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Are you responsible for understanding an employees’ experience? Have you tried to incorporate people analytics in your organization but have struggled? Have you ever wondered what it means to have a data culture? Would you like to make more data-driven decisions? These are the kinds of discussions you can expect to hear on People Analytics Deconstructed. Co-hosts Ron Landis and Jennifer Miller are co-founders of Millan Chicago, a data science consulting company dedicated to helping organizations make the most out of their data. Each week, they will ‘deconstruct’ modern and contemporary topics in the People Analytics space.
26 Episodes
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In the inaugural episode, co-hosts Ron Landis and Jennifer Miller introduce their podcast, People Analytics Deconstructed. They discuss the ever-growing field of People Analytics, sometimes also commonly referred to as HR Analytics or Workforce Analytics. In this episode, we had conversations around these questions: What is People Analytics? What are some of the challenges that organizations face in the area of People AnalyticsWhat are some clear steps that HR professionals ...
In this special episode, co-hosts Ron Landis and Jennifer Miller discuss what's on the horizon for the field of people analytics in 2022. They first set the backdrop by discussing recent events, including the impact of the ongoing pandemic and recent exodus of people from the workforce. Next, they discuss what to watch for in people analytic for 2022. In this episode, we had conversations around these questions: How does the ongoing pandemic contribute to changes in the fiel...
In this episode, co-hosts Ron Landis and Jennifer Miller discuss the importance of utilizing data to make data-driven decisions. While many organizations have turned to analytics to better understand customers, employees, and processes, many are struggling to effectively get the most out of their data. Often, companies fail to use data to its fullest potential because many often lack a strong data culture. In this episode, they had conversations around these questions: What i...
In our first technically-focused episode, co-hosts Ron Landis and Jennifer Miller deconstruct a common statistical technique called linear regression. They focus on how regression can be used to better understand the relations between key drivers of important outcomes. In this episode, they had conversations around these questions: What is linear regression? How are regression analyses used in organizational contexts? How can linear regression be used to drive optimal b...
In this episode, co-hosts Ron Landis and Jennifer Miller deconstruct employee experience. While many organizations have historically focused on employee engagement, there has been a shift to focus on the broader set of experiences employees have within their organization. This increased focus on the experience is in part due to the ongoing pandemic and the significant number of individuals leaving the workforce. In this episode, we had conversations around these questions: What is...
In this episode, co-hosts Ron Landis and Jennifer Miller deconstruct data literacy. Data is increasingly important to driving important decisions. While many organizations have access to even more data than before, most organizations could gain significant benefits better using their data to its fullest potential. One of the primary reasons for not maximizing the use of data is that many employees do not have key data literacy skills. In this episode, we had conversations around t...
In this episode, co-hosts Ron Landis and Jennifer Miller deconstruct organizational network analysis or sometimes referred to as ONA. Social interactions are becoming increasingly important to understand in the context of organizational success. While many have access to data related to interactions (i.e., communication patterns including email, chat) little has been done to analyze those patterns. Using ONA to understand and quantify such relational data provides organizations with a means f...
2In another technically-focused episode, co-hosts Ron Landis and Jennifer Miller deconstruct a statistical technique called logistic regression. They focus on how logistic models can be used to predict the likelihood of a particular outcome. Given the numerous organizational outcomes that are binary in nature (for example, turnover, absence, or promotion), logistic models can provide important insights as to the drivers of such variables. In this episode, we had conversations around these...
In the first episode of a special three part mini-series about measurement, co-hosts Ron Landis and Jennifer Miller discuss how measures impact People Analytics. In this episode, we had conversations around these questions: What does it mean to measure employee behavior in an organizational context? Why is measurement foundational to People Analytics? What is the process to ensuring that measures are good? What are some of the challenges that organizations face w...
In the second episode of a special three part mini-series about measurement, co-hosts Ron Landis and Jennifer Miller discuss how reliability of measurement is critical in People Analytics. In this episode, we had conversations around these questions: What is reliability? Why is it is important to consider the reliability of measures? What are the different types of reliability? How do I determine the reliability of a measure? What are some of the step...
In the third episode of a special three part mini-series about measurement, co-hosts Ron Landis and Jennifer Miller discuss how validity of measurement is critical in People Analytics. In this episode, we had conversations around these questions: What is validity? Why is it is important to consider the validity of measures? What are the different types of validity? How do I determine the validity of a measure? What are some of the steps that HR ...
In this episode, co-hosts Ron Landis and Jennifer Miller deconstruct the concept of a flight risk model. They focus on how these types of models can be used to predict the degree to which employees are at risk of leaving an organization. In this episode, we had conversations around these questions: What is a flight risk model? Why are flight risk models important? How do you build a flight risk model? What are some clear steps that HR professionals can take to bu...
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? &n...
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 requirem...
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 an...
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 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 tendenc...
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, 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 d...
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...
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