Intentional networking

This week in OB 565 Leading Change we've been discussing the role of networks in organizational change. For the most part, we're taking off from two articles:

The general idea is that you can map out who-knows-what and who-knows-who to understand how organizations actually work, not just how they're formally organized. Using ORA and observed company data, we get network graphs like this:

That all makes sense to me. It's all graph math based on relationships between nodes. It's similar enough to how we model how faults propagate through a network in aircraft, from the sensors that detect them to the computers that collect and analyze them to the displays that alert the pilots and the maintenance crews on the ground and so on.

The part that isn't sinking in is the idea that organizational networks can be designed intentionally to produce some desired outcome (or avoid some undesired outcome, like having noncommunicating factions within the organization). It all sounds like nice theory—very nice and neat, the kind of thing a professor can write on a board and then, with a swish of the hand, declare it "obvious".

I'm slow. It isn't yet obvious to me how you can go from (a) computational organization design to (b) a meaningful real world change. I believe it, and I can feel it, but I can't see the connection yet to the real world how-to yet.

The two examples that come to mind that are the nearest to what I am most often concerned with:

  1. How to organize teams at work in such a way that the many components and subsystems and software and test kits and so on can get done in an "optimal" way. (The scarequotes mean: what is optimal? Fastest? Cheapest? Most robust organization that won't fail due to turnover or difficult technologies not getting ready in time?)
  2. What skills should I learn and who should I meet to achieve some goal?

So I'm looking around for a bit more practical information about the how-to. I don't need any papers that explain the graph math of different organization structures. It's not even really about considering the pros and cons of different structures. It's more like: (a) how do you really know what structure you have, and (b) how do you know what structure you should have, and (c) how do you perform small experiments to discover a good path from (a) to (b)? I suspect it's right there in front of me in these articles and papers, but I'm too dense to get it. It's an interesting problem and I'd be surprised if there's a tried-and-true method to do it.

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