People Analytics Deconstructed

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: 

1. Talent management is more than hiring. It spans workforce planning, development, retention, and transitions—all aimed at growing both individuals and the organization.
2. Measure what matters. Organizations often collect too much data without knowing how to use it. The focus should be on meaningful, actionable insights—not just numbers.
3. AI is accelerating talent work. From training design to communication, AI offers efficiency—but ethical use and human oversight are key.
4. Future readiness is top of mind. Organizations must build skill-based pipelines and prepare current employees for emerging challenges.
5. Career paths are evolving. The traditional step-by-step ladder is giving way to skill-based growth and internal mobility. Flexibility and visibility are essential.

Contact Dr. Brown Meola

• LinkedIn Profile 

Contact Millan 

We’d love to hear from you! If there’s a people analytics topic you’re curious about or would like to explore further, don’t hesitate to reach out to us at [email protected]. For more information about our services and insights, visit our website at www.millanchicago.com, or connect with us on LinkedIn: Millan Chicago. 

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 intentional, ensure feedback is truly heard and understood, and foster a culture of trust where feedback is seen as an opportunity for growth. Ultimately, the conversation explores how organizations can move beyond anecdotal evidence to quantify the ROI of investing in feedback processes and training, linking it to tangible outcomes like improved performance, reduced turnover, and increased engagement. 

Key Takeaways

• Effective feedback is a two-way street that requires both skillful delivery and a receptive mindset. 
• Measuring the impact of feedback goes beyond simply tracking conversations and involves looking at tangible outcomes and changes in behavior. 
• Building a culture of trust and psychological safety is essential for individuals to be open to receiving and acting on feedback. 
• Organizations should consider investing in training for both giving and receiving feedback to maximize its positive impact on performance and culture. 
• Connecting feedback to individual and organizational goals increases its relevance and drives meaningful change. 

Contact Us 

We’d love to hear from you! If there’s a people analytics topic you’re curious about or would like to explore further, don’t hesitate to reach out to us at [email protected].

For more information about our services and insights, visit our website at www.millanchicago.com, or connect with us on LinkedIn: Millan Chicago. 

Contact Shea Smith 

Shea Smith is an executive coach who equips high-performing teams and individuals to break through barriers, expand their vision, and deliver outstanding results. Since 2014, she’s been helping leaders get unstuck, think bigger, and achieve lasting transformation that redefines what’s possible. Shea is passionate about empowering her clients to push beyond high performance and unlock extraordinary growth.

thesheasmith.com, [email protected]

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 studies to provide a clearer and more reliable picture of the effect of a certain phenomenon. The goal is to take the variability from individual studies and make informed decisions based on a broader data set. 
• Why does a meta-analysis matter? Meta-analyses are essential when making significant decisions in work settings, like policy or procedural changes. Instead of relying on anecdotal evidence or small sample studies, practitioners can reference meta-analyses that aggregate findings from hundreds of studies for more reliable data. 
• Are all meta-analyses created equal? Not all meta-analyses are created equal. It’s crucial to consider the quality of studies included and the transparency of the meta-analysis process. Look for proper reporting guidelines and study inclusion/exclusion criteria. 
• If you’re making decisions in your organization, consider reviewing meta-analyses related to your topic. Whether it’s remote work, employee well-being, or another area, see what broader research trends tell you. 

Contact Us 

We’d love to hear from you! If there’s a people analytics topic you’re curious about or would like to explore further, don’t hesitate to reach out to us at [email protected].  

For more information about our services and insights, visit our website at www.millanchicago.com, or connect with us on LinkedIn: Millan Chicago. 

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 from. 

