There’s a lot of talk these days about complexity in enterprise IT. The heterogeneous solutions we’re building today seem more complex than the monolithic solutions of the past. But are they really? I’ll grant you that a lot of what is being built at the moment is complicated. Complex though? I don’t think so. The problem is that we’re building new things in old ways when we need to be building new things in new ways.
I’ve always used a simple rule of thumb when thinking about complexity. Some folk like to get fancy with two and three dimensional models that enable us to ascribe degrees of complexity to problems. While I find these models interesting, my focus has always been on how do I solve the problem in front of me. What is the insight that will make the hard easy? For me, one simple distinction seems to provide the information I need. Is a solution complex? Or is it complicated?
Something is complicated if the model we use to understand the problem requires patches, exceptions to make it work. The model might be simple and well understood, but we’re forced to patch the model for it to succeed when confronted by the real world; we’re adding epicycles. It’s not a complex system, but it is complicated.
On the other hand, something is complex if it’s difficult to develop a consistent model for the problem. While we might have a well understood model, it’s definitely not simple, requiring a great deal of academic and tacit knowledge to understand. There’s no epicycles, but there are a large number of variables involved, and their interactions are often non-linear.
While this binary separation might not be strictly true (the complicated can sometimes be complex), I find that the truly complex problems are rare enough that the rule of thumb is useful most of the time. After all, that’s what a rule of thumb is. The few times that it breaks down, experience comes to the rescue.
Distinguishing between the complex and the complicated is not hard; just look for the epicycles. Planning engines – such as material planning or crew scheduling – are a good example of a complex solutions. Business processes management is a good example of a complicated solution. Psi calculus – the model at the heart of a modern BPM engine – is well understood and BPM engines work as described. However, managing business exceptions is a mess as support for them is tacked on rather than an inherent part of the model. Smashing together psi calculus, transactions and number of other models has resulted in epicycles.
Most of the problems we’re seeing in enterprise IT are complicated, but not complex. Take the current efforts to create IT estates integrating SaaS and public cloud with more traditional enterprise IT tools, such as on-premesis applications and BPO. Conventional approaches to understanding and planning IT estates are creaking at the seems. The model – the enterprise integration patterns – which we’ve used for so long is well understood, but it’s creaking at the seams as we bolt on more epicycles to cope with exceptions as they arise.
A great piece of advice from a former lecturer of mine always comes to mind in situations like this. As I’ve mentioned before:
If you don’t like a problem, then change it.
Our solution is complicated because we’re trying to solve the wrong problem. We need to change it.
The problem with BPM is that business exceptions are not exceptional, but are simply alternative ways of achieving the same goals. To resolve the epicycles we need to shift the problems centre of gravity, moving the earth from the centre of the universe to a more stable orbit. If business exceptions are not exceptional, then we should simply consider them as different business scenarios, and use a scenario based approach to capturing business processes. The epicycles then melt away.
I think we can use a similar approach to help us with the challenges we’re seeing in today’s IT estates, the same challenges which are trigger some of the discussion on complexity. The current approach to planning and provisioning IT is data centric; most applications are, after all, just large data management engines. This data-centric approach is forcing us to create epicycles as we attempt to integrate SaaS, cloud, and a whole raft of new tools and techniques. The solution is to move from a data-centric approach, to decision-centric approach. But that’s a different blog post.
Tags: BPM, Calculus, complexity, consistent model for the problem, cynefin, Deferent and epicycle, Economic model, epicycles, Ethology, heterogeneous solutions, KK Pang, lecturer, monolithic solutions, on-premesis applications, Orbit, psi calculus, Science, Structure, Systems