Using People Analytics to Acquire Top Talent, Predict Performance & Reduce Turnover

August 19, 2020 – The value of combining Big Data with statistical predictive modeling has been highly touted in recent years. The idea is to apply advanced computing capabilities to talent-related data to better predict employee behavior.

“This people analytics approach has been adopted with the aim of simultaneously doing a better job of improving performance, acquiring top talent and reducing turnover,” said Richard Stein, chief growth officer at financial services and technology-focused Options Group, in a new report. “In real-world experience, too many big organizations are finding it difficult to reap the promised return from investment in people analytics,” he said. “The difficulty stems in large part from the inability of many organizations to translate data into practical action.”

According to many of the CHROs Options Group spoke with recently as it gathered market intelligence from a broad spectrum of organizations, the two most common complaints are that predictive results from people analytics models have not proven to be significantly better than traditional human-rich people-management; the other: people analytics teams are too often viewed inside their organizations as merely data analysts, detached from the realities of talent management and without authority to change behaviors.

“It’s not surprising that many executives would like to forget the whole thing and revert to traditional talent management,” said Mr. Stein. “But that would be a mistake. The firms that continue building people analytics capabilities appreciate how powerful the systematic harnessing of people data can be – once, that is, they’ve gone through the early teething pain.”

Applying Relational Analytics
Professional sports teams were early adopters of people analytics. The management of the Oakland Athletics baseball team, for example, famously pioneered the application of enhanced data modeling. Doing so changed baseball. “The effect of people analytics in professional sports has been undeniable,” said Mr. Stein. “That revolution is here to stay. In other businesses the results have been less than hoped. Traditionally, people-management analytics in businesses other than sports have focused on two types of talent data: first, data that does not change and is easy to gather, such as ethnicity and gender; and second, data that does change, such as tenure or compensation. These are both useful, of course. But to produce strategic, practical outcomes they are insufficient,” he said. “They do not account for human relations.”

Relational Analytics
“Relational analytics,” as it is called, is now evolving as a science of social networks, adding layers of insight about the ways people connect and work with one another. “For small to medium-sized -firms the applicability, not to mention the implementation, of relational analytics would probably be overkill,” said Mr. Stein. “Executives inside such organizations should already understand how their people work together. Their most effective tools for enhancing performance will still be intuition, empathy and engagement.”

“For large organizations, grafting relational analytics to existing analytic strategies can improve identification of employees capable of achieving organizational goals who might otherwise fly under the radar,” Mr. Stein said. “Ideally, organizations will also get a better grasp of which essential individuals they can’t afford to lose. Add to that a picture of where silos exist within their organization.” People analytics will never be a substitute for the application of human judgment to talent-management practice, Mr. Stein said. “But it offers sophisticated tools for augmenting and improving that judgment.” Here is how to do that: “Decades of research convincingly describe how relationships among employees – together with individual attributes – explain workplace performance,” said Mr. Stein. “All you need is to find patterns in the data. Simple.”

What is needed is identification within the collected relational data of six traits that most often impact performance: Ideation; influence; efficiency; innovation; siloing; and vulnerability. “Among employees these individual traits naturally evolve,” Mr. Stein said. “But managed on an enterprise basis there is a likelihood that relational analytics can portray not just current but future performance.”

“Data analysts don’t typically have intimate knowledge of employees and teams. Working alongside leadership teams they can make a transformational change in understanding data and its impact on performance. But too many firms don’t realize that they are overlooking their best resources for data analysis.”

Talking Out Loud About Data and Behavior
The digital motifs comprising relational analytics are generally derived from logs, emails and the contents of everyday interoffice activity. “These can be used to construct a view of employee, team and organizational networks in which it is possible to pick out patterns,” Mr. Stein said. “Navigating these patterns depends on ensuring correlation to specific behaviors in relation to individual, team and organizational communications.

Mr. Stein said it is essential to keep records on stored data. “These include the reasons for its collection, the sources from which it is derived and the people with whom it has been shared,” he said. “Mishandling employee data has an obviously negative effect on employee relations. It also generates reputational risk to the organization, along with the possibility of litigation and investigation.”

“Interpreting – correctly – relational analytics will be transformational in constructing a real-time view of an organization’s network, its capabilities and its long-term prospects of success,” Mr. Stein said. “Powerful stuff.

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