Paralysis of Analysis
Last week I talked about garbage-in, garbage-out with respect to data-based decision making. Another symptom I have seen in many organizations is what I call the paralysis of analysis. All that data is so interesting that it can be almost hypnotizing. I’ve had situations where I’ve done exhaustive analysis and provided reams of data only to be asked to go out and get more. I came to realize that no matter how much information I provided, there was never enough to reduce the risk or provide enlightenment to actually move forward in a bold way. What generally resulted were half-funded initiatives where the original goals were kept but with inadequate resources. On the other side is action. Why wait for every piece of data before trying different things to either optimize or grow an initiative? I had a recent personal experience that drove this home for me.
I’m an avid cyclist (actually, I’m avid about cycling, swimming, and general exercise). About 4 years ago I bought my first carbon fiber road bike to replace my venerable Lemond Zurich steel framed bike. I was sure that my performance would improve on this new bike as, not only was it outfitted well, but I had it professionally fitted. Over the four years I’ve never been really happy with my speed and hill-climbing on this bike. To be honest, I mostly blamed it on me getting older and just can’t expect the performance I used to have. Then last weekend midway thru my ride, I decided to raise my seat height by ¾ of an inch. The result was dramatic. On the way back were the two toughest hills of the ride. I reached the top well faster than I ever have and with much less fatigue than I would usually feel. Then I passed 3-4 riders who had passed me on the way out. I couldn’t believe that all this time I had put up with the status quo and by just making a minor adjustment I achieved fantastic results! How does this relate to organizations?
Back to data. Data-centric decision making can be good if you are looking at the right data. However, sometimes it is more effective to make tweaks to an organization or process or business model and just see what happens. Experimentation can be a good thing and can deliver dramatic results. Another thing I have found is that people who are affected by change will have a tendency to overstate the negative effects of the change. Of course, for the leader, a major part of the job is to assess risk, and then to initiate action. I believe that there are many instances that data analysis is used to inhibit action rather than to spur change.
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