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The Role of Data Modeling in Data Governance

by Danny Sandwell       • January 19, 2016

Greetings and Happy New Year to you all!

Over the past 9 months, the erwin Modeling team has been busy shouting from the mountaintops about our entry in the Data Governance space.  In April of 2015, we released a new edition of our modeling portal, erwin® Web Portal Data Governance Edition, and we have focused on explaining the key capabilities and value that this solution delivers in support of an organization’s data governance efforts.  Built on a foundation of erwin DM models, we naturally refer to our approach as model-driven data governance.  This is an apt description in that we support data governance controls and processes through and by leveraging data models.  However, I think in our natural inclination to focus on what’s “new,” we are relegating a key element of our data governance solution to the shadows.

The act of data modeling, when done correctly, is by definition a data governance activity and a key enabler for success for any data governance initiative.  A high-value output of any well-executed data modeling process is a set of standardized, business-aligned, and multi-contextual data definitions: “standardized” in that they conform to a reusable set of data definition requirements; “business-aligned” in that they capture business rules and regulatory requirements; “multi-contextual” in that they encapsulate metadata that represents both a business and a technical or infrastructure perspective.  They are also generally derived from a collaboration of business and IT stakeholders.  If the data modeling practice is mature, the models and the definitions they contain were created under some form of versioning control, review, and approval processes and industry-standard notations and methodologies.  In other words, the data modeling process is governed in its own right which naturally dovetails into and supports data governance at the enterprise level.  So it’s not good enough to say that data modeling supports data governance because, truth be told, data modeling and data definition through modeling is a key pillar of data governance.

Being the leader in data modeling, erwin Modeling has been delivering valuable capabilities in support of data governance for years.  It’s in the form of reusable templates, naming standards, use-defined properties, and support for other standards. It’s the relationships between the entities that capture the business rules and constraints inherent in our businesses and reflected in our data assets.  It’s the sound model management practices that promote collaboration and enable traceability.  Similarly, as data modelers, you have been living, breathing, and evangelizing data governance within your organization with every model you create.

As a heads up, I will be participating in a panel discussion on this very topic at DATAVERSITY’s EDGO conference on January 27th.  I would recommend attending this valuable virtual conference for anyone interested in this topic or data governance in general.

As an old grizzled data modeler (who asked to remain nameless) asserted in a conversation on the rise of data governance as a topic of interest and inquiry, we have been practicing data governance for decades.  We didn’t need a fancy name, we just called it good data administration.  Just another example of data modelers being ahead of the curve and leading the charge.

In this new year of 2016, make sure you resolve to hug a data modeler.  They deserve it more that you realize.

Have a great year!!!

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