Key Takeaways: 

1. The Role of Data in Performance Evaluation: Using data in performance management is crucial for accurate assessments. The shift away from traditional numeric evaluations has brought challenges when performance isn’t evaluated using quantitative approaches. 
2. Culture Drives the Process, Not Technology: Technology alone can’t create an effective performance management system. Company culture should guide the process, ensuring it aligns with core values, mission, and business goals. 
3. Goal Setting is Key: Clear, measurable goals are at the heart of successful performance management. Regina highlights how effective goal setting connects to broader performance strategies and how it serves as a tool for growth and improvement. 
4. Adapting Performance Management to Hybrid Environments: Performance management needs to adapt to hybrid work environments. The key components for success include clear communication, flexibility, and consistent feedback, regardless of whether employees are in the office or working remotely. 

Contact Regina Taute

• LinkedIn
• Collective Growth
• Email - [email protected] 

Contact Millan 

We’d love to hear from you! If there’s a people analytics topic you’re curious about or would like to explore further, don’t hesitate to reach out to us at [email protected]. 

For more information about our services and insights, visit our website at www.millanchicago.com, or connect with us on LinkedIn: Millan Chicago. 

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 behind what we often misinterpret as generational divides. If you’re ready for a fresh perspective on how age-related assumptions influence the workplace, don’t miss this eye-opening discussion! 

Key Takeaways: 

1. Generational differences? Not so much. Dr. Costanza walks us through his research (including two extensive meta-analyses!) showing that there’s no scientific basis for generational differences in the workplace. 
2. The harm in labeling. When we use generational labels, we risk stereotyping individuals based solely on when they were born—oversimplifying the unique experiences and skills they bring to the table. 
3. The real causes of 'differences.' Many of the traits we attribute to specific generations are more likely to be cohort or period effects—shaped by historical or cultural events rather than age. 
4. Focus on opportunities, not generations. Instead of making decisions based on generational assumptions, organizations should ensure that opportunities are accessible to everyone, regardless of age. Tailoring solutions to perceived generational gaps often misses the mark. 

Contact Dr. Costanza 

• Academic Website 
• LinkedIn
• Email - [email protected]

Contact Millan 

We’d love to hear from you! If there’s a people analytics topic you’re curious about or would like to explore further, don’t hesitate to reach out to us at [email protected]. 

For more information about our services and insights, visit our website at www.millanchicago.com, or connect with us on LinkedIn: Millan Chicago. 

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 when solving challenges using analytics. They break down the key steps to take when making strategic decisions that are informed by data. 

Key Takeaways: 

• As we kick off the new year, think about what’s truly necessary for your organization. Use a data-informed approach to make strategic decisions. 
• The essential steps for making data-driven decisions: 
  1. Understand the focus of the effort – What are you trying to solve? What decision needs to be made? 
  2. Collect key information – With a clear focus, gather the right data to inform your decision. 
  3. Analyze the information – Understand what the data is telling you and apply the right techniques to summarize and identify patterns or differences. 
  4. Interpret the analysis – Relate the analysis to the initial challenge to guide you toward a data-informed decision. 

Tune in for a fresh start to Season 2, where we’ll continue exploring how analytics can shape the future of work! 

 Contact Us!  

We’d love to hear from you! If there’s a people analytics topic you’re curious about or would like to explore further, don’t hesitate to reach out to us at [email protected]. 

For more information about our services and insights, visit our website at www.millanchicago.com, or connect with us on LinkedIn: Millan Chicago. 

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 which factors? 
• How do we interpret results?

 Key Takeaways:  

1. We should look at the results after rotating the initial solution. Factor rotation can be orthogonal or oblique. Oblique solutions allow for correlations between factors and orthogonal solution force them to remain independent. In most situations, we would likely start with an oblique rotation. 
2. We can determine the number of factors using a few different approaches. Kaiser’s criterion retains factors with eigenvalues greater than 1.00. A scree plot visualizes the eigenvalues of each factor from largest to smallest. We look for where the plot “flattens out” to determine the number of factors to retain. A parallel analysis simulates results from random data that have the same structure as our focal data (i.e., same number of observations and items) and produces associated eigenvalues. We compare our observed eigenvalues to those produced through the parallel analysis and retain those in which our eigenvalues are greater. 
3. When we associate a given item to a particular factor, we are looking for the largest loading. We generally set a cut of at least .30 or .40 to associate an item with a factor. If an item has no loadings higher than our cut, we will say the item doesn’t load on any factor and discard from further analysis. If an item has a high loading on multiple factors, we will seek to understand why and may choose to either drop or retain the item based on the context.
4. We ended the episode by briefly talking about confirmatory factor analysis (CFA) as an alternative to EFA.

