Better People Analytics: Measure who they know, not just who they are.

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,  Dell’s experiments with increasing the success of its sales force.

But the hype 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.

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.

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.

Capture Your Company’s Digital Exhaust

Why, then, don’t most companies use relational analytics for performance management? There are two reasons.

·       The first is that many network analyses companies do are little more than pretty pictures of nodes and edges. They don’t identify the patterns that predict performance.

·       The second reason is that most organizations don’t have information systems in place to capture relational data.

But all companies do have a crucial hidden resource: their digital exhaust—the logs, e-trails, and contents of everyday digital activity. Every time employees send one another e-mails in Outlook, message one another on Slack, like posts on Facebook’s Workplace, form teams in Microsoft Teams, or assign people to project milestones in Trello, the platforms record the interactions. This information can be used to construct views of employee, team, and organizational networks in which you can pick out the structural signatures we’ve discussed.

To gather relational data, companies typically survey employees about whom they interact with. Surveys take time, however, and the answers can vary in accuracy (some employees are just guessing). Also, to be truly useful, relational data must come from everyone at the company, not just a few people.

Company-collected relational data, however, creates new challenges. Some employees feel that the passive collection of relational data is an invasion of privacy. This is not a trivial concern. Companies need clear HR policies about the gathering and analysis of digital exhaust that help employees understand and feel comfortable with it.

What About Employee Privacy?

Relational analytics changes the equation when it comes to the privacy of employee data. When employees actively provide information about themselves in hiring forms, surveys, and the like, ...

Behavioral data is a better reflection of reality.

As we’ve noted, digital exhaust is less biased than data collected through surveys. For instance, in surveys people may list connections they think they’re supposed to interact with, rather than those they actually do interact with. And because every employee will be on at least several communication platforms, companies can map networks representing the entire workforce, which makes the analysis more accurate.

Also, not all behaviors are equal. Liking someone’s post is different from working on a team with someone for two years. Copying someone on an e-mail does not indicate a strong relationship. How all those individual behaviors are weighted and combined matters. This is where machine-learning algorithms and simulation models are helpful. With a little technical know-how (and with an understanding of which structural signatures predict what performance outcomes), setting up those systems is not hard to do.

Constant updating is required.

Relationships are dynamic. People and projects come and go. To be useful, relational data must be timely. Using digital exhaust in a relational analytics model addresses that need.

Analyses need to be close to decision makers.

Most companies rely on data scientists to cull insights related to talent and performance management. That often creates a bottleneck, because there aren’t enough data scientists to address all management queries in a timely manner. Plus, data scientists don’t know the employees they are running analyses on, so they cannot put results into context.

Dashboards are key.

A system that identifies structural signatures and highlights them visually moves analytic insights closer to the managers who need them. As one executive at a semiconductor chip firm told us, “I want my managers to have the data to make good decisions about how to use their employees. And I want them to be able to do it when those decision points happen, not later.”

CONCLUSION

People analytics is a new way to make evidence-based decisions that improve organizations. But in these early days, most companies have been focused on the attributes of individuals, rather than on their relationships with other employees. Looking at attributes will take firms only so far.

 If they harness relational analytics, however, they can estimate the likelihood that an employee, a team, or an entire organization will achieve a performance goal. They can also use algorithms to tailor staff assignments to changes in employee networks or to a particular managerial need. The best firms, of course, will use relational analytics to augment their own decision criteria and build healthier, happier, and more productive organizations.

The above article is a synthesis of the original HBR Article https://hbr.org/2018/11/better-people-analytics