Consider what young people are learning in school today. Concepts such as mean, mode, range and probability theory, once taught in freshman university statistics courses, are introduced to children in their early elementary school years. Children are taught these methods in a very practical way. For example, 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 already see the impact of this learning shift with Gen Y, or Echo boomers, who are entering the work force. They are accustomed to easy access to information and are highly self-sufficient in understanding its utility. Following the progression, the next generation after them will not have any fear of analytics or looking toward an "expert” to do the math.

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

Questions for Analytics to Answer

Personally, I am interested to see when the thought-provoking questions listed below might be routinely answered. With business analytics, big data, and enterprise and corporate performance management (EPM/CPM) software the answers may be within reach.

• 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 wouldn’t 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 health care providers synchronize patient appointment schedules to reduce the amount of idle waiting room time? Then you could show up just before your appointment.

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

• Why can’t hotel elevator systems better position the elevators to arrive at floors 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 destination level.

• Why can’t airport passport control managers regulate the number of agents in synchronization with the arrivals of international flights? Then you wouldn’t need to wait in long queue lines while the extra staff shows up (sometimes).

• Why can’t retail stores partner with credit card companies for integrated customer 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 provide instant monitoring and feedback of domestic residences so that households can determine which appliances or events (e.g., taking showers) consume relatively more or less energy? Then households could adjust their usage behavior to lower their utility bills.

• Why can’t personnel and human resource departments conduct 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 be translated into headcount capacity planning by type of skill or job group? Then the workforce on hand would match organizational needs without scrambling when changes or mismatches occur.

• Why can’t the magazines you subscribe to print a customized issue that includes 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 keep in stock 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 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.

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