Assumptions about monitoring
Hi Michael, thanks for you kind comments about my blog. As you’ve probably gathered, my thoughts on monitoring are developing so I appreciate your questions. Take the following comment you make:
One question that comes to mind immediately is an extension of his base assumption that there’s an optimum level and pace of monitoring given a particular context. This suggests in turn that overmonitoring can be as much of a problem as undermonitoring.
Getting the balance right is tricky and I guess this is why I titled the post ‘The Art of Monitoring’. I don’t think there is an optimum level and there would be no way to really tell. In the complex domain you are looking for ‘good enough’.
You suggest, by omission, that monitoring doesn’t make sense in the complex domain: “Monitoring certainly makes sense in the known and knowable domains of the Cynefin model, when an organization’s context and activities are reasonably reduced to linear and causal models of behavior.” I hold the view that monitoring is essential in the complex domain for the simple reason that each intervention only makes sense in hindsight and therefore you have to have a look to see what happened. Of course just by having that look you are changing the system.
Shawn, author of Putting Stories to Work, is one of the world's leading business storytelling consultants. He helps executive teams find and tell the story of their strategy. When he is not working on strategy communication, Shawn is helping leaders find and tell business stories to engage, to influence and to inspire. Shawn works with Global 1000 companies including Shell, IBM, SAP, Bayer, Microsoft & Danone. Connect with Shawn on:
Comments
Comments are closed.
Agreed that monitoring is an art in any phase. There’s no simple universal answer to how much monitoring of an organizational system is enough, or when it’s best done. And, as you suggest, the best answer at one point in time might be wholly wrong weeks or months later.
My suggestion was more that in known and knowable domains, the complicated but not necessarily complex nature of the problems at hand lend themselves well to a streamlined and rational program of monitoring and assessment.
It is at least logically feasible in these domains to suggest that a predictive model can be generated based on the selection of the right variables to monitor and the right times and methods to engage in that monitoring – however one defines “right”, which is a matter of significant debate in itself. There’s probably an ideal optimum level, but even in known and knowable domains, we settle for “good enough”.
Conversely, in complex or chaotic domains, such a regimented program of prediction oriented measurement begins to unravel. My point above is more that tweaking a regimen of monitoring does not help and indeed can lead to delusions of control, which may even be worse than no control orientation at all.
As you suggest, monitoring does not and should not cease in these domains – but its nature and application likely changes. My guess is that it should likely be less regimented, more qualitative , more holistic, and more focused on information gathering for future retrospective analysis vs. information analysis for immediate application. “Good enough” may be the best you can aim for.
The primary response for some in times of unboundable complexity is to work overtime to contain it, whereas the appropriate answer might be to simply immerse one’s self in the experience and learn what one can from it for later application. That said, unfortunately most people aren’t encouraged to sit back, watch and reflect on the job, especially in times of crisis.
An interesting question, one that I’ve tried to wrestle with myself to varying levels of success…
http://engineerswithoutfears.blogspot.com/2005/02/boot-on-monitor.html