As Americans, we love a good scare. We love dressing our children up as their favorite super heroes, villains, and princesses and parading them around the neighborhood during trick-or-treat. We’ve developed quite a figurative sweet-tooth for everything that has to do with Halloween. The corn mazes, haunted houses, elaborate decorations and undeniable joy we get from torturing our dogs and cats with their own costumes add to the magic of 21st century Halloween celebrations… but at what cost?
How early is too early to introduce quality into your everyday life? Have we missed out on improvement opportunities in our personal lives along our paths to achieving our career goals as quality professionals? These questions have me pondering about how life could have been different for me growing up with a little more emphasis on data analysis for improvement.
In the age of advanced data analytics, sports statistics abound. Take professional sports, for example. It may be common knowledge that basketball player Michael Jordan is revered as the greatest scorer of all time – averaging a record 30.12 points per career NBA game. We know, by virtue of statistics, that Drew Brees is the most accurate quarterback of all time sporting the highest career completion percentage on pass attempts in the NFL. What about Baseball hitting metrics? A great hitter scores high on per-season statistics such as batting average, on-base percentage, and slugging percentage (total bases achieved divided by number of at-bats). Regardless of sport, tradition suggests that statistics are best consumed as a summation of effort over a season or career, and rarely do we compare year-over-year or game-to-game statistics with any value. Further, results analysis is generally Pass/Fail in nature. Did the athlete succeed? Is this number successful? Why or why not? By the time these questions are answered, it’s too late to do anything about it!
In the world of quality, we know better! We understand that any process – even sports – can and should be considered as a process over time if we want to continuously improve. After all, professional athletes are surely striving for their numbers to go up over time. It would be interesting to focus less on “athletic doing” and more on “athletic learning.” What’s important in Statistical Process Control is that we ask the right questions and understand the variation in the process actually being monitored.