Here is an excerpt from an article written by Paul Leonardi and Noshir Contractor for Harvard Business Review and the HBR Blog Network. To read the complete article, check out the wealth of free resources, obtain subscription information, and receive HBR email alerts, please click here.

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“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 surveyby 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 amongpeople 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.

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.

Relational Analytics: A Deeper Definition

To date, people analytics has focused mostly on employee attributedata, of which there are two kinds:

• Trait: facts about individuals that don’t change, such as ethnicity, gender, and work history.
• State: facts about individuals that do change, such as age, education level, company tenure, value of received bonuses, commute distance, and days absent.

The two types of data are often aggregated to identify group characteristics, such as ethnic makeup, gender diversity, and average compensation.

Attribute analytics is necessary but not sufficient. Aggregate attribute data may seem like relational data because it involves more than one person, but it’s not. Relational data captures, for example, the communications between two people in different departments in a day. In short, relational analytics is the science of human social networks.

Decades of research convincingly show that the relationships employees have with one another—together with their individual attributes—can explain their workplace performance. The key is finding “structural signatures”: patterns in the data that correlate to some form of good (or bad) performance. Just as neurologists can identify structural signatures in the brain’s networks that predict bipolar disorder and schizophrenia, and chemists can look at the structural signatures of a liquid and predict its kinetic fragility, organizational leaders can look at structural signatures in their companies’ social networks and predict how, say, creative or effective individual employees, teams, or the organization as a whole will be.

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