This should not be shocking news, especially for anyone who has watched both outsourcing and the rise of Asia, especially China, as a manufacturing platform.
What is beginning to change in the era of big data is the number and type of factors being quantified, analyzed and included in calculations of the relative worth of various strategic options.
Enterprises are no longer limited to the cost of wages and benefits (in those rare instances where benefits are still offered). Now, institutions can identify and measure traits like commitment, happiness, positive interactivity, moodiness and enthusiasm. The contribution of any factor to desired outcomes can be evaluated as can the effect of efforts to drive those qualities deemed beneficial.
While some may find this creepy, the reality is that managers have been attempting to assess these characteristics on their own without much guidance. The development of verifiable and comparable metrics is a useful development. That societal norms have not yet emerged to govern the application of such data should be a cause for prudence, if not concern. Yet.
It seems that humans are far more predictable than they might think. It's what lessons are drawn from that predictability that should cause executives to focus on the quality of the data employed - as well as its interpretation and use. JL
Christopher Mims comments in the Wall Street Journal:
“People analytics,” treats the humans in an organization just like any other asset in the supply chain: as something that can be monitored, analyzed and reconfigured. Humans, it turns out, are remarkably predictable.
Imagine a top corporate executive of the future. Instead of finding out what’s going on in her company by asking her subordinates, she consults a digital dashboard that tells her everything from who is at their desk to how happy they are about it.
Any measurement that falls outside historic norms or deviates from industry standards is flagged in red. Within minutes our manager knows what issues to focus on today—or this quarter—whether it’s employee engagement or the size of her sales staff’s social networks.
This might sound far-fetched, but it isn’t far removed from the services offered by a growing number of companies.
What the services have in common is a willingness to use data to drive decision making in an area that has traditionally been an analytical backwater: human resources.
The result is something academics have dubbed “people analytics,” and it treats the humans in an organization just like any other asset in the supply chain: as something that can be monitored, analyzed and reconfigured.
Humans, it turns out, are remarkably predictable.
“Most companies have different flavors of the same basic problems,” says Ryan Fuller, co-founder of Volometrix. Almost all HR software includes analytics, but Volometrix is unusual in its appetite for acquiring data. The company’s software ingests every email and calendar item of every employee in a company, and uses that data to build a picture of who is doing what, and with whom.
Many managers believe salespeople with more contacts outside a company are more successful. But, data reveals their success is also highly dependent on the size of their networks within the company. And the true size of those networks is easy to determine by analyzing who employees email and with whom they meet.
Volometrix automatically does what a manager might do, such as emailing individual salespeople to encourage them to increase the size of their networks. (The company says it renders the data it collects on things like email anonymous to protect employee privacy.)
For customers including Qualcomm Inc., Boeing Co. and Symantec Corp. , Volometrix’s software is a sort of real-time management consultant in a box. Rather than waiting for something to break and then calling a professional-services firm, managers can intervene on a continuous basis, says Mr. Fuller.
Another approach to people analytics starts from the premise that happy workers are productive workers. CultureAmp, which is used by just about every tech startup you can name, including Box, Uber Technologies and Airbnb, allows managers to regularly take the temperature of their workforce through short surveys. The goal, says company founder Didier Elzinga, is to turn managers into “people geeks,” who are obsessed with quantifying otherwise nebulous concept of corporate culture.
Kim Rohrer, who runs HR and people analytics at Disqus, an online-community startup with just 65 employees, says tools like CultureAmp have allowed her to accomplish what previously required an entire team of in-house people analysts at a company like Google , which in many ways pioneered people analytics. Ms. Rohrer organizes regular meetups in San Francisco for other heads of “people ops,” the de rigueur term for this new, more data-oriented kind of HR. And she says she has witnessed a rapid democratization of the tools and methods of people analytics.
In a way, people analytics is about lending credibility to one of the most vital and yet overlooked functions of a company—getting the most out of the unruly hominids who make it run. In a modern corporation, data is a kind of currency.
The more of it you have, the more power and influence you wield. Measuring and acting on things once viewed as “warm and fuzzy,” like whether employees feel recognized for their work, has had a quantifiable impact on retention and productivity at Disqus, says Ms. Rohrer.
That’s one reason Mr. Fuller of Volometrix thinks companies will soon be creating stand-alone people-analytics divisions that function at the intersection of sales, information technology and HR. “More and more we see companies who are hiring for a [vice president] or director of people analytics,” says Mr. Fuller.
The idea that an organization can be made more effective by quantifying the moods and actions of its human constituents is likely to be intoxicating for managers who are accustomed to optimizing supply chains and IT infrastructure. But there are pitfalls in all this measurement. If the management’s goal becomes optimizing certain performance metrics, whatever isn’t measured can fall by the wayside.
And yet, owing to a tight tech labor market, many of the companies most likely to espouse people analytics are also the ones that put the most value on things like culture that can’t be quantified.
Making employees more efficient, even happier, isn’t the same thing as making them more creative or innovative. The experiments companies do in encouraging people to expand their in-house networks are repeatable and their results verifiable. But what about the experiments that really matter—which companies succeed or fail?
For now at least, this most important test of the value of people analytics has yet to be examined with anything like the rigor the discipline demands.
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