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 the difference between an EFA and principal components analysis (PCA)? 
• What format should the data be in to complete the EFA?  
• How does EFA work?  
• What is a loading matrix?  

Key Takeaways:  

• A factor analysis is useful to determine whether you are measuring what you intend to measure with a survey. We continue our example of measuring engagement with four dimensions (satisfaction with manager, satisfaction with co-workers, satisfaction with compensation, satisfaction with working conditions). 
• There are three main aspects to conducting an EFA. First, you need to decide on the type of analysis (I.e., PCA, EFA). Second, you need to rotate the solution. Third, you need to interpret the results. In this episode, we cover the first and part of the second steps in EFA.  
• We also discussed the concept of a loading matrix. First, each item is correlated with each factor. Each correlation can be squared to get the percentage of variance explained. Second, the sum of all the squared values down a column are computed, which is the eigenvalue. Third, communality is determined by summing across the row for each item. Finally, the uniqueness can be computed by 1-communality.  
• The ultimate goal is to associate each item with a factor. The initial solution will almost never allow us to see the underlying structure. The concept of rotation was briefly mentioned and is covered in additional detail in the next episode.  

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 them. 
• We talked about approaches to writing items (for example, do we do it as a group?, do we ask stakeholders to comment?, do we pilot test?). In particular, we talked about balancing the need to get feedback from different stakeholders while being as efficient as possible. 
• We talked about the negative outcomes that may occur if we don't carefully consider whether individuals will interpret the items in the same way. 
• We spent time talking about the number of scale points that we might include on our engagement. We emphasized the importance of balancing giving people options so that we get variability with a need to ensure our measures are reliable. 
• We also talked about the importance of using the best anchors with the goal of ensuring that we collect the most accurate information. 
• We emphasized the importance of a clear communication plan for launching and administering the survey. 

Key Takeaways:  

• When creating an engagement measure, be sure that your items are clear and fit within the test blueprint. You should also ensure that the items are interpreted consistently by respondents. This can be facilitated through engaging various stakeholders during the item writing process. 
• Think carefully about the number of scale points that you will use and the anchors that will accompany each of those scale points. Good items can be undercut if the anchors and/or scale points create confusion or frustration for the respondents. 
• Make sure that whenever a survey is launched that there is a clear and comprehensive communication plan in place to ensure that people understand what is being measured, how the data are being used, and why it is important to respond. 

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 thinking about whether our survey is measuring one big factor or is comprised of several subdimensions. 
• The importance of specificity in defining what you want to measure. 
• The importance of considering how engagement scores will ultimately be used. Specifically, are we interested in using overall scores or are dimension scores more helpful for decision making of building models (such as flight risk)? 
• The definition as the foundation for creating a test blueprint. The blueprint helps ensure that we measure exactly what we want to measure (nothing more or nothing less) 
• Aspects of item writing were also 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 them. 

Key Takeaways:  

• When creating an engagement measure (or any measure), a critical step is to first carefully define what it is you are interested in measuring. Incompleteness, ambiguity or inaccuracy at the outset will lead to a poor measure. 
• Carefully consider the dimensionality of what it is you are measuring. The dimensionality will serve as a foundation for creating the measure and for evaluating its validity. 
• Every measure you use should always have a test blueprint that serves as a framework for guiding item writing 
• When you write items, keep in mind that they are the only way you have of communicating with your respondents. Write items that are clear and to the point and presented in a manner that facilitates ease of understanding.