Data and Text Analytics for Human Resources
About the Book
We have more information at our disposal about tastes, opinions, and certain behaviors that could be very useful in making decisions about performance, salaries, turnover rate, leaves of absence, etc.
Accordingly, People Analytics processes that information through analysis tools that allow you to look at talent management decisions in a new way. People Analytics is based on data analysis as opposed to traditional methods of relationships or personal experiences.
I have worked in the area of analytics and data for quite a few years. Lately I've been specializing in data for use in Human Resources departments. I have spent a lot of time doing consulting work and training others. Now, I also direct People Analytics Pro (http://www.peopleanalyticspro.com), where we host a 140-hour Master's program that teaches HR professionals to use data and analytics tools to make informed decisions that improve employee management in an organization. In other words, we help prepare HR analysts.
This book was born from this experience. I intend to give a practical tour of the most important points that are taught in the People Analytics Pro Master's program. In fact, this book is conceived of as a practical guide for how to put into place what I previously only talked about.
People Analytics offers a unique opportunity for human capital professionals to position themselves as strategic senior management partners, using analytically proven techniques to hire and retain employees who drive higher value in companies.
Table of Contents
- Jose Gonzalez: People Analytics the easy way
Burn After Reading!
- More about People Analytics
- Is This Book for Me?
- Structured and Unstructured Data
- What Will You Find in This Book?
- Table of Instructions
- Without further ado, I want you to know more about me and my flaws.
- Vampirizing Data Coaching
- It's a fun book that you can learn from.
- Illustrations by Marisa Echarri
I. An Introduction to People Analytics
01. What Is People Analytics About?
- 1. About Moneyball
- 2. The Most Valuable Asset is Also the Most Expensive.
- 3. Tomayto, Tomahto
- 4. Big Data for Human Resources
- 5. The Path to Transformation
- Recommended Reading
02. People Analytics: Why and How
- 1. If You Could Only Ask One Question
- 2. Result Indicators and Causal Indicators, Happiness and Profit
- 3. Methodology
03. Big Data and People Analytics
- 1. The Great Confusion
- 2. Big Data and Privacy: A Practical Rule to Govern Ethics
- 01. What Is People Analytics About?
II. Statistics, Errors, and Biases
04. Intuition vs. Data Analysis
- 1. The Power of Intuition
- 2. The Synthesis Between Intuition and Data
- Recommended Reading
05. Defense Against the Dark Arts: Don’t Be Fooled by Data
- 1. Correlation Does Not Imply Causality.
- 2. Simpson's Paradox and Causality
- 3. So, How is Causation Demonstrated?
- 4. Too Small of a Sample: the Law of Small Numbers
- 5. A Sensible Guide to Cleaning Data
- 04. Intuition vs. Data Analysis
III. Strategy and Economics
06. Building a Business Case for Human Resources
- 1. What is a Business Case?
- 2. The Business Case as a Trip
07. Taking the Right Measurements
- 1. The Most Frequently Used Metrics in People Analytics
- 2. Calculating Turnover Costs Is a Must.
- 3. Epilogue. Turnover Cost in The Apartment (Billy Wilder 1960)
08. Lifetime Value, the Gold Standard
- 1. The ELTV Should Be a Key Metric in All Organizations. Why?
- Recommended Reading
09. Employee Experience, Engagement, and the Bottom Line
- 1. What Do I Mean by "Engagement?"
- 2. The eNPS, Measuring Company Happiness
10. Performance and Compensations
- 1. If you Can't Measure It…
- 2. Subjective performance measurement
- 3. Objective performance measurement
- 5. Compensation and performance
- 06. Building a Business Case for Human Resources
IV. Learning to Work with Real Cases of People Analytics
11. Clear and Simple Algorithms for People Management
- 1. Responsible Decision-Making
- 2. The Sorcerer's Apprentice
- 3. How Can You Predict the Consequences?
- 4. Good and Bad Algorithms
12. Surveys and the Lingering Doubters
- 1. Theory of Measurement
- 2. There are many of us
- 3. How to Get More Valid Data
13. Segmentations: Divide and Conquer
- 1. Marketing Saw It First
- 2. Monty Python's Life of Brian: "You Are All Different"
- 3. Recency, Frequency, and Monetary Value
14. Predictive Selection
- 1. Eternal Return
- 2. Google and Selection
- 3. Daniel Kahneman: An Algorithm or at Least a Structured Interview
- 5. Let's go back to Google. How many interviews are needed to hire a "Googler?" The rule of 4.
- 6. The Best Candidates from a Turnover Perspective). Predictive Turnover Analysis
- Recommended Reading
- 1. Data about Retention and Turnover
- 2. Let's Clear Up Some Previous Concepts
- 3. Analyzing information you don't see: Abraham Wald and the Lancaster bombers
- 5. Predictive Model for Talent Flight
- 11. Clear and Simple Algorithms for People Management
V. Text Analytics for Human Resources
16. Open-Text Analytics and the Voice of the Employee
- 1. Data Sources
- 2. Memento
- 3. Why do you need open-ended survey questions?
- 4. Text Analytics: Value as a Wedding Cake
- 5. How to Incorporate Text Analytics into the Voice of the Employee
- 16. Open-Text Analytics and the Voice of the Employee
- Annex. Basic Bibliography on People Analytics
Right the Wronghttp://www.oxfamamerica.org
Oxfam America is a global organization working to right the wrongs of poverty, hunger, and injustice. We save lives, develop long-term solutions to poverty, and campaign for social change. As one of 17 members of the international Oxfam confederation, we work with people in more than 90 countries to create lasting solutions.
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