In The Hitchhiker’s Guide to the Galaxy by Douglas Adams, ‘the answer to the Ultimate Question of Life, the Universe, and Everything, calculated by an enormous supercomputer named Deep Thought over a period of 7.5 million years, is 42′. The meaning of life is … 42.
Maybe in organization life, the meaning of life is 37.
One of the problems we have in organizations is to decide when enough data is enough in order to make decisions and move next. In my organizational world, as organization architect, the teams I lead or advise, sometimes hesitate on the number of people interviewed, or the right number of focus groups, for example. Most people agree that there is an invisible threshold beyond which no more ‘new data’ comes in and the number of insights reach a plateau or even decrease. Whether market research, customer insights or employee insights, it’s always the same. Yet, many people keep going to ‘complete the study’.
Although in different worlds, it may be useful to take into account ‘the 37% rule’, also known as ‘the secretary problem’. You interview a finite number of candidates (to be a secretary). You rank each on their own merits. You then stop at 37% of the total numbers you plan to interview, and from then on, you select/hire the next one who is better than anybody else seen so far. It’s also known as the ‘Stopping Rule’ or optimal stopping.
It has been applied to dating! Why not? Date the first 37% and marry the next one who is better than all of the previous one so far. Don’t bother to continue dating.
The rule has multiple twists, applications, folk notions and theoretical mathematics behind. Known from the 50’s, it was not written down until 1960. There is plenty of background you can explore asking Mr. Wikipedia. For the dating saga, see this piece in The Washington Post.
All this applies to ‘selecting’ but, for me, it has a broader conceptual territory that is one of ‘optimizing’ your data sources and knowledge. It would be theoretically incorrect to apply the 37% rule to my focus group scenario above, but the issue of ‘when to stop’ remains a good one. (37% of them? just kidding)
This is very different from ‘statistically significant’ numbers of interviews and data points. For me ‘the stopping rule’, the stopping algorithm, is part of our natural heuristics, often translated as ‘I think I have enough of these’.
When to stop (in interviewing, sampling, asking people, accumulating data) is as important, if no more, as having plan for continuing, for constantly extending your possibilities of data or insight sources.
Some kind of Stopping Rule, is overdue in our day to day management of the organization. Back to our heuristic brain, where else?
37, the meaning of organizational life is 37.