Let’s use People Analytics to change how we teach HR
When students discuss business strategy in case-based learning, they often make recommendations based on past experiences or intuition of what is effective and fair. The problem is that intuition isn’t always right - business students’ experiences may be unique, come from a singular industry or job, or be influenced by a specific boss. Answering questions like, “What should you look for when hiring a new employee?” or “How can you run a performance management conversation effectively?” may seem straightforward, but the nuances of different students’ experiences can leave them with a sense that managing people is a set of soft skills that don’t have hard answers.
Some professors are masters in surfacing these intuitions across students and comparing the logic of each. By doing so, these professors can compare the intuitions to a set of research findings for what should be recommended in the case.
Last spring, I developed a course to change the method of instruction - I taught one of the first People Analytics classes in the country. In this class, I changed the script of teaching conventional HR topics. Instead of relying on lectures and hypothetical cases, and arguing against students’ intuition, I worked with many companies to develop real cases, accompanied with real (but sanitized) employment data, and asked students to develop recommendations based on their analysis of the data.
I wanted to teach the students how to test their intuition and expose them to different theories and research. By doing so, students not only can learn about and evaluate different research findings, but they can also develop the skills to conduct and evaluate their own research.
For instance, in talking about recruitment, students were given a dataset from an organization with two years’ worth of applicant, hiring, and performance data. Students had to analyze the effectiveness of the hiring process and make recommendations to improve the process. We could then talk about different approaches to recruitment and selection, and evaluate which ones have data to support them.
I had three goals. First, I wanted to introduce students to a data-driven way to manage employees and the many HR functions that influence people’s experience at work. We examined data related to recruitment and selection, managing diversity, performance management, employee engagement, and retention.
Second, I wanted to introduce students to the research design process demonstrating how companies should evaluate the effectiveness of their HR functions. Students walked away with a greater understanding of the data required to assess whether their intuition and experience are right.
Finally, I wanted students to develop three mindsets that I believe must be integrated to generate solutions to People Analytics problems.
- Data Sense – a data scientist who is well versed in extracting knowledge and insights from data
- People Sense – a psychologist who understands human behavior and can apply meaning to data about human beings (people data are different than marketing or financial data)
- Business Sense – a business partner who is adept in positioning recommended solutions within the constraints of the organization to change policy
In total, I wrote eight new cases, six of which had datasets from real organizations. Even though the confidentiality of the individual employees and anonymity for the company was maintained, I was still able to provide real insight into how these firms ultimately managed the issues.
The landscape of HR is changing. Companies are using data more and more to manage their people. Organizations should look for future leaders, inside and outside of HR, who can combine business sense with data fluency and an understanding of human psychology. These future leaders require a different vision for how conventional HR topics are taught in business schools and how students are prepared to tackle tricky people issues. By teaching them how People Analytics can help them make better people decisions, students will be able to generate robust insights based on evidence, not just their intuition, and ultimately make work better.
Ethan Burris is an Associate Professor of Management at The University of Texas at Austin McCombs School of Management.
This blog is the first in a series from business school professors who are pushing the study and teaching of human resources forward by employing data, science, and analytics in their curricula.