Why Companies Need Better People Analytics

We all know that data can be used to drive business success—executives and HR professionals alike have been collecting stats for years. More recently, however, our reasons for using this data have changed, particularly for learning and development (L&D) teams.Organizations are finally beginning to realize that understanding its employees—how they learn, work and communicate—is essential in maintaining a diverse, global workforce, which has led many to refocus their efforts on better people analytics.

According to Ideal, a company that uses AI to help businesses make smarter hiring decisions, people analytics is defined as “the use of data and data analysis techniques to understand, improve and optimize the people side of business.” Ideal also claims there are three main types of employee-related data to consider: people (demographics, skills, engagement), program (attendance, participation) and performance (ratings/evaluations, self-assessments). On a technical level, this information can be used to better assess HR hiring and training processes—for example, data can identify bias in recruitment—but there’s a lot more to it than that. Used properly, people analytics can contribute to the success of individual employees, as well as the organization as a whole.

Analytics have long been used to measure hard, quantitative data (e.g. profits, number of sales), but now, more and more company leaders—including those in HR and L&D—are starting to collect data for qualitative reasons. McKinsey Quarterly offers a great example of this in a recent case study about a restaurant chain that hoped to improve customer experience by addressing their higher-than-average employee turnover rate. Having exhausted traditional solutions, the company started collecting data on the hiring/onboarding process, including the personality traits of new hires. They also examined the minutiae of day-to-day employee management and specific employee behavior—even using sensors to track physical movements and recording how much time employees spent talking vs. listening to customers and colleagues. By identifying which employee features aligned with desired outcomes, and making HR decisions based on this, the chain was able to significantly improve employee retention. Customer satisfaction also improved by more than 100% and order times decreased by 30 seconds.

It’s easy to understand how similar data collection methods can be used by L&D professionals to revolutionize training processes—the same data used to improve hiring can be leveraged to more effectively upskill existing employees. Using sophisticated people analytics to assess employee communication, for example, can help to define areas for improvement, like language learning and soft-skills development. It can also be used to dispel incorrect assumptions, as was the case for the restaurant chain. Prior to adopting people analytics, they believed managerial tenure had a major impact on employee skills and behavior, but the data proved otherwise. Mining for data, such as this, forces companies to set aside inhibiting thought patterns, which allows them to come up with new HR and business solutions.

Organizations have begun to understand how data can be used to support learning and development initiatives—39% of HR leaders have plans to begin using data for better people analytics by 2020. However, when asked how they would like to use this data, “tracking learning progress” was cited as the number-one goal, followed closely by “not sure.” This highlights one of the largest problems surrounding people analytics today: since it’s a relatively new practice, companies don’t know how it can be used and often don’t have a proper strategy or structure in place.

This was the case for Chevron. As a large international company, it already made extensive use of data when it came to HR, but there was little coordination between different regions and departments. When oil prices began to decrease, the company needed to come up with new ways of improving per-employee revenue and profitability, so they decided to refocus their efforts by supporting business strategies with people analytics. This led to the formation of a robust people analytics team that operated across their global workforce. The team eventually standardized reporting for all talent metrics, developed new analytics programs and also began closely assessing each of the company’s reorganization and restructuring decisions. As a result, Chevron was able to lower their overall operational costs and increase productivity by 30%—organizational success that would not have been achieved had they not taken a closer look at the data surrounding their people and processes.

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When it comes to international, diverse companies, it can be difficult to replace a host of decentralized practices and initiatives with a single solution that addresses the needs of every team. The opportunity to leverage data is great, but it’s also all too easy for us to make sweeping decisions that don’t work in a specific office or department—and to lose our focus on the individual.

Used correctly, however, people analytics offer an opportunity to use data in a way that addresses different needs and suggests changes at a granular level, while still looking at the bigger picture. As seen in the restaurant example above, data is most effective when it focuses on small details that influence the employee experience. Quite often, it also ends up showing us that our assumptions—and subsequent solutions—are incorrect.

Data provides a channel of communication that doesn’t necessarily depend on a particular language or cultural context, since it is an objective set of information. By being the common ground on which to build a global organization, people analytics can vastly improve a company’s overall performance by reducing redundancy, misunderstandings and poor communication.