If it’s Wednesday, it’s Pratt Statistics Day!
I’m at our Global Sales Kick Off, so I think it’s Wednesday. These meetings are a blur, and through the time smear, I’m glad I have another guest post cued up from data-queen Lisa Pratt. This is Lisa’s 4th guest post on wicked big data, and while she goes there again today, she also publicly comes clean about her sugar addiction. Thank you for your courage, Lisa.
By Lisa Pratt
I have a sweet tooth. Lots of people like sweets and consume too much sugar, so I didn’t think my sugar issue was any worse than “average”. I am an analytical person both by nature and by career choice, so I decided to do some hypothesis testing and was shocked by what I learned.
For a period of several months, I had been keeping a food diary on myfitnesspal.com. In addition to calories, it tracks sodium, carbs, protein, and sugar relative to a daily target. So, fortuitously, I had data available. Not surprisingly, I was over the sugar allotment every day. I thought it was because I eat several pieces of fresh fruit per day. So, I subtracted the sugar from fruit, veggies, and other naturally occurring sources such as milk, and calculated the % of my daily caloric intake that came from added sugar, assuming I would be fine. Apparently, 25 – 30% added sugar is NOT ok. In fact, the World Health Organization recommends 5% of calories from added sugar as a goal and the average American consumes 15% of calories from added sugar. I consume double the added sugar of the average American and 5 to 6 times the recommended amount! Clearly, without the pre-disposition to measure, I would not have uncovered the magnitude of my sugar intake problem and put a plan in place to manage it before it impacted my life and health.
I bring this up to highlight the power and usefulness of measurement and the golden nuggets that can be uncovered with data that you may already have. There is almost always plenty of data available, even if it is imperfect or collected for another purpose. Organizations are often sitting on a gold mine of operational data that could be used to spot trends, uncover potential dangers, or identify areas for higher revenues or lower costs – but is instead only used for regular reporting. Reporting is useful for tracking what is already known and deemed to be important to the successful running of the business. Companies that can proactively analyze their wealth of data assets to bring opportunities and issues to light that they didn’t previously know about are the ones that will have a competitive advantage.
One of the most untapped areas ripe to benefit from Big Data and Analytics is Workforce Management. Workers are the lynchpin of making any company run smoothly. Finding the right equilibrium of workers, skills, and schedules can have a huge impact on profitability. Static reporting can not uncover staffing anomalies and are often available too late. Mining workforce data using more sophisticated tools, such as Workforce Analytics, allows managers to customize the view into current data so they can plan better up front and make quicker adjustments when problems arise. The efficiencies gained go straight to the bottom line.
As organizations evolve to be more numbers driven, the ones that have an appetite for trusting data rather than waiting for their intuition to kick in will be steps ahead of their competitors. What data does your company collect that can start you on a more analytical approach to optimizing your workforce? What analysis can you do with it to make better decisions?