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Customer Experience and Design

Healthcare Data Modeling Governance

I participated in a webinar/panel discussion last week hosted by Dataversity on Data Modeling Governance, which was well attended and lively. The focus was on governance of Data Models and the Data Modeling environment (e.g., tools, model repositories, standards).

Data Modeling Governance is supported by Data Governance – and Data Governance benefits significantly from Data Modeling Governance. I will describe what Data Modeling Governance is and how it relates with Data Governance.

Data Modeling Governance entails making strategic decisions about enterprise data model assets, data modeling practices, and the data modeling environment in order to support improved governance, management, quality, and definition of data assets. Data assets usually originate from a data model – the more our models are aligned with the enterprise (e.g., standards, nomenclature, and modeling practice) – the more our data assets will be aligned, reusable, sharable, and of higher quality.

Some examples of Data Modeling Governance in support of Data Governance:

  • Development of and adherence to a Data Modeling Standards document. By documenting these standards (rather than having them in people’s heads or in an email somewhere) a common modeling practice can be instituted, leading to less contention and confusion, more effective model reviews, more rapid modeling (not reinventing the wheel on every project), and more reusable and standardized model objects (resulting in more reusable, integratable, and shareable data – all good things that Data Governance loves).
  • Enabling a common model repository leads to more secure and findable models. Data models often express significant intellectual capital of the enterprise and so needs to be properly secured. Much business metadata can/should be expressed in data models (e.g., business object names, conceptual relationships, definitions, etc. This is of much value to Data Governance and especially important to Data Stewardship.

Data Governance in turn needs to support Data Modeling Governance. Often, Data Architects and Modelers struggle to implement best practices only to face resistance from project teams. By raising awareness of issues encountered to a Data Governance Board, Data Governance working with Application Development leadership can make recommendations for updating the SDLC to ensure those best practices are included so that adequate time for solid modeling practices is accommodated in projects.

I am interested to hear your feedback and experience of Data Modeling Governance. You can reach me at, on twitter at @pstiglich, or in the comments section below.

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