Twenty years ago, Tim Berners-Lee (inventor of the World Wide Web) asked people to put their documents online. His suggestion completely revolutionized how we interact with the world around us. Last year, he reflected upon that transformation: “I said, ‘Could you put your documents on this web thing?’ And you did. Thanks. It’s been a blast, hasn’t it?”
Now, Lee is calling people to put their data on the internet. He had an audience chant, “raw data now!” after lecturing on how he believes that the more pieces of data we connect, the more powerful it becomes. Considering his history of incredible foresight, I daresay he’s onto something.
We’re finally taking informational data and placing it within holistic data schemes. On a global scale, people are using data to address mass challenges like feeding people, supplying medical care, providing energy, and averting climate change – and if data can do all that, it can certainly help us keep top talent engaged in our organizations. In business, this data reflects things like culture, engagement, loyalty, and leadership – areas HR professionals care a lot about.
So how can you leverage your data to improve HR decision making? Here are a few tips.
When you frame issues:
– Ask the right questions. Generally, the problem isn’t embracing the concept of analytics, but knowing how to focus them. Asking “how are we celebrating birthdays?” or “how many people signed up for our volunteer day?” won’t create lasting impact. Instead, ask “how is top talent being engaged?” and “what if everyone were able to reach his or her full potential?” Measuring too many things (or too many of the wrong things) leads to watered down results. According to a 2015 survey, one of the least-measured HR metrics is “quality of hire,” which is only being measured by 28.5% of organizations. Of those, more than half ask only one question: how satisfied is the hiring manager with the new recruit. Actionable? Not so much. Data is only as good as the questions it answers.
– Address the big picture. Instead of asking each department to provide three measurements to add to the scorecard, focus on the whole—the why, the root cause—and let those strategies cascade through everything else. Imagine a hierarchy: strategic impact on top, effective processes in the middle, and transactional efficiencies on the bottom. Draw an organizational map to see how the concepts and outcomes relate. Those ground-level tactics may be similar across different businesses, but the top level is unique to yours organization.
– Think forward. The beauty of analytics is that it helps us chart our future course, not just record the past. One of the most popular HR metrics is voluntary and involuntary turnover, measured by 78% of organizations. But if you’re not recognizing valuable talent until it’s walking out the door, you’re missing the power of data. McKinsey tells of an organization that used predictive behavioral analytics to learn that most employees were leaving because of inadequate recognition and limited training. Unknowingly, management had been giving expensive retention bonuses that were “an ineffective and costly Band-Aid.” The company redesigned training and recognition, and consequently reduced its retention bonuses by $20 million and employee attrition by half. Be predictive, not reactive.
– Make it personal. There is no one-size-fits-all formula for data management. Walmart, Goldman Sachs, and Zappos might all measure turnover, but they’re using results to address different questions. One reason Google’s intensive people operations team has been immensely successful is because their strategy and tactics are so specific to Googlers. They’ve learned that time off and exposure to senior leaders are more motivating rewards than cash or prizes. A study revealed that their people value even-keeled managers who make time for one-on-one check-ins and help their people solve problems. Using that information, they helped 75% of their least effective managers improve. Rather than try to replicate another company or adopt best practices, measure and understand what works for your organization.
When you whip out your calculators:
– Bring in the right minds. You need business people who address the right problems, statisticians who ensure there’s rigor in the methods, and technologists who make solutions scalable and transparent. Some data collections and analyzations are huge undertakings: statisticians often gather thousands of observations across hundreds of variables. They may need to code non-numerical information (feedback, interview notes) in order to synthesize results and look for patterns. There may be random correlations that look significant but are actually just noise. There may be correlations that shouldn’t be confused with causations. Make sure you have the right people on your team.
– Complement humans, but don’t replace them. In the euphoria of new analytics tools, it’s easy to think a few simple clicks will transform your business. But assumptions can be built into the metrics that are biased and discriminatory, or they could be measuring irrelevant or excessive information. We’re still at the outset of the big data revolution and are learning how to handle it best. These processes require strategy, critical thought, and creativity.
When you get the results:
– Make them visual. As data visionary Hans Rosling said, “Let the dataset change your mindset.” One of the best ways to do so is by visualizing the information. Design it so it makes more sense, tells a story, and lets patterns and connections emerge. Sight is the fastest of our senses, with the same bandwidth as a computer network. But of all the daily visuals and patterns pouring in, we’re only consciously aware of about 0.7% of what we see. Rosling created a free software, Gapminder World, that can animate statistics. Google, ever the early adopter, started using this software in 2007.
– Make them actionable. Without some link to a future course of action, data overload is overwhelming and counterproductive. It’s tempting to throw around best practices and measure everything we can get our hands on. But, we can’t realistically embrace 20 improved practices at once without ranking them by importance. Google figured out they could improve their on boarding process by 25% (a full month faster) by sending a simple reminder email to the hiring manager the Sunday before the new hire starts. The email highlights five quick bullets: have a role and responsibilities discussion, match your new hire with a peer buddy, help your new hire build a social network, set up onboarding check-ins, and encourage open dialogue. That’s it. Their data shows that employees work best under the assumption that they are smart and can figure things out, so the company empowers managers to take care of the rest.
Kenneth Cukier, Data Editor of The Economist, says, “Data doesn’t just let us see more of the same thing we were looking at. More data allows us to see new. It allows us to see better. It allows us to see different.” Some call it “the new oil,” others “the new soil,” but whatever it is, it’s all around us, and it’s a fertile, creative medium. Take these steps to properly refine your data and use it to your advantage.
Also published in FirstPerson.