From foosball tables and volleyball courts to gourmet cafeterias and "hacker days," Google goes to great lengths to keep its employees happy . As someone who believes passionately in the notion of "slow business" and putting humanity at the heart of our business lives, I can only believe that that's a good thing.
But Google's Human Resources efforts are not about starry-eyed idealism or whacky Bay Area non-conformity. They are, in fact, deeply driven by analyzing reams of data about how Google employees work. In a recent article for the Atlantic on Google's data-driven People Analytics Department , Chris Derose explains how this works:
While the rest of the world is waking up to analytics and the power of Big Data, Google has spent years methodically building one of the most refined performance-management engines in the corporate world. Similar to any of the company’s other departments, every People Operations project starts with a question to answer. Google’s People Analytics team has posed questions that range from tactical issues (“What if we could decrease ramp-up time for new employees by a month?”) to the existential (“What if all engineers were able to reach their potential for innovation?”) to the seemingly outlandish (“What if working at Google increased your life span by a year?”).
Where does this data-centric decision making end?
It was at this point in my paranoid ruminations, however, that I realized I was doing exactly what Google's People Analytics sets out to avoid — and that's allowing my emotions to cloud my reasoning. While human wisdom is central to good decision making, human prejudice, fear, and temperamentally are not. One study , for example, has shown that simply having what's perceived as a "black" name on a resume can significantly and negatively impact a candidate's chances for success. Given such shortcomings in human-based hiring decisions, some data-driven rigor might just have its place.
Google's approach to HR is not intended to displace humans from the decision making process at all, but rather give them the tools they need to get past their own cognitive shortcomings. Here's more from Derose:
Rather than entrust important talent decisions to black-box calculations, Google’s leaders asked [Prasad] Setty to focus on providing insights that could help decision-makers improve the odds of getting complex decisions right. As Setty explained, the models that result from his department’s experiments explain normal or “average contexts” that won’t apply universally. The goal of People Analytics is to “complement human decision makers, not replace them.”
To return to my previous example, the real travesty of No Child Left Behind was not that it sought to put a metric on school success, but rather that it took such a blunt approach to doing so. Instead of obsessing about "failing" schools and "failing" teachers, and posting somewhat arbitrary targets to address them, what if education authorities had instead asked themselves "what makes a child achieve their own deepest potential?" or "what changes to the school environment improve learning and retention?"
Evidence is central to informed, enlightened and accountable decision making. But before you gather evidence, you must first identify the question you are trying to address.
If we humans can define our questions with wisdom, clarity and a clear sense of ethics, Big Data can step in to help us answer them.
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