Without models, making sense of data is hard. Data helps describe reality, albeit imperfectly. On its own, though, data can’t recommend one decision over another. If you notice that your best-performing teams are also your most diverse, that may be interesting. But to turn that data point into insight, you need to plug it into some model of the world — for instance, you may hypothesize that having a greater variety of perspectives on a team leads to better decision-making. Your hypothesis represents a model of the world.
Though single models can perform well, ensembles of models work even better. That is why the best thinkers, the most accurate predictors, and the most effective design teams use ensembles of models. They are what I call, many-model thinkers.
Too often new collaborative technologies — though intended to connect employees seamlessly and enable work to get done more efficiently — are misused in ways that impede innovation and hurt performance.
Age-old wisdom suggests it is not what but whom you know that matters. Over decades this truism has been supported by a great deal of research on networks. Work since the 1970s shows that people who maintain certain kinds of networks do better: They are promoted more rapidly than their peers, make more money, are more likely to find a job if they lose their own, and are more likely to be considered high performers.
But the secret to these networks has never been their size. Simply following the advice of self-help books and building mammoth Rolodexes or Facebook accounts actually tends to hurt performance as well as have a negative effect on health and well-being at work. Rather, the people who do better tend to have more ties to people who themselves are not connected. People with ties to the less-connected are more likely to hear about ideas that haven’t gotten exposure elsewhere, and are able to piece together opportunities in ways that less-effectively-networked colleagues cannot.
If bigger is not better in networks, what is the actual impact of social media tools in the workforce? The answer: They are as likely to actually hurt performance and engagement as they are to help — if they simply foist more collaborative demands on an already-overloaded workforce. In most places, people are drowning in collaborative demands imposed by meetings, emails, and phone calls. For most of us, these activities consume 75% to 90% of a typical work week and constitute a gauntlet to get to the work we must do. In this context, new collaborative technologies, when not used appropriately, are over-loading us all and diminishing efficiency and innovation at work.
During Jeff Immelt’s 16 years as CEO, GE radically changed its mix of businesses and its strategy.
Its focus—becoming a truly global, technology-driven industrial company that’s blazing the path for the internet of things—has had dramatic implications for the profile of its workforce. Currently, 50% of GE’s 300,000 employees have been with the company for five years or less, meaning that they may lack the personal networks needed to succeed and get ahead. The skills of GE’s workforce have been rapidly changing as well, largely because of the company’s ongoing transformation into a state-of-the-art digital industrial organization that excels at analytics. The good news is that GE has managed to attract thousands of digerati. The bad news is that they have little tolerance for the bureaucracy of a conventional multinational. As is the case with younger workers in general, they want to be in charge of their own careers and don’t want to depend solely on their bosses or HR to identify opportunities and figure out the training and experiences needed to pursue their professional goals.
What’s the solution to these challenges? GE hopes it’s HR analytics. “We need a set of complementary technologies that can take a company that’s in 180 countries around the world and make it small,” says James Gallman, who until recently was the GE executive responsible for people analytics and planning. The technologies he’s referring to are a set of self-service applications available to employees, leaders, and HR. All the apps are based on a generic matching algorithm built by data scientists at GE’s Global Research Center in conjunction with HR. “It’s GE’s version of Match.com,” quips Gallman. “It can take a person and match him or her to something else: online or conventional educational programs, another person, or a job.”
It seems like every business is struggling with the concept of transformation. Large incumbents are trying to keep pace with digital upstarts., and even digital native companies born as disruptors know that they need to transform. Take Uber: at only eight years old, it’s already upended the business model of taxis. Now it’s trying to move from a software platform to a robotics lab to build self-driving cars.
And while the number of initiatives that fall under the umbrella of “transformation” is so broad that it can seem meaningless, this breadth is actually one of the defining characteristic that differentiates transformation from ordinary change. A transformation is a whole portfolio of change initiatives that together form an integrated program.
And so a transformation is a system of systems, all made up of the most complex system of all — people. For this reason, organizational transformation is uniquely suited to the analysis, prediction, and experimental research approach of the people analytics field.
People analytics — defined as the use of data about human behavior, relationships and traits to make business decisions — helps to replace decision making based on anecdotal experience, hierarchy and risk avoidance with higher-quality decisions based on data analysis, prediction, and experimental research. In working with several dozen Fortune 500 companies with Microsoft’s Workplace Analytics division, we’ve observed companies using people analytics in three main ways to help understand and drive their transformation efforts.
When Brian Jensen told his audience of HR executives that Colorcon wasn’t bothering with annual reviews anymore, they were appalled. This was in 2002, during his tenure as the drugmaker’s head of global human resources. In his presentation at the Wharton School, Jensen explained that Colorcon had found a more effective way of reinforcing desired behaviors and managing performance: Supervisors were giving people instant feedback, tying it to individuals’ own goals, and handing out small weekly bonuses to employees they saw doing good things.
Back then the idea of abandoning the traditional appraisal process—and all that followed from it—seemed heretical. But now, by some estimates, more than one-third of U.S. companies are doing just that. From Silicon Valley to New York, and in offices across the world, firms are replacing annual reviews with frequent, informal check-ins between managers and employees.
How We Got Here
Historical and economic context has played a large role in the evolution of performance management over the decades. When human capital was plentiful, the focus was on which people to let go, which to keep, and which to reward—and for those purposes, traditional appraisals (with their emphasis on individual accountability) worked pretty well. But when talent was in shorter supply, as it is now, developing people became a greater concern—and organizations had to find new ways of meeting that need.
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.
"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.
The modern workplace is awash in meetings, many of which are terrible. As a result, people mostly hate going to meetings. The problem is this: The whole point of meetings is to have discussions that you can’t have any other way. And yet most meetings are devoid of real debate.
To improve the meetings you run, and save the meetings you’re invited to, focus on making the discussion more robust.
When teams have a good fight during meetings, team members debate the issues, consider alternatives, challenge one another, listen to minority views, and scrutinize assumptions. Every participant can speak up without fear of retribution. However, many people shy away from such conflict, conflating disagreement and debate with personal attacks. In reality, this sort of friction produces the best decisions. In my recent study of 5,000 managers and employees, published in my recent book, I found that the best performers are really good at generating rigorous discussions in team meetings. (The sample includes senior and junior managers and individual contributors from a range of industries in corporate America; my aim was to statistically identify work habits that correlate with higher performance.)
So how do you lead a good fight in meetings? Here are six practical tips:
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.