This week, two LinkedIn posts and conversations and one MOOC made me reflect upon the way we deal with complexity. This week was all about knowledge, the complexity of the world we live in and more specifically the complexity associated with knowledge capture and knowledge transfer, and the hidden dangers of simple solutions.
- Nick Milton's post, "When "Copy Exactly" Pays Off in KM" discusses one possible strategy for going from development to large scale production from a knowledge transfer perspective. The argument is that if you don't fully understand a complex system but you know that it works, don't mess with it. If the black box works, don't open it up to try to understand how it works. Just find a way to replicate the black box.
This may work in a very narrow range of contexts. I just helped write a lesson learned in a very different context that suggested something completely different: Beware a calling something a "rebuild" or a copy and assume the copy will be cheaper to build because we now know how to build it. Obviously, context is critical. I'm talking about rebuilding an instrument for a space mission. Nick was talking about mass production of computer chips.
In yet another context, international development, the idea of piloting an activity somewhere on a small scale and then implementing a "copy-and-scale" either in the same country or elsewhere is going to raise a few red flags. Any push to scale or repeat in another geographical, economic, political and cultural context will likely require some "adaptation" when transferring knowledge. In an international development context, I'd be very weary of replicating something just because it works somewhere, without understanding why / how it's working.
- In another LinkedIn conversation, Chris Leljedal asked about best practices for collecting tacit knowledge from retiring employees. Of course, there are a number of ways to do it and again, the context is going to be critical in deciding what approach is both feasible, practical, and likely to yield the best results. I would like to argue, however, that it would help to step back, pause for a second (or two) and ask a few other questions.
For example, how did these retiring employees share their knowledge throughout their careers? The image that is perpetuated of "knowledge walking out the door" is a little misleading in my view. Hopefully these retiring employees did not work in isolation and they've shared their knowledge through ongoing interactions with colleagues and through the normal workflow. Putting the emphasis on trying to capture critical knowledge before employees leave is the wrong approach. There's nothing wrong with giving retiring employees an opportunity to celebrate their career and tell a few good stories as a way of transferring some last minute career lessons, but it's too little too late if the knowledge hasn't circulated through the organization already.
This is where knowledge management meets human resources and talent management, where you can deal with the immediate threat of knowledge loss due to retiring employees with a relatively simple set of KM best practices, but the better option would be to look at the broader, more complex issues associated with long-term knowledge acquisition and knowledge retention through a human resources perspective in addition to the KM perspective. I'm sure someone would also come in with an IT solution but I won't go there.
- Finally, the MOOC I was keeping up with this week was "L'avenir de la décision: connaitre et agir en complexité" (Translation: The future of decision-making: Knowing and Acting in Complexity). The key insight I gathered was that we shouldn't ignore complexity and come up with simplistic solutions just because it's difficult and we shouldn't thrown the towel either. We need to recognize complexity, accept it, but don't let it paralyze you. Move forward with simple, short- and medium-term practical solutions based on realistic expectations and humility and keep trying to understand how all the pieces of the puzzle fit together (the complexity behind everything). (see my puzzle post of late August and whether it ties in)
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