Summary of Predictive HR Analytics

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Predictive HR Analytics book summary

Editorial Rating

8

Qualities

  • Comprehensive
  • Applicable
  • Concrete Examples

Recommendation

In this true hands-on workbook, analytics experts Martin R. Edwards and Kirsten Edwards include everything you need to learn predictive HR analytics using cloud-based tools. Within its 509 pages, you can find explanations, scenarios, warnings of the limitations of predictive analytics, and dozens of cases to help you put your results in context and interpret them with business relevance. This critical combination equips HR professionals with the tools needed to make better decisions bounded by data privacy and ethics. 

About the Authors

Martin R. Edwards teaches and consults in HR analytics at universities, and with client firms worldwide. Kirsten Edwards leads analytics at Empathix and lectures on the topic at Kent and King’s College business schools.

Summary

The era of predictive HR analytics has arrived.

A new era of data and evidence-based decision-making has arrived, and with it, proven techniques for predicting the outcomes of workforce-related decisions and interventions. Predictive analytics explores patterns in the data – the reasons for numbers and outcomes – and what drives them.

HR can, for example, use models to predict which new hires will perform better and stay longer, or what interventions might reduce unwanted turnover. Such data, in turn, equips leaders to make evidence-based business decisions. 

Gain analytics skills by using simple statistical techniques in user-friendly software.

Cloud-based tools such as SPSS and R let HR professionals construct models to investigate data, and find predictive patterns and trends. For example, an increase in one variable (such as recognition) might reveal changes in another (like engagement), which, in turn, drives productivity or turnover. 

Sourcing, obtaining and ensuring the quality of workforce data is the first hurdle for most HR teams. Look for data in human resource...


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