Provocative questions for analytics to answer

Consider what young people are learning in school today. They are taught mean, mode, range and probability theory in their freshman university statistics course. Today’s children have already learned some of this math in the third grade! They are taught these methods in a very practical way. If you have x dimes, y quarters and z nickels in your pocket, what is the chance of you pulling a dime from your pocket? Learning about range, mode, median, interpolation and extrapolation follow in short succession.

We are already seeing the impact of this learning with Gen Y/Echo boomers who are getting ready to enter the work force. They are accustomed to having easy access to information and are highly self-sufficient in understanding its utility. The next generation after them will not have any fear of analytics or looking toward an “expert” to do the math.

Given that these analytical capabilities are becoming commonplace, there is a broad range of problems and opportunities that can be addressed that were unimaginable to be tackled only a few years ago.

I am interested when the questions listed below might be routinely answered with business analytics, big data, and enterprise and corporate performance management (EPM/CPM) software:

• Why can’t traffic intersection stoplights be more variable based on street sensors that monitor the presence, location and speed of approaching vehicles? Then you would not have to impatiently wait at a red light when there is no cross-traffic.

• Why can’t a call center route your inbound phone call to a more specialized call center representative based on your phone number and your previous call topics or transactions? And once connected, why can’t that call rep offer you rule-based offers, deals or suggestions most likely to maximize your customer experience? Then you might get a quicker and better solution to your call.

• Why can’t dentists and doctors synchronize patient appointment schedule arrival times to reduce the amount of wasted time that so many people collectively have to idly sit while in the waiting room? Then you could show up just before your appointment.

• Why can’t airlines better alert their ground crews for plane gate arrivals? Then passengers don’t have to wait, sometimes endlessly, for the jet bridge crew to show up and open the airplane’s door.

• Why can’t hotel elevators better position the floors the elevators arrive at to pick up passengers based on when hotel guests depart their rooms? Then you don’t have to get stuck on a slow “milk-run” elevator stopping at so many floors while an “express” elevator subsequently arrived and could have quickly taken you to your selected floor.

• Why can’t airport passport control managers regulate the number of agents in synchronization with the arrivals of international flights? Then you don’t have to wait in long queue lines only to have the extra staff show up (sometimes) much later.

• Why can’t retail stores partner with credit card companies and their transaction histories and use algorithms like Amazon.com and Netflix do to suggest what a customer might want to purchase? Then you might more quickly find what you are shopping for.

• Why can’t water, gas and electrical utility suppliers to home residences provide instant monitoring and feedback so that households can determine which appliances or events (e.g., taking showers) consume relatively more or less? Then households could adjust their usage behavior to lower their utility bills.

• Why can’t personnel and human resource departments do better workforce planning on both the demand and supply side? That is, for the supply side, why can’t they predict in rank order the most likely next employee to voluntarily resign based on statistical data (e.g., their age, pay raise amount or frequency) of employees who have previously resigned? For those who will retire, isn’t this predictable? For the demand side, why can’t improved forecasting of sales volume and mix be translated into headcount capacity planning by type of skill or job group? Then the workforce on hand will match the needs without scrambling when mismatches occur.

• Why can’t magazines you subscribe to print at the time of production a customized issue for you that has advertisements (and maybe even articles) tailored to what you likely care more about based on the profile they may have about you? Then the magazine’s content may be more relevant to you.

• Why can’t your home’s refrigerator and food pantry keep track using microchips and barcode scanners of what you purchased and the rate of usage? Then you could better replenish those items when out shopping.

Are these a vision of the future? Not in all cases. With business analytics software and communication technology some, if not all, of these questions are already solvable. Analytics not only proves or disproves an analyst’s hypothesis, but its truth-seeking tests also reveal cause-and-effect relationships. Understanding causality serves for making better decisions by reducing uncertainty.

It is a complex world that we live in. It is now time that gut-feel, intuition and guessing be replaced with applying analytics to better manage organizations and better serve their customers.