9/26/2017

People Analytics. Want to Read the Book for Free?


The deal: I want your feedback! Read People Analytics. Data and Text Analytics for Human Resources. It is free now.

I have finished writing this book. It is 367 pages long. I am a firm believer of feedback. I want your help!
  1. Download here the Intro and Chapter 1.
  2. Read it.
  3. If you want to continue reading, drop me a line (on my LinkedIn profile) and I send you the whole book for free.
  4. Continue reading.
  5. Reciprocate by giving feedback.

About the Book

My purpose: writing a book both enjoyable and instructive. The very few who have read it assured me that it is a practical book, an easy read, sometimes provocative, and always amusing.
I usually define myself as an ”optimistic simpleton.” I can’t help but see the positive side of almost everything, a little bit like Forrest Gump, but plumper and with no known tendency to run. It shows. In my optimistic naivite, I imagine that there is a hidden and silent minority who enjoys, as I do, both the practicality of the analytics culture and its scientific approach as well as a peculiar sense of humor, historical references, philosophy, and film. You will be finding extravagant sections called: “Descartes Explains the Reason for the X Files”, "The True Story of the Master Who Taught Me the Great Lesson of What for?" or “Digression On Questions, Philosophy, Obstetrics, and Criticism.”
Is This Book for Me?
This book is aimed at professionals who have responsibilities over people and want to learn how data and analytics will help them make better decisions.
More specifically, it is aimed at:
• Managers of organizations who want to learn how to extract more value from the data collected by the HR department.
• HR leaders who wish to either incorporate analytics and data departments or acquire new techniques of value extraction using data.
• HR pros who want to learn how to use people analytics in the daily tasks of their professional activity.
• Data-Analytics fans or pros, even if they are not specialized in human resources.

What Will You Find in This Book?

People Analytics is divided into five parts and is a pedagogical tour quite similar to the one we do in the Master program in People Analytics that I lead in Spain.
The book starts with general concepts but narrows to practical tasks of what needs to be done with each of our tools in order to solve the analytics problems that are presented.

In the first part of the book (I. An Introduction to People Analytics):

Chapter 1. What Is People Analytics about? I examine the case of Google. I walk you through the history of people analytics, from the beginning of the 20th century to the present day. I confess everything that we owe to marketing in people analytics.
Chapter 2. People Analytics: Why and How. I present the reason for using people analytics and its near-universal methodology.
Chapter 3. Big Data and People Analytics. I explain what big data really is and where it is applied in people analytics, beyond what we are told in the media. I make a small foray into the area of ethics.

In the second part of the book (II. Statistics, Errors, and Biases):

Chapter 4. Intuition vs. Data Analysis. I warn of the danger of cognitive biases in decision-making.
Chapter 5. Defense Against the Dark Arts: A Guide to Not Being Fooled by Data. I reveal how data can be deceptive. It includes the story of how the Bill and Melinda Gates Foundation squandered $1.7 billion because it did not consider the Law of Small Numbers. It ends with a sensible guide to cleaning data.

In the third part (III. Strategy and Economics):

Chapter 6. Building a Business Case for Human Resources. A business case is used to justify an investment in a project. I’ll teach you how to build one.
Chapter 7. Taking the Right Measurements. I go over the most frequently used metrics in people analytics, adding a guide to show you how to calculate the cost of attrition.
Chapter 8. Lifetime Value, the Gold Standard. I’ll show you how to measure the net contribution that an employee makes to an organization while working there.
Chapter 9. Employee Experience, Engagement, and the Bottom Line. Results of engagement and climate surveys must be adapted according to changes in the bottom line.
Chapter 10. Performance and Compensations. I show you how to use data to evaluate performance. This will allow you to link incentives closer to the behaviors you want to reward.

In the fourth part (IV. Learning to Work with Real Cases of People Analytics): I provide real, practical HR cases that work with structured data.

Chapter 11. Clear and Simple Algorithms for People Management. I explain the importance of understanding the real causes of a problem and of learning how to anticipate the direct and indirect consequences of a performance improvement activities.
Chapter 12. Surveys and the Lingering Doubters. I demonstrate how science also needs urgent repairs in order to get reliable data. I teach you to use the ”Cronbach’s alpha” and the ”Inter-Class Correlation.”
Chapter 13. Segmentations: Divide and Conquer. Clustering helps to segment by actual behaviors and not only by sociodemographic variables.
Chapter 14. Predictive Selection. I’ll teach you how to predict which candidate will be high-performing, effective, loyal, and aligned with the corporate culture.
Chapter 15. Turnover. I demonstrate how to use analytics to understand the causes that generate talent leakage in the organization and to build a predictive model to be able to anticipate people leaving.

In the fifth part (V: Text Analytics for Human Resources):

Chapter 16. Open-Text Analytics and the Voice of the Employee. The feedback that employees provide is extremely valuable for understanding strengths and weaknesses, and above all, areas where we can make a difference.
I am repeating my SOS message again.
I want your help!
  1. Download here the Intro and Chapter 1.
  2. Read it.
  3. If you want to continue reading, drop me a line (on my LinkedIn profile) and I send you the whole book for free.
  4. Continue reading.
  5. Reciprocate by giving feedback.



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