A series of examples highlighting the cross-pollination of analytical methods and ideas.
Diversity fuels innovation. The joke about the physicist, chemist, and statistician also suggests that at times diversity impedes progress:
These three scientists are called to a room where they see a fire in the wastebasket. The physicist says, “I know what to do! We’ll cool the materials until their temperature is lower than the ignition temperature and then the fire will go out.” The chemist says, “No! No! I have a better idea—we’ll cut off the supply of oxygen so the fire will go out due to lack of one of the reactants.” While the physicist and chemist argue, they notice the statistician running
around the room starting more fires. They both scream, “What are you doing?!” To which the statistician replies, “Trying to get an adequate sample size.”
This reflects the different perspectives these 3 analytical thinkers have—the kinds of problems they are exposed to in their area of study and the ways they are used to framing them. We need this diversity of thought, ideas, and views to see things in new ways that may lead to better approaches and outcomes. This cross-pollination of ideas is vital to progress.
One of my favorite quotes is from the famous statistician, John Tukey: “The best thing about being a statistician is you get to play in everyone’s backyard.” Statistics helps us understand variation and uncertainty so we can better explain phenomena, better understand our world, and be more informed in the myriad decisions we make every day. In a world of growing complexity and accelerating change, we need more and better ways to deal with uncertainty.
Making decisions in the face of uncertainty draws heavily on statistics. More broadly though, analytics includes other related disciplines to help us solve a wider variety of problems. For example, operations research helps us factor in goals and constraints to make better decisions. Simulation helps us assess different conditions and the impact of change. Probability theory, from which basic concepts of statistics originate, allows us to deal with random phenomenon in building models of processes, systems, behaviors, etc. The history of these and other related disciplines (which are synergistic yet distinct) is full of examples of cross-pollination and resulting innovations. We want to highlight some relatively recent and practical examples of cross-pollination and innovative problem-solving, drawing on the many ideas and methods contributed to and borrowed from multiple analytical disciplines.
We Stand on the Shoulders of Giants Every Day
There are many examples where an idea to solve one problem is reused and extended to solve other kinds of problems. In this series, we share a few examples of some noteworthy cross-pollination of ideas and methods in analytics–we hope they inspire your thinking and lead to innovations of your own.
