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 requirements needed to utilize forecasting in organizational contexts?   
• What are some clear steps that HR professionals can take to use predictive models?  

Key Takeaways:  

• In general, we can think about three broad categories of data analytics: descriptive, inferential, and predictive.  
• Ron and Jennifer provide a framework of how to build predictive models. First, all the relevant variables and relations among those variables need to be in the model. Second, the model needs to have data divided into a training set and test set to determine how well the model predicts the data. Third, they discuss how the model can be used in organizational contexts.  

Related Links  

• Millan Chicago