Allegorical Analytics Fra Diavlo – Hold the Sausage
I’m hunkered down preparing for KronosWorks (and hopefully a KISS show) beginning Saturday, so I’m very thankful Lisa Pratt cooked up another serving for Pratt Statistics Day! This is Lisa’s 5th guest post on wicked big data, and today she tells a story about how to turn “boring” numbers into a, uh, story. Thanks, Lisa!
In order to engage an audience, there must be a compelling story. That is true in movies, television shows, books, blogs, and presentations. And, it is certainly the case with analytics. Nothing puts a non-data geek audience to sleep faster than a bunch of numbers. For those who can stay awake, a number by itself isn’t interesting or actionable, but it is forgettable. This is unfortunate because coming up with those numbers can be time consuming and expensive. There is no doubt that more data is available than ever and there are even tools that will mine the data for you. But that doesn’t mean that there are more insights or that better decisions are being made. It just means that there are more numbers.
A friend who lives an exciting life in show business once said to me: “Tell me what your job is again. I always ask, but it sounds boring so I don’t really listen.” Clearly, I thought, my personal elevator pitch needs some tuning! Every day I help my company learn things they didn’t know before so they can make educated decisions that shape our strategy, customer experience, products, etc. That sounds pretty exciting to me! I realized that, in the words I chose, I suffered from the same mistake that I have cautioned many of my employees and mentees about over the years. I needed to tell a story. No one cares about the sausage making that goes on, it is more interesting, and less gross to know what happened to the sausage, post-production. (For the record, I hate sausage.) Rather than mentioning the number crunching, it is much more compelling to talk about how the data and analysis I provide is used to help my company decide what new markets to enter, which products to hype, which customer segments need more love, how to position our brand, etc…
As companies build up their stores of data, and analytical tools, and analysts to use the data and tools, they need to be careful to make sure there are plenty of good story tellers on staff. Let’s look at an example of what happens when analytics is approached analytically vs allegorically.
Company A has a robust data base, a suite of analytical tools, and a team of statisticians culling through the data. At the year-end meeting, the VP of Numbers gets up and shows a chart with upward trending labor costs, labor as a % of revenue, and turn over. Based on the numbers, his recommendation is to have managers crack down on overtime and find ways to more cheaply deliver training for waves of new employees coming on board as a result of turnover issues. Managers are told to look for ways to skim hours from schedules, however, this is negatively impacting revenue. The executives get back together and start pointing fingers.
Company B has a robust data base, a suite of analytical tools, including analytics to increase visibility to their workforce, and a team of statisticians working alongside of business experts to ask the question “so what”? At the weekly executive update meeting, the Chief Story Teller gets up and shows the upward trend of labor costs, labor as a % of revenue, and turnover. She then goes on to describe how the mix of full time, part time and temporary workers has shifted over time. These shifts have also been accompanied by a reduction in productivity output even as overtime has increased, indicating that scheduling of certain skills has not been adequate on some shifts. She then shows how different combinations of skills and full time/part time/temporary workers can lead to different levels of productivity and where additional training on certain skills will provide more flexibility in scheduling for both Company B and for its workers. Given demand for Company B’s offerings, the Chief Story Teller is able to pinpoint the optimal combination where labor supply equals what is needed to meet all customer demand. Because she was able to clearly articulate and support the story, her recommendations were put into action. Company B was able to increase profits.
Would you rather be at company A or company B? And, do you have what it takes to be the Chief Story Teller?