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xxvi Enterprise Data Governance

As with DNA, reference and master data are the
codification of your business, shared across all business
lines, consumed by all IT systems.

So, if reference and master data are the DNA of your
business, data governance is the genetic engineering. It
means that the main purpose of a data governance initiative
is to improve the quality, consistency and relevance of this
data across the entire organization, not to fix issues after
they have already occurred.

As in biology, improving the quality of your data cannot
rely only on curative techniques. While data quality and data
integration solutions are a key foundation for cleansing and
connecting your data, you need to provide your business
users with an active control on their shared data. Data
governance is a pro-active business initiative that has a real
benefit to enabling efficient and effective business initiatives
or compliance requirements.

Semantic data modeling and Model-driven MDM

If your goal is to gain a real control over your data, you
cannot avoid the data modeling exercise. Without a common
and unified description of your data, how could business
users share the same concepts?

In this book, Pierre Bonnet introduces the concept of a
Model-driven MDM based on semantic data modeling. Far
beyond traditional models, semantic models describe your
data in meaningful terms for all stakeholders, including
business users. This means you can design a rich description
of your reference and master data and hide or bypass the
usual constraints of IT relational oriented modeling such as
join tables or frozen cardinality links. Then it becomes
possible to define complex data objects, mix hierarchical,