Enterprise Architectures as Data

Enterprise architectures are more than static structures, use cases, and process/sequence models.  Enterprise architectures are also queryable data sources that, once constructed, can be used to answer a great many questions relevant to decision making based on multiple stakeholder concerns; operational, technical, financial, logistical.  In my experience, this assertion usually surprises people.  Operators want to know what complex (in their minds complicated) EA models have to do with getting the job done.  Appropriators want to know how system producer-consumer dependencies relate to purchasing decisions.  To anyone unfamiliar with enterprise architecture, EA can be seen as not only having no value, but as an unwelcome cost burden.  Yet, each of these perspectives is relevant to an enterprise architecture.  Many architects understand this problem but have been helpless to address it.  Enterprise architecture is a rigid, rigorous discipline.  The language and views of architects are complex and detailed.  The tools architects use are highly specialized.  All of this contributes to formidable barrier to information and knowledge sharing.

The good news is that there is a relatively simple technology solution that can cut through the complexity and lead to better decision making informed by a variety of stakeholder perspectives.  Now, I'm not saying this is a case of technology riding in on a white horse to save the day, but it's darn close.  To be sure, technology's job here is to get out of the way; to provide the least amount of resistance and friction to business processes and people communicating.  By focusing on a strategy for how EA data is stored, extracted, and transformed we can make the data more versatile.  By making the data more versatile we can make the information that data describe more useable.  

The solution strategy, then, is to make enterprise architecture data more versatile using standards.  

By implementing this strategy we can use enterprise architectures as data sources to answer a diverse set of typical stakeholder questions.  Using this strategy we clearly see that otherwise detailed, complex data can be easily queried, extracted, transformed, and visualized in entirely new ways.