In this rich and timely conversation, Dr. Julianne Brown Meola joins Jennifer and Ron to explore the evolving landscape of talent management. Drawing on her extensive experience across multiple industries—including retail giants and Fortune 500 companies—Julianne offers a candid, practical, and strategic view of how organizations can navigate workforce challenges in a rapidly shifting environment. Key Takeaways: Talent management is more than hiring. It spans workforce planning, de...

In this episode, Shea Smith, an executive coach who helps leaders get unstuck, shares her insights on the importance of feedback for team and organizational growth. We discuss how to capture and measure the behavior of giving and receiving feedback as well as the challenges in quantifying feedback. The discussion highlights that ineffective feedback often stems from vague delivery, poor timing, and a lack of connection to individual and organizational goals. To improve, leaders need to be int...

In this episode, we’re diving into the world of meta-analysis and its importance for decision-making in professional settings. While we won’t get into the technical aspects, we will focus on how to effectively use and what to consider when reading a meta-analysis. We’ll break down why this technique is critical, especially for those making policy or procedural decisions based on research. Key Takeaways: What is a meta-analysis? A meta-analysis combines data from multiple st...

In this episode, we sit down with Regina Taute, a seasoned Talent Management expert with over 20 years of experience. Regina shares her insights on the evolving role of data in performance management, exploring how different work environments, including hybrid models, can impact performance evaluation. We discuss why technology should support, not define, your organizational culture, and the critical components needed for successful performance management, no matter where your team is working...

In this episode, Jennifer and Ron sit down with Dr. David Costanza, a Professor of Commerce at the University of Virginia, to explore his groundbreaking research on generational differences in the workplace. While it’s widely believed that different generations have distinct traits, Dr. Costanza reveals there’s no solid scientific evidence to support this idea. They dig into the dangers of using generational labels, how these labels shape workplace dynamics, and uncover the real factors behin...

People Analytics Deconstructed is back for Season 2! In this season opener, hosts Ron and Jennifer kick things off by discussing what's in store for the upcoming season. From guest interviews with industry and academic experts to deep dives into workforce analytics, this season promises insightful conversations for anyone passionate about using data to drive decisions in the workplace. In this episode, Ron and Jennifer discuss the foundational framework that organizations should follow ...

In this episode, co-hosts Jennifer Miller and Ron Landis continue their conversation about developing a measure of employee engagement. This episode is the second part of a discussion about how to use a statistical technique called factor analysis to examine the dimensions of employee engagement. In this episode, we had conversations around these questions: What is rotation in factor analysis?How do we determine the number of factors to retain?How do we identify which items load on wh...

In this episode, co-hosts Jennifer Miller and Ron Landis continue their conversation about developing a measure of employee engagement. In this episode they focus on how to use a statistical technique called factor analysis to examine the dimensions of employee engagement. In this episode, we had conversations around these questions: What is factor analysis? What is the difference between exploratory factor analysis (EFA) and confirmatory factor analysis (CFA)? What is...

In this episode, co-hosts Jennifer Miller and Ron Landis continue their conversation about developing a measure of employee engagement. They identify and discuss several steps/phases that should be present when we develop measures of any kind. In this episode, we had conversations around these topics: Aspects of item writing were discussed. In particular, we talked about making sure that we carefully consider how items will be read and interpreted by people who will respond to t...

In this episode, co-hosts Jennifer Miller and Ron Landis start their conversation about developing a measure of employee engagement. They identify and discuss several steps/phases that should be present when we develop measures of any kind. In this episode, we had conversations around: The importance of clearly, accurately, and comprehensively defining what it is that we are interested in measuring (e.g., engagement). As part of the definition process, the importance of thin...

In this episode, co-hosts Jennifer Miller and Ron Landis continue their discussion on the importance of data cleaning and management. They review three of five aspects of data cleaning that are critical to checking prior to the analytic phase. In this episode, they discuss how to check for linearity and normality, outliers, and multicollinearity. In this episode, we had conversations around these questions: How do you check for linearity and normality in a data set? ...

In this episode, co-hosts Jennifer Miller and Ron Landis discuss the importance of data cleaning and management. They identify five aspects of data cleaning that are critical to checking prior to the analytic phase. In some cases, data management is often embedded in the data encoding and storage process (I.e., certain rules are in place to ensure that data fields can only handle one type of data such as a date). In this episode, they discuss how to check for data accuracy and what to do with...

In another technically focused episode, co-hosts Jennifer Miller and Ron Landis discuss how to use multiple linear regression to test models involving moderation (or interaction). In episode 18, we discussed multiple linear regression in which we used multiple variables to predict the outcome or criterion variable. But what happens if you have a situation in which the relation between the predictor and outcome variable is actually dependent upon (or is conditional upon) the level of a third v...

In this episode, co-hosts Jennifer Miller and Ron Landis discuss the emerging field of artificial intelligence (AI). In particular, they discuss machine learning and two broad categories of algorithms, unsupervised and supervised learning. In this podcast episode, we had conversations around these machine learning questions: What is artificial intelligence? What is machine learning? What are some applications of machine learning in People Analytics? What is the...

Earlier in this season, we discussed a commonly used technique called simple linear regression. In this technique, we used one variable to predict an outcome. But, let's face it – life is a little bit more complex than just having one predictor and many times, organizations have lots of data that can be used to predict an outcome. In another technically focused episode, co-hosts Ron Landis and Jennifer Miller deconstruct multiple linear regression. They focus on using multiple predictors to p...

In this episode, Ron Landis and Jennifer Miller deconstruct the importance of utilizing descriptive statistics as the foundation of starting the data analytic process. As many advanced statistical techniques are built on descriptives such as the mean and standard deviation, it is imperative to understand the characteristics of the data set being analyzed. In this episode, they have conservations around the following questions: What are the various ways in which central tende...

In this episode, Ron Landis and Jennifer Miller deconstruct the key characteristics to consider when developing visualizations. In working with data, many are faced with decisions about how to communicate results. Given that one of the primary functions of analytics is to inform various stakeholders of the results, visualizations and other representations of data often play an important role in communicating findings. In this episode, we had conversations around these questions: ...

In the first "Analytics in Practice" episode, co-hosts Ron Landis and Jennifer Miller deconstruct how to utilize the data analytic process for performance appraisal. Given the widespread and varied use of performance assessments in organizations, there are numerous opportunities to reap the benefits of applying data analytic thinking to the process. In this episode, we had conversations around these questions: What are some of the decisions to consider for each step of the data ...

In this episode, co-hosts Ron Landis and Jennifer Miller deconstruct building predictive models and specifically, utilizing forecasting in organizational context. In this episode, we had conversations around these questions: What are different types of data analytics? What are some of the decisions to consider when building predictive models? What are some contexts in which predictive models can be used in organizations? What are some of the data analytic requir...

In this episode, co-hosts Ron Landis and Jennifer Miller deconstruct natural language processing (NLP), a technique used to drive insights from text based information. They focus on how natural language processing can uncover information from different types of text such as performance management reviews, employee engagement responses, pulse survey responses, and job descriptions. In this episode, we had conversations around these questions: What is natural language processing? ...