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Predictive Analytics for Human Resources

A practical, step-by-step guide to applying descriptive, predictive, and prescriptive analytics to human capital so HR can uncover the causal drivers of workforce outcomes and connect talent decisions to business value.

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What it’s about

Written by the father of HR metrics, Jac Fitz-enz, and analytics practitioner John Mattox, this book demystifies predictive analytics for human resources by showing that analytics is first a logical mental framework and only second a set of statistical operations. Through a running case study of the 'Retain & Grow' talent initiative, it walks readers from gathering efficiency, effectiveness, and outcome data, through descriptive dashboards, correlation, regression, and structural equation modeling, all the way to predicting individual productivity and profitability. It teaches not just the statistics but the salesmanship, sponsorship, change management, and questioning discipline needed to build an analytics unit or culture, sell it to the C-level, and turn disparate data into actionable business intelligence. Grounded in frameworks like the Talent Development Reporting Principles and Boudreau and Ramstad's optimization model, it argues that people are best measured not as inert assets but through the efficiency, effectiveness, and outcomes of their processes—and that the future of HR belongs to those who can 'manage tomorrow today.'

The through-line

Who it’s for
An HR, talent, or learning professional (or analyst/manager) who wants to add measurable value to their organization and earn credibility with business leaders.
The problem
HR generates activity and cost reports but cannot connect talent decisions to business outcomes like productivity, retention, and profitability. They feel overwhelmed, ignorant, or powerless in a world of Big Data and fast computers, fearing they lack the statistical expertise to be taken seriously.
The plan
  1. Clarify vision, brand, and culture and ask logical questions to define the true problem and its purpose.
  2. Identify and gather efficiency, effectiveness, and outcome metrics, securing data ownership and quality.
  3. Display data in descriptive dashboards and reports, then relate it to internal and external forces.
  4. Build predictive models using correlation, regression, or structural equation modeling to find causal drivers.
  5. Sell the effort to the C-level with a sponsor and translate insights into financial outcomes and prescriptions.
The payoff
HR provides actionable, financially framed intelligence that executives use for decision making. · Talent inputs are optimized to improve productivity, retention, and profitability. · The analyst gains credibility, a seat at the table, and possibly a permanent analytics unit or culture.

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