Message from the editor

Mathematics and modeling in general have taken a beating during the downturn, especially when it comes to economics. Nobel Laureate Paul Krugman, an economist himself, put it this way: “The economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth...” Later in the same New York Times article, Krugman concluded that “economists will have to learn to live with messiness. That is, they will have to acknowledge the importance of irrational and often unpredictable behavior.”

As any businessperson will realize, the problem Krugman alludes to isn’t the use of mathematics or models per se. In business, as in all other complex aspects of life, the problem is relying on bad models that don’t allow for unpredictable behavior—what modelers call the “emergent properties” of complex systems. Every company is itself a complex adaptive system. Often it’s the unpredictable behavior of that complex system that leads to value creation in a company. For that reason, especially when it comes to today’s rapidly changing business environment, how enterprises address the “messiness” factor can lead to success or failure.

People use models all the time, whether it’s a drawing on a napkin, a diagram in a drawing tool, the formulas in a spreadsheet, the output of an enterprise architecture tool, or a full-blown simulation study. Models are a necessary abstraction, a simplification of the real world people have to create to accomplish any objective that involves any degree of complexity. They’re essential—otherwise, the complexity is overwhelming. That’s especially the case for large enterprises. Models simplify to allow understanding—their primary role. Because they simplify, they can’t be totally accurate. Sometimes they’re obviously inaccurate, but helpful anyway. As statistician George E. P. Box observed, “All models are wrong, some are useful.”

Models not only simplify but explain complex behavior and help predict outcomes. Predicting outcomes to the extent possible—for instance, a range of the likely outcomes, ranked in order of probability—is essential to transformation efforts. During a time when many of our clients face the need to transform to survive and simultaneously reposition themselves for future growth, a sound overall approach to modeling becomes indispensable.

In this issue of the Technology Forecast, our annual enterprise architecture issue, we look at modeling and simulation from an architectural and transformation standpoint. As usual, during the research phase for the project, we first reviewed architecture, transformation, and modeling quite broadly. What we learned from this first exploratory research phase was that companies are using models in a wide variety of ways to help with a wide variety of decisions. Some of those decisions involve how to launch entirely new businesses. And some of the best decisions being made are supported by models that look at companies as complex adaptive systems.

For that very reason, our lead article for this issue, "Embracing unpredictability", focuses on the rich modeling and simulation techniques companies are beginning to use to make space for and encourage emergent properties, the unpredictable behavior that can create value. As data become more available and companies can take greater advantage of them with more powerful computing capability, agent-based modeling, which studies the behavior of individual actors in a complex environment, is becoming more prevalent. Though we use fundamental innovation as a primary example of how agent models can be used, they’re useful for a variety of transformation efforts.

"Escaping the EA stereotype" looks at modeling from an enterprise architect’s point of view. To date, EAs have had a value proposition that has been almost entirely IT focused. Because IT costs have spun out of control, the tools that emerged to support the EA primarily focused on IT financial and asset management. But this is changing. New tools and additional features of existing tools have started to move the needle for the EA function, opening up the possibility of a new, more business-unit-centric role for EAs. EAs are also taking advantage of the new power of semantic technologies to help solve the interoperability issues that plague many transformation efforts.

When we started our research, we expected to find that CIOs and enterprise architects would be playing key roles in improving enterprise transformation effectiveness. This is because we figured modeling was the special domain of enterprise architects. What we found instead is that business modeling has been happening mostly without the direct contribution of IT. "The CIO’s Opportunity to Transform Transformation" explores how CIOs can have more of an impact on transformation efforts by anticipating and harnessing the power of complex adaptive system models, as an adjunct to the modeling efforts the IT department is already involved with.

In addition to these features, we include interviews with four people who underscore both the leading edge and established practice in these areas:

  • Dr. William Rouse of the Tennenbaum Institute at the Georgia Institute of Technology explores why most transformations fail and how modeling and simulation can help.
  • Dr. Mark Paich of Decisio Consulting describes how modeling enterprises as complex adaptive systems can pay off in transformation initiatives.
  • Tannia Dobbins, an enterprise architect at AMD, sheds light on the practicalities of enterprise architecture in today’s cost-conscious business environment.
  • Michael Lang of Revelytix and Brooke Stevenson of Spry describe how fixing data description problems can improve your odds of transformation success.

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As always, we welcome your feedback and your ideas for where we should focus our research and analysis in the future.