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.
Relational Analytics: A Deeper Definition
To date, people analytics has focused mostly on employee attribute data, 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.
The Six Signatures of Relational Analytics
Drawing from our own research and our consulting work with companies, as well as from a large body of other scholars’ research, we have identified six structural signatures that should form the bedrock of any relational analytics strategy.
Let’s look at each one in turn.
Most companies try to identify people who are good at ideation by examining attributes like educational background, experience, personality, and native intelligence. Those things are important, but they don’t help us see people’s access to information from others or the diversity of their sources of information—both of which are arguably even more important.
Good idea generators often synthesize information from one team with information from another to develop a new product concept. Or they use a solution created in one division to solve a problem in another. In other words, they occupy a brokerage position in networks.
The sociologist Ronald Burt has developed a measure that indicates whether someone is in a brokerage position. Known as constraint, it captures how limited a person is when gathering unique information. Study after study, across populations as diverse as bankers, lawyers, analysts, engineers, and software developers, has shown that employees with low constraint—who aren’t bound by a small, tight network of people—are more likely to generate ideas that management views as novel and useful.
In one study, Burt followed the senior leaders at a large U.S. electronics company as they applied relational analytics to determine which of 600-plus supply chain managers were most likely to develop ideas that improved efficiency. They used a survey to solicit such ideas from the managers and at the same time gather information on their networks. Senior executives then scored each of the submitted ideas for their novelty and potential value.
The only attribute that remotely predicted whether an individual would generate a valuable idea was seniority at the company, and its correlation wasn’t strong. Using the ideation signature—low constraint—was far more powerful: Supply chain managers who exhibited it in their networks were significantly more likely to generate good ideas than managers with high constraint.
A study Paul did at a large software development company bolsters this finding. The company’s R&D department was a “caveman world.” Though it employed more than 100 engineers, on average each one talked to only five other people. And those five people typically talked only to one another. Their contact with other “caves” was limited.
Such high-constraint networks are quite common in organizations, especially those that do specialized work. But that doesn’t mean low-constraint individuals aren’t hiding in plain sight. At the software company, relational analytics was able to pinpoint a few engineers who did span multiple networks. Management then generated a plan for encouraging them to do what they were naturally inclined to, and soon saw a significant increase in both the quantity—and quality—of ideas they proposed for product improvements.
Developing a good idea is no guarantee that people will use it. Similarly, just because an executive issues a decree for change, that doesn’t mean employees will carry it out. Getting ideas implemented requires influence.
But influence doesn’t work the way we might assume. Research shows that employees are not most influenced, positively or negatively, by the company’s senior leadership. Rather, it’s people in less formal roles who sway them the most.
If that’s the case, executives should just identify the popular employees and have them persuade their coworkers to get on board with new initiatives, right? Wrong.
A large medical device manufacturer that Paul worked with tried that approach when it was launching new compliance policies. Hoping to spread positive perceptions about them, the change management team shared the policies’ virtues with the workers who had been rated influential by the highest number of colleagues. But six months later employees still weren’t following the new procedures.
Why? A counterintuitive insight from relational analytics offers the explanation: Employees cited as influential by a large number of colleagues aren’t always the most influential people. Rather, the greatest influencers are people who have strong connections to others, even if only to a few people. Moreover, their strong connections in turn have strong connections of their own with other people. This means influencers’ ideas can spread further.
The structural signature of influence is called aggregate prominence, and it’s computed by measuring how well a person’s connections are connected, and how well the connections’ connections are connected. (A similar logic is used by search engines to rank-order search results.)
Employees are not most influenced by the company’s senior leadership.
In each of nine divisions at the medical device manufacturer, relational analytics identified the five individuals who had the highest aggregate prominence scores. The company asked for their thoughts on the new policies. About three-quarters viewed them favorably. The firm provided facts that would allay fears of the change to them as well as to the influencers who didn’t like the policies—and then waited for the results.
Six months later more than 75% of the employees in those nine divisions had adopted the new compliance policies. In contrast, only 15% of employees had adopted them in the remaining seven affected divisions, where relational analytics had not been applied.
Click here to continue reading Paul Leonardi and Noshir Contractor’s article.