We'd like to think that if we could find the right algorithm, we could make predictions with 100% accuracy. The truth is that certain concepts are difficult to quantify. This is especially true when it comes to human behavior. Companies like Netflix have awarded a million dollars to make their movie recommendations just a tiny bit more useful. Amazon spends millions tweaking their recommendations in the hopes of increasing purchases. It's much more challenging to use data to alter human behavior in the workplace.
An increasing number of companies are turning to deep analytics in an attempt to transform their workforces into powerhouses. Despite incredible efforts, it seems that it isn't that easy to find helpful insights. This article from the Atlantic discusses some of the challenges facing the many companies who are attempting to harness company information in search of productivity gains:
But as the industry grows, big questions remain about what can be done with this newly discovered trove of data. Bersin's research shows that only four percent of large companies can make meaningful predictions about their workforces, while 90 percent can accurately predict business metrics such as budgets, financial results, and expenses. Can human-resources analytics do enough to capture the behavior and preferences of its endlessly complex subjects: humans?
"It’s one of the few areas of business that hasn’t really been figured out yet," says Bersin. "People are imperfect machines. Nobody ever figures out people completely."
But that doesn't mean companies aren't going to try. On the Big Data front, the company VoloMetrix mines calendar and mailbox data to determine over a hundred predictive indicators. From those indicators, the company works with clients to determine how to solve a given problem, from determining what makes a great salesperson to how emails can be more efficient.
There have been some surprises along the way:
Some of the surprising results VoloMetrix has found from client datasets challenge conventional workplace wisdom. For example, for a client that wanted to know when the best time of the day was to have meetings, VoloMetrix looked at how disengaged employees were by seeing how many emails they were sending during meetings. At 9 a.m. meetings, roughly 8,500 emails were sent, while meetings at 6 p.m. were only slightly better at 7,000 emails. Meanwhile, employees in meetings between 10 a.m. to 2 p.m. didn't send very many emails—so the company rescheduled for the middle of the day.
There are other interesting findings from "small data" surveys:
As for the problems that Big Data can't solve, small data might help. The company TINYpulse works with 500 companies to take feedback surveys, typically a yearly chore, and turn them into a weekly, anonymous, one-question pop-up. Some of the questions they've asked have garnered some very unconventional, but perhaps incredibly honest, answers. For example: the question “If you were promoted to be your boss's manager in the new year, what's the first thing you would change?" The most popular answers ranged from traditional answers such as better pay and hours, to firing and demoting employees who were dead weight. Another unconventional question TINYpulse asks to measure workplace satisfaction is whether employees have interviewed for another job in the past three months.
But all of this information still has to be weighed carefully:
"It’s very tricky. People data can be very misleading," says Bersin. "The data won’t necessarily tell you everything: You have to interpret it, know what it means, and try to make sense of it. It’s not like you can sit in a black box and look at the data."
Here is the link to the full article: The Quantified Workplace: Despite the Hype, Not All That Useful Yet — The Atlantic
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