I know this video is old, but I remembered today how angery it made me. This is me calling it out.
I’m a data nerd and as much as I hate to admit it the first thing that happens when I see a video like this I get pulled into the numbers. I immediately begin to back track and comprehend how they came upon the random slew of statistics.
Then inevitably I get angry and begin to realize that these statistics are either immeasurable of completely ridiculous. Take from the piece above. “India has more honors kids than America has kids.” How can you measure that? Does the US or India have a way to measure honors kids? And if they do does there definition of an honors student equal the same thing? And as my mind begins to spin off into a tizzy of possible measurement scenarios, I realize the slow drip quicksand has caught me-out of context data vomit.
It’s hard not to fall into the trap; our society routinely practices data bulimia. We choke down stakes of facts and figures and just regurgitate them out often with little planning. It hard not to with all the new ways to collect data; even the most useless facts seem alluring. Did you know that Mosquito’s are attracted to the color blue twice as much as to any other color?
But numbers are useless without a well framed purposed and without context statistics are not actionable. For example, let’s say Fred’s Fish Bar does a survey of customers and finds delivery to be the least satisfactory service in the restaurant. They spend a large portion of their next quarters budget revamping the delivery service only to see no change in results.
This is the set up for the classic contextual faux pas. Digging deeper into the data find that while delivery WAS the lowest scoring service, it was only marginally lower. Also, the survey was only administered to people who visited the restaurant for a sit down dinner-those who may not find as much value in delivery. Also, they find that while family dinning wasn’t the least satisfactory service, it was significantly lower among women. Perhaps the investment would have been wiser spent creating a family friendly perception of the restaurant targeted at women. But, who knows because this is all hypothetical- ie I made it all up.
But the issues addressed in the fantasy example all too easily plague real world businesses. How do you get data that isn’t just compelling, but actionable? It’s simple ,build a frame work. Take time to invest thought into the following 3 questions:
1. What are you looking to measure, and why?
2. What do you plan to do with results of your research?
3. Can this metric be backed up by any additional data?
