People Analytics Deconstructed
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.
People Analytics Deconstructed
What are Predictive Models?
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Millan Chicago
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Season 1
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Episode 14
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.
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