HR

Inside Google’s culture of relentless self-surveying
Tim Fernholz. 6/26/2013. “Inside Google’s culture of relentless self-surveying.” Quartz. Publisher's VersionAbstract

When Google recently admitted that the baffling brainteasers it posed to interviewees were utterly useless at predicting which ones would make good employees, it was another example of the power of what Google calls “people analytics”—the mixing of Big Data with management science to come up with smarter ways to work.

The company’s obsession with human data is perhaps best known for producing the rule that no employee should sit more than 150 feet (46 meters) away from a micro-kitchen, and that in those kitchens the chocolate M&Ms be kept in opaque jars while healthier food is in clear containers, to encourage healthy eating habits. Google’s often controversial culture of omniscience about its users is mirrored, inside its posh campuses, by a team of industrial-organizational psychologists, behavioral economists, consultants and statisticians who survey and experiment with Google’s staff.

Read More.

HR Must Make People Analytics More User-Friendly
John Boudreau. 6/16/2017. “HR Must Make People Analytics More User-Friendly.” Harvard Business Review. Publisher's VersionAbstract

Managing HR-related data is critical to any organization’s success. And yet progress in HR analytics has been glacially slow. Consulting firms in the U.S. and Europe lament the slow progress. But a Harvard Business Review analytics study of 230 executives suggests a stunning rate of anticipated progress: 15% said they use “predictive analytics based on HR data and data from other sources within or outside the organization,” while 48% predicted they would be doing so in two years. The reality seems less impressive, as a global IBM survey of more than 1,700 CEOs found that 71% identified human capital as a key source of competitive advantage, yet a global study by Tata Consultancy Services showed that only 5% of big-data investments were in human resources.

Recently, my colleague Wayne Cascio and I took up the question of why HR analytics progress has been so slow despite many decades of research and practical tool building, an exponential increase in available HR data, and consistent evidence that improved HR and talent management leads to stronger organizational performance. Our article in the Journal of Organizational Effectiveness: People and Performance discusses factors that can effectively “push” HR measures and analysis to audiences in a more impactful way, as well as factors that can effectively lead others to “pull” that data for analysis throughout the organization.

Read More.

Better People Analytics
Paul Leonardi and Noshir Contractor. 11/1/2018. “Better People Analytics.” Harvard Business Review. Publisher's VersionAbstract

"We have charts and graphs to back us up. So f*** off.” New hires in Google’s people analytics department began receiving a laptop sticker with that slogan a few years ago, when the group probably felt it needed to defend its work. Back then people analytics—using statistical insights from employee data to make talent management decisions—was still a provocative idea with plenty of skeptics who feared it might lead companies to reduce individuals to numbers. HR collected data on workers, but the notion that it could be actively mined to understand and manage them was novel—and suspect.

Today there’s no need for stickers. More than 70% of companies now say they consider people analytics to be a high priority. The field even has celebrated case studies, like Google’s Project Oxygen, which uncovered the practices of the tech giant’s best managers and then used them in coaching sessions to improve the work of low performers. Other examples, such as Dell’s experiments with increasing the success of its sales force, also point to the power of people analytics.

But hype, as it often does, has outpaced reality. The truth is, people analytics has made only modest progress over the past decade. A survey by Tata Consultancy Services found that just 5% of big-data investments go to HR, the group that typically manages people analytics. And a recent study by Deloitte showed that although people analytics has become mainstream, only 9% of companies believe they have a good understanding of which talent dimensions drive performance in their organizations.

What gives? If, as the sticker says, people analytics teams have charts and graphs to back them up, why haven’t results followed? We believe it’s because most rely on a narrow approach to data analysis: They use data only about individual people, when data about the interplay among people is equally or more important.

People’s interactions are the focus of an emerging discipline we call relational analytics. By incorporating it into their people analytics strategies, companies can better identify employees who are capable of helping them achieve their goals, whether for increased innovation, influence, or efficiency. Firms will also gain insight into which key players they can’t afford to lose and where silos exist in their organizations.

