Recognise This! – Data is useless unless you know how to analyse your findings, measure the results and report on the ROI of your efforts.

I’m a proponent of big data – especially in HR. You can change a company culture without big data, but you certainly cannot proactively manage it on an ongoing basis. But collecting the data isn’t enough. You have to know what data to collect in the first place, and then what questions to ask about the resulting information as you conduct deep analysis.

As David Bernstein, head of eQuest‘s Big Data for HR/Predictive Analytics Division, said in a recent TLNT article, “[Big data] is nothing without knowledgeable people making the essential connections.”

Indeed, I encourage you to read David’s entire post as he offers sound advice on how to effectively use the data you collect, including several steps in big data analysis that rely on human intelligence for proper interpretation and, critically, application.

For a real-world application of big data collection, analysis and application, there are few better examples than Google’s People Operations department. Slate shared  this recent article on the depths of Google’s big data gathering on its people (including the size of café tables and lunch lines). I was particularly impressed by this:

“Then there was the happiness problem. Google monitors its employees’ well-being to a degree that can seem absurd to those who work outside Mountain View. The attrition rate among women suggested there might be something amiss in the company’s happiness machine. And if there’s any sign that joy among Googlers is on the wane, it’s the Google HR department’s mission to figure out why and how to fix it.

“Google calls its HR department People Operations, though most people in the firm shorten it to POPS. The group is headed by Laszlo Bock, a trim, soft-spoken 40-year-old who came to Google six years ago. Bock says that when POPS looked into Google’s woman problem, it found it was really a new mother problem: Women who had recently given birth were leaving at twice Google’s average departure rate. At the time, Google offered an industry-standard maternity leave plan. After a woman gave birth, she got 12 weeks of paid time off. For all other new parents in its California offices, but not for its workers outside the state, the company offered seven paid weeks of leave.

“So in 2007, Bock changed the plan. New mothers would now get five months off at full pay and full benefits… . It would be a mistake to conclude that Google doles out such perks just to be nice. POPS rigorously monitors a slew of data about how employees respond to benefits, and it rarely throws money away. The five-month maternity leave plan, for instance, was a winner for the company. After it went into place, Google’s attrition rate for new mothers dropped down to the average rate for the rest of the firm. “A 50 percent reduction—it was enormous!” Bock says. What’s more, happiness—as measured by Googlegeist, a lengthy annual survey of employees—rose as well. Best of all for the company, the new leave policy was cost-effective. Bock says that if you factor in the savings in recruitment costs, granting mothers five months of leave doesn’t cost Google any more money.”

3 Keys to Successful Big Data Application

1)      Gather the right data in the first place.

2)      Analyse the data to understand where and when it is most important and effective to intervene and the actions to take

3)      Measure the results to prove the ROI of actions taken (and, therefore, the benefit of the big data collection and analysis, too)

If your goal is to deeply understand your organisation’s culture so you can intervene and take effective actions to change the culture for long-term success, one of the most effective big data collection mechanism is strategic, social employee recognition. Look to all of your employees, frequently and globally, to tell you how your culture is doing based on the core values they are recognising in their peers and colleagues.

Does your organisation collect, analyse and take action on big data efforts around your people operations? What effects have you seen?