Systematic enterprise architecture is often maligned as antithetical to inventive, innovative and agile organizations. I have always thought that small, early-stage organizations do not need much formal enterprise architecture (EA), but as these organizations grow more complex, EA becomes a critical part of strategy execution, ensuring alignment and coherence across many moving parts. Furthermore, in complex organizations, EA plays a critical role in enabling innovation.
The book Nail It Then Scale It (NISI) identifies the confirmation of a “monetizable pain” as the first step in building a successful startup. The book distinguishes between two types of problems: execution and search problems. Execution problems occur when organizational strategy execution can be based on a history of stable operations. For example, mergers between two established businesses, onboarding of new B2B customers, and enterprise software modernization are all, by and large, execution problems. The scope and nature of execution problems are clear, and there is relevant background. Therefore, structured, phased approaches such as the TOGAF Architecture Development Method (ADM) are therefore ideal for envisioning, investigating, planning and overseeing required change. Of course, complex efforts like these almost always involve some novel aspects requiring investigation and iteration. The TOGAF ADM therefore supports iterative architecture development.
However, search problems tackled by startup efforts must typically iterate much more rapidly than EA efforts. Their deliverables are different as well. Therefore, systematic EA as defined by the TOGAF standard, the Zachman framework, and other EA paradigms is not a fit for solving search problems.
However, in established organizations, EA can be used to set the stage for solving search problems. For example, organizations seeking to crowdsource ideas or otherwise collaborate on innovation need enabling business, data, application and technology architectures. Organizations seeking insights into consumer behavior often require carefully architected data aggregation and analytic capabilities. Organizations seeking to create innovative and profitable consumer experiences often need service architectures that expose the logic and data of core business applications. These service architectures must also address the challenges inherent in applications that consumers expect to be available and highly responsive all the time. My recent presentation Always-On Services for Consumer Web, Mobile and the Internet of Things explains one way of meeting these challenges.
In summary, systematic EA is not the right approach for highly iterative efforts that must rapidly find and evaluate new opportunities. However, EA plays a critical role in enabling innovation teams in established organizations to both leverage and protect their existing assets and operations.
How do you use EA to enable innovation? Please post your comments.