The data-driven workplace of the future
The next craze in big data has to do with where you work.
“When employers have the capacity to monitor you, they will,” says Esther Kaplan. She took a look at how various industries, from shipping and transportation to retail to freelance work, has changed since the introduction of big data tracking to the workplace.
Her piece, “The Spy Who Fired Me” appears in the March edition of Harper’s Magazine.
Specifically, Kaplan wrote about telematics, a combination of information that could include everything from GPS data to sales information. Employers use telematics software to decide how to schedule, hire or even fire workers.
But measuring workers based on specific metrics can have some unintended consequences, says Kaplan. For example, she followed UPS drivers who have gotten into the habit of buckling their seat belts behind them when they drive. The vehicle logs that the seat belt has been buckled, but the drivers are able to sprint in and out of the trucks faster to meet their delivery quotas.
“So you’re maximizing certain metrics, but all kinds of incidental side effects are not being measured, not being captured, and may be getting worse,” Kaplan says.
Read an excerpt from Kaplan’s report in Harper’s Magazine below:
Whenever you drive up to a McDonald’s window, or push your grocery cart to a Stop & Shop checkout line, or head to the register at Uniqlo with a blue lambswool sweater in hand, you, too, are about to be swept up into a detailed system of metrics. A point-of-sale (P.O.S.) system connected to the cash register captures the length of time between the end of the last customer’s transaction and the beginning of yours, how quickly the cashier rings up your order, and whether she has sold you on the new Jalapeño Double. It records how quickly a cashier scans each carton of milk and box of cereal, how many times she has to rescan an item, and how long it takes her to initiate the next sale. This data is being tracked at the employee level: some chains even post scan rates like scorecards in the break room; others have a cap on how many mistakes an employee can make before he or she is put on probation.
Until recently, most retail and fast-food schedules were handmade by managers who were familiar with the strengths of their staff and their scheduling needs. Now an algorithm takes the P.O.S. data and spits out schedules that are typically programmed to fit store traffic, not employees’ lives. Scheduling software systems, some built in-house, some by third-party firms, analyze historical data (how many sales there were on this day last year, how rain or a Yankees game affects revenue) as well as moment-by-moment updates on the number of customers in the store or the number of sweaters sold in the past hour or the pay rate of each employee on the clock — what Kronos, one of the leading suppliers of these systems, calls “oceans of valuable workforce data.” In the world of retail, all of this information points toward one killer K.P.I.: labor cost as a percentage of revenue.
In postwar America, many retailers sought to increase profits by maximizing sales, a strategy that pushed stores to overstaff so that every customer received assistance, and by offering generous bonuses to star salespeople with strong customer relationships. Now the trend is to keep staffing as lean as possible, to treat employees as temporary and replaceable, and to schedule them exactly and only when needed. Charles DeWitt, a vice president at Kronos, calls it “the era of cost.”
Workforce-management technologies make productivity visible and measurable, allowing employers to distinguish between labor time that generates profits and labor time — down to the minute — that does not.
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