[originally posted on 11/29/2008]
At the recent Strategic Architecture Forum (SAF) in San Francisco, I was given opportunity to discuss the topic of “What an Architect Needs to Know”
My opening slides contained some statistics of what industry research organizations believe we should know:
Roger Sessions recently posted about Senate Bill S.3384 and Public Sector IT identifying how government through committee oversight can “fix” this. Roger accurately identifies, “There is no possible way that S.3384 can be implemented successfully unless the government first takes steps to understand and manage IT complexity.”
A key point Roger captures here is complexity. It seems to me that many organizations prefer to adopt a philosophy around Occam’s Razor thinking they will have greater success if they pursue simplicity and eliminate complexity. There obviously is some merit with this philosophy except when the problem space/solution truly is complex.
The government suggests the best approach to deal with complexity is to manage it up front and halt the development process once there is a 20-40% deviation for the committee to evaluate the situation. The idea we can mitigate the effects of complexity by unraveling it all up front implies, to me at least, the problem space/solution was never complex to begin with.
What this meant was that a 60% on a test could easily be an “A”
|Is it just me, or does this closely align with the type of situations we IT Architects deal with in our problem space/solution deliveries? We are brought in to apply our previous experiences and knowledge (often in the form of proven patterns) to problems with many unknowns. While we can’t guarantee success in terms of what outside observers use to measure success, we can apply our Deep Smarts to attack the problems presented using the best known techniques and practices to solve the unknown.||Deep Smarts are the engine of any organization as well as the essential value that individuals build throughout their careers. Distinct from IQ, this type of expertise consists of practical wisdom: accumulated knowledge, know-how, and intuition gained through extensive experience.|
As the unknown becomes more known, our industry responds with frameworks and even packaged software solutions. But, this does not occur overnight or with the first iteration of a solution. It takes years for this to happen. At one time, for example, a spreadsheet was a completely new concept from a software perspective. Today, it is a commodity.
As we work with customers, we try to get a grasp for how well what they are seeking to accomplish with technology aligns with the business problem they seek to solve and if a package or framework exists that will expedite the solution. We then compare the knowns to the unknowns to suggest solution approaches that, on one end, will be more “out-of-box” but perhaps less aligned with business needs to the other end with more complex adaptations that offer the flexibility to more closely align to the business strategy and promote user adoption. Of course, projected timelines and costs equally span.
If we were to consider that approaching IT Solutions is more like a Physics Exam, we might conclude that the outside assessment of 60% is a more accurate representation of the problem space and not so much the solution process. When looking at solutions with more unknowns in this way, we see that having real “problem solvers” in the solution delivery team is as important to reliability & repeatability as is the proper choice of process, methodology, and if a framework or package applies. It is the resources that do the deep-thinking that’s required to deal with the solution’s unknowns.
Industry often diminishes the human factor by promoting stellar tools that seek to automate/ensure quality. I would suggest that industry research has clearly identified those alone are not enough and perhaps our smoking gun for the poor industry metrics is both not understanding what they really identify (a tough problem space) and the missing leg of the stable solution triad (problem-solving resources).
If organizations seek to better develop and utilize their individuals with Deep Smarts as an active role in their solution delivery, they might realize steeper gains in success metrics.
In a future posting, I hope to fall back to physics to contemplate approaches for problem-solvers to address problem spaces with many unknowns.