People Analytics For Dummies
Description
Maximize performance with better data
Developing a successful workforce requires more than a gut check. Data can help guide your decisions on everything from where to seat a team to optimizing production processes to engaging with your employees in ways that ring true to them.
People analytics is the study of your number one business asset-your people-and this book shows you how to collect data, analyze that data, and then apply your findings to create a happier and more engaged workforce.
- Start a people analytics project
- Work with qualitative data
- Collect data via communications
- Find the right tools and approach for analyzing data
If your organization is ready to better understand why high performers leave, why one department has more personnel issues than another, and why employees violate, People Analytics For Dummies makes it easier.
More details
Other editions
Additional editions
Person
Mike West was a founding member of the first people analytics teams at Merck, PetSmart, Google, and Children's Health Dallas before starting his own firm, PeopleAnalyst, LLC. He has helped companies large and small design people analytics applications and start their own people analytics teams. Mike brings a unique perspective about how to use data to create winning companies and great places to work.
Content
Introduction 1
About This Book 1
Foolish Assumptions 2
Icons Used in This Book 3
How This Book is Organized 3
Part 1: Getting Started with People Analytics 3
Part 2: Elevating Your Perspective 4
Part 3: Quantifying the Employee Journey 4
Part 4: Improving Your Game Plan with Science and Statistics 5
Part 5: The Part of Tens 5
Beyond the Book 5
Where to Go from Here 7
Part 1: Getting Started With People Analytics 9
Chapter 1: Introducing People Analytics 11
Defining People Analytics 12
Solving business problems by asking questions 14
Using people data in business analysis 19
Applying statistics to people management 20
Combining people strategy, science, statistics, and systems 21
Blazing a New Trail for Executive Influence and Business Impact 22
Moving from old HR to new HR 22
Using data for continuous improvement 24
Accounting for people in business results 24
Competing in the New Management Frontier 25
Chapter 2: Making the Business Case for People Analytics 27
Getting Executives to Buy into People Analytics 29
Getting started with the ABCs 29
Creating clarity is essential 30
Business case dreams are made of problems, needs, goals 30
Tailoring to the decision maker 31
Peeling the onion 32
Identifying people problems 34
Taking feelings seriously 35
Saving time and money 36
Leading the field (analytically) 37
People Analytics as a Decision Support Tool 38
Formalizing the Business Case 40
Presenting the Business Case 41
Chapter 3: Contrasting People Analytics Approaches 43
Figuring Out What You Are After: Efficiency or Insight 44
Efficiency 44
Insight 45
Having your cake and eating it too 46
Deciding on a Method of Planning 47
Waterfall project management 47
Agile project management 47
Choosing a Mode of Operation 50
Centralized 51
Distributed 52
Part 2: Elevating Your Perspective 55
Chapter 4: Segmenting for Perspective 57
Segmenting Based on Basic Employee Facts 58
"Just the facts, ma'am" 58
The brave new world of segmentation is psychographic and social 62
Visualizing Headcount by Segment 62
Analyzing Metrics by Segment 63
Understanding Segmentation Hierarchies 65
Creating Calculated Segments 68
Company tenure 68
More calculated segment examples 72
Cross-Tabbing for Insight 74
Setting up a dataset for cross-tabs 74
Getting started with cross-tabs 75
Good Advice for Segmenting 78
Chapter 5: Finding Useful Insight in Differences 79
Defining Strategy 80
Focusing on product differentiators 83
Identifying key jobs 85
Identifying the characteristics of key talent 86
Measuring If Your Company is Concentrating Its Resources 87
Concentrating spending on key jobs 88
Concentrating spending on highest performers 88
Finding Differences Worth Creating 93
Chapter 6: Estimating Lifetime Value 95
Introducing Employee Lifetime Value 96
Understanding Why ELV Is Important 97
Applying ELV 99
Calculating Lifetime Value 101
Estimating human capital ROI 102
Estimating average annual compensation cost per segment 103
Estimating average lifetime tenure per segment 103
Calculating the simple ELV per segment by multiplying 104
Refining the simple ELