We have been doing a lot of thinking and work focused on improving outcomes for organizations approaching innovation projects, which I believe encompasses both modernization and transformation. Organizations want to be successful (including attempts to transform) but often struggle with balancing effort across execution phases while addressing critical success factors during each phase.
In the last 10 years 94% of large Federal Information Technology Projects were unsuccessful, over 50% of these were delayed, over budget, or didn’t meet user expectations, and some 41.4% failed completely (Standish Group & NY Times).
Our research and experience shows that a lack of understanding and vision early in a project results in assumption based decision making, unnecessary risk, and failure. Business transformations are complex from multiple perspectives - user needs, business processes, systems, data, and politics. Like structural engineering, the key is a solid foundation that you can build from, simulate, measure, and understand prior to making large investments.
Traditional ‘paper’ based approaches to acquisition decisions, requirements, and implementation lack the discipline, detail, user engagement, and computer aided decision support necessary to unravel these complex environments, improve our decision making, address our stakeholder challenges, and implement successful transformations. Our approaches to tackling these transformations are themselves in need of innovation and transformation.
We can address these problems by integrating existing industry best practices for agile development, collaborative process, data, and application modeling, along with related software to support our transformation and modernization needs. Applying these approaches early in a project develops the foundation that is needed to mitigate risk and address critical success factors across execution phases.
Instead of ‘paper’ based requirements and user need assumptions we start with an integrated set of collaboratively developed models that computers can simulate, analyze, and prototype to better understand our true needs and the hidden risk that lie in our complex environments. This gives us the basis for improved decision making, planning, customer and subject matter expert engagement, and the added benefit of managing a consistent set of information and models across each phase which better supports our audit needs and increases our efficiency each step of the way.
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