How can enterprise architects extract far more insight from their models without significantly increasing the effort required?
The effort required to collect, validate, analyse, and report on enterprise architecture information is itself one of the biggest obstacles to EA delivering value — and yet most approaches simply accept that effort as a given. This 2013 presentation by Graham McLeod challenges that assumption directly, arguing that the right combination of integrated meta models, inferencing, derived values, and visual techniques can dramatically increase the insight produced by an EA repository without requiring proportionally more effort to maintain it. A particularly compelling section introduces polymetric diagramming — a technique that modifies the visual properties of model symbols (size, colour, shape, border width, position) based on the actual data values of the objects they represent, turning what would otherwise be static structural diagrams into rich, information-dense pictures that exploit the human visual system's innate ability to detect patterns, movement, and anomalies. Worked examples show function models where symbol width reflects delay time, process models where width maps to duration, height to cost, and colour intensity to resource consumption, and application maps clustered and sized by investment or number of non-standard interfaces. The underlying architecture — a separation of logical model types from their visual representations, with polymetric specifications scripted in a flexible DSL — is implemented in Pharo Smalltalk using the Mondrian and Roassal graphics libraries and the EVA Graphical Modeler. For practitioners wrestling with the gap between the volume of data in their EA repositories and the quality of insight they can extract from it, this presentation offers both a compelling vision and a concrete technical path.
Originally presented by Graham McLeod at an Inspired event, September 2013.
