Over the past decade or so, increases in compute power, emergence of friendly analytic tools and an explosion of data have created a wonderful opportunity to bring more analytical rigor to nearly every imaginable question. Not coincidentally, organizations are increasingly looking to apply all that data and capability to what is typically their greatest area of expense and their greatest strategic differentiator—their people. For too long, many of the most critical decisions in an organization—people decisions—had been guided by gut instinct or borrowed ‘best practices’ and the democratization of people analytics opened up enticing pathways to fix that. Suddenly, analysts who were originally interested in data problems began to be interested in people problems, and HR professionals who had dedicated their careers to solving people problems needed more sophisticated analysis and data storytelling to make their cases and to refine their approaches for greater efficiency, effectiveness and impact.
Doing data work with people in organizations has complexities that some other types of data work doesn’t. Often, the employee populations are relatively smaller than data sets used in other areas, sometimes limiting the methods than can be used. Various regulatory requirements may dictate what data can be gathered and used, and what types of evidence might be required for various programs or people strategies. Human behavior and organizations are sufficiently complex that typically, multiple factors work together in influencing an outcome. Effects can be subtle, or meaningful only in combination, or difficult to tease apart. While in many disciplines, prediction is the most important aim, for most people analytics projects and practitioners, understanding why something is happening is critical.
While the universe of analytical approaches is wonderful and vast, the best ‘swiss army knife’ we have in people analytics is regression. This volume is an accessible, targeted work aimed directly at supporting professionals doing people analytics work. I’ve had the privilege of knowing and respecting Keith McNulty for many years – he is the rare and marvelous individual who is deeply expert in the mechanics of data and analytics, curious about and steeped in the opportunities to improve the effectiveness and well-being of people at work, and a gifted teacher and storyteller. He is among the most prolific standard bearers for people analytics. This new open source volume is in keeping with many years of contributions to the practice of understanding people at work.
After nearly 30 years of doing people analytics work and the privilege of leading people analytics teams at several leading global organizations, I am still excited by the problems we get to solve, the insights we get to spawn, and the tremendous impact we can have on organizations and the people that comprise them. This work is human and technical and important and exciting and deeply gratifying. I hope that you will find this Handbook of Regression Modeling in People Analytics helps you uncover new truths and create positive impacts in your own work.
Alexis A. Fink
Alexis A. Fink, PhD is a leading figure in people analytics and has led major people analytics teams at Microsoft and Intel before her current role as Vice President of People Analytics and Workforce Strategy at Facebook. She is a Fellow of the Society for Industrial and Organizational Psychology and is a frequent author, journal editor and research leader in her field.