Most people analytics teams rely on a narrow approach to data analysis.

Fortunately, the raw material for relational analytics already exists in companies. It’s the data created by e-mail exchanges, chats, and file transfers—the digital exhaust of a company. By mining it, firms can build good relational analytics models.

In this article we present a framework for understanding and applying relational analytics. And we have the charts and graphs to back us up.

Read More.

 

They're Watching You at Work
Don Peck. 12/2013. “They're Watching You at Work.” The Atlantic. Publisher's VersionAbstract

What happens when Big Data meets human resources? The emerging practice of "people analytics" is already transforming how employers hire, fire, and promote.

in 2003, thanks to Michael Lewis and his best seller Moneyball, the general manager of the Oakland A’s, Billy Beane, became a star. The previous year, Beane had turned his back on his scouts and had instead entrusted player-acquisition decisions to mathematical models developed by a young, Harvard-trained statistical wizard on his staff. What happened next has become baseball lore. The A’s, a small-market team with a paltry budget, ripped off the longest winning streak in American League history and rolled up 103 wins for the season. Only the mighty Yankees, who had spent three times as much on player salaries, won as many games. The team’s success, in turn, launched a revolution. In the years that followed, team after team began to use detailed predictive models to assess players’ potential and monetary value, and the early adopters, by and large, gained a measurable competitive edge over their more hidebound peers.

That’s the story as most of us know it. But it is incomplete. What would seem at first glance to be nothing but a memorable tale about baseball may turn out to be the opening chapter of a much larger story about jobs. Predictive statistical analysis, harnessed to big data, appears poised to alter the way millions of people are hired and assessed.

Read More.

Competing on Talent Analytics
Thomas H. Davenport, Jeanne Harris, and Jeremy Shapiro. 10/2010. “Competing on Talent Analytics.” Harvard Business Review. Publisher's VersionAbstract
Do you think you know how to get the best from your people? Or do you know? How do investments in your employees actually affect workforce performance? Who are your top performers? How can you empower and motivate other employees to excel?

Leading-edge companies are increasingly adopting sophisticated methods of analyzing employee data to enhance their competitive advantage. Google, Best Buy, Sysco, and others are beginning to understand exactly how to ensure the highest productivity, engagement, and retention of top talent, and then replicating their successes. If you want better performance from your top employees—who are perhaps your greatest asset and your largest expense—you’ll do well to favor analytics over your gut instincts.

Harrah’s Entertainment is well-known for employing analytics to select customers with the greatest profit potential and to refine pricing and promotions for targeted segments. (See “Competing on Analytics,”HBR January 2006.) Harrah’s has also extended this approach to people decisions, using insights derived from data to put the right employees in the right jobs and creating models that calculate the optimal number of staff members to deal with customers at the front desk and other service points. Today the company uses analytics to hold itself accountable for the things that matter most to its staff, knowing that happier and healthier employees create better-satisfied guests.

Read More.

Demystifying People Analytics – Part 4: Examples of People Analytics Projects
2/14/2017. “Demystifying People Analytics – Part 4: Examples of People Analytics Projects”. MoreAbstract

Previously in the ‘Demystifying People Analytics’ series I’ve written about where the team should sit (Part I), the skills and capabilities required to do people analytics (Part II) and the vital role of storytelling (Part III).

This time I’m going to provide examples of people analytics projects companies have undertaken to help drive both business and employee outcomes.

The five example projects outlined are spread across the employee lifecycle (workforce planning, talent acquisition, engagement, retention and compliance). Whilst the projects themselves are different, what they share in common is that they were all undertaken to help solve business challenges that were high priority for the organisations concerned.

Two examples (Cisco and Shell) were presented at HR Tech World in Paris last October. I am proud to once again be moderating the Smart Data breakout at the forthcoming HR Tech World in London on 21-22 March, and would urge all those interested or involved in people analytics to attend. Presenters from the likes of Adidas, ING, EY, Merck and Quintiles will be outlining the people analytics journeys at their respective companies.

Read More.