benefit from the experiences
of organizations that have pioneered MDM and organize their efforts around a
common set of principles. For those organizations whose initial MDM efforts
brought early success, the question is: how can the lessons learned be leveraged
to drive more effective data governance across other lines of business and more
elements of the system's infrastructure?
Some first-generation MDM adopters have been able to build on their initial
implementations to address other important business problems. Observing these
efforts, certain IT analysts and industry observers are beginning to publish
articles laying out models for taking MDM implementations from the early planning
stages through to mature, second- or third-generation stages. Many of these
observers advocate an incremental approach, usually based on a particular data
type or within a single system, such as a data warehouse. Others advocate targeting
a single architectural style, such as a registry style, and then building on
that implementation to address other styles, such as collaborative, transaction
or hybrid.
The reasoning behind this type of approach follows a conservative "technology
maturity curve" as a way to keep data governance requirements in check
and the overall risk of failure as low as possible. These are legitimate concerns,
and many organizations have been able to realize modest gains in solving their
master data problems by following these precepts. However, limiting the scope
of your initial MDM implementation is also likely to constrict the potential
for greater success and return on investment down the road.
The true promise of MDM is that it enables the organization to create a single,
clean and correct version of its most important reference data, and eliminate
the business process inefficiencies that arise from conflicts in various data
sources. This is why it is far more effective to organize MDM implementations
around specific business problems such as compliance objectives, business process
optimization, customer on-boarding, financial risk management and so forth.
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