Live life in the intersection
I’ve been thinking a lot about Venn Diagrams lately. Or, more accurately, I’ve been giddily appreciating Venn Diagrams lately.
If you’re unfamiliar, they’re well outlined here.
As a way of ordering the universe I’m thinking (at least lately) that Venn Diagrams can’t be beat. The basic premise (in my musings) is that if a circle encloses all the members of a certainy category like all blonde-headed people, or all gardeners, or all circus clowns; and then another circle encloses all the members of a completely different category, like all lovers of books more than 1000 pages, or all people who like asparagus, or all bloggers that meander from weird topic to weird topic; well then what exciting possibilities exist for understanding our universe in the intersection of those populations?!
- What do we think about blonde-headed people who like asparagus?
- Is there a market to sell things to gardeners who love books longer than 1000 pages?
- Gracious, what conclusions can we draw about meandering bloggers who are also circus clowns?
As a lens through which to view the world (literally…a two-population Venn Diagram looks kind of like glasses, no?) Venn Diagrams seem to offer infinite possibilities.
If we are (as some of us do) measuring training:
- What conclusions can we draw about managers who’ve been in their role for more than 10 years AND engage in athletic hobbies?
- What about new hires who have experience in food-service (I always favored these folks as a hiring manager)?
- What about training held on a Monday compared with the same training held on a Wednesday?
In the same vein, what if we take the population of managers in a company for whom their team turnover is higher than the company average and investigate to see what else is true for them? What’s the size of the intersection for managers with high turnover and managers who work first shift? Second shift?
Measurement isn’t just about collecting numbers and crunching them around to see what they tell us. Sometimes the most useful measurement activity is thinking about all the possible populations we’re faced with and imagining how they intersect in order to imagine the story behind it.
- What if 1st shift managers in a production environment have higher team turnover? What hyphothesis do we form?
- What if leaders with MBAs have shorter overall tenure than leaders without MBA’s? What hypothesis?
- Do male new hires score higher on 90 evaluations than female? Whats that hypothesis?
Venn Diagrams. Bringing order and clarity. Try it…what are the population intersections you’re most interested in?
~Geek~
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Greg, as usual, awesome. Often one of the larger issues in training is “I need to measure stuff”. Often (or at least, hopefully) the measurement strategy is seeking to confirm in some objective manner a result (or relative impact of said result) relative to its means (in our case, “the training”). Yet we often discover related, or even non-related, patterns of correlation- these patterns may often be more powerful, more actionable, and may even provide ‘better’ and/or additional results than the initial results we were seeking in the measurement exercise.
The key elements that I recommend for any measurement strategy are:
1. Get one- understand what you are seeking to prove, and why
2. It’s not a yes/no question… data, and patterns in data, are powerful
3. Observe pattern, apply to ‘why’ rather than just ‘what’
4. We’re not in stats class, it’s okay to be both inductive and deductive at the same time on the same data set