What Do You Mean You Don't Need a Data Model?

On more than one occasion across my career I've found myself having to justify why we should bother undertaking data modeling exercises. Thankfully I'm not facing that problem currently, but I am in the midst of refreshing a set of strategy, standards and guidelines documents and recently found myself writing a "Why Model Data?" section as a preface to one document. It got me thinking that, despite all of the prior times I'd had the discussions, I'd never really succinctly put down in one place the why it is that I think we should undertake a data modeling process - how it adds value, if you like. So, this blog post is the result.

Modeling of information systems without modeling of the data they operate with can, and does, lead to bad outcomes. Modeling at only a systems or application level often fails to consider important characteristics of the underlying data, instead painting a picture which is only representative of a particular method of use of that data. Such modeling frequently impedes reuse of data across organisations, lowers the quality and speed of decision making and can lead to the unnecessary IT spend and the development of siloed IT applications with unwanted and sometimes unrecognised redundancy. Modeling of data assists with building understanding not only of data content but also of data relationships, with an effective data model being a key enabler of successful integration and well aligned and efficient application architectures and portfolios.

The explicit act of data modeling also encourages discussion about the meaning of data and the appropriate (or otherwise) use of various data elements in different functions of the organisation. It may well expose differing definitions and understanding of the same elements of data that, prior to the modeling exercise, those in the organisation had been unaware of.

Higher level data models also serve as a useful communication tool, helping to bridge the IT – Business divide and provide a mechanism to forge a common understanding of particular areas of the organisation’s data. Data which is easily and well understood is more likely to be reused as not only is the barrier to reuse lowered, but the urge to collect and store the same data elsewhere for another purpose less likely to come to the fore. This important aspect can be a mechanism for cost avoidance in that it lessens the risk of the design and implementation of systems which make ineffective or inappropriate use of the data assets.

Finally, when coupled with other [types of] models, data models allow an understanding of an organisation’s current technology landscape and the way it interacts with business processes. This understanding forms the foundation for efficient and low cost impact analysis around process and system change, in turn enabling business agility.

So there you have it. Part elevator pitch, part business case executive summary. I hope it helps someone the next time they're arguing for funding to develop or maintain the enterprise data model or trying to convince a bunch of rogue developers that simply using Visio to draw an ERD after the application is delivered isn't really the best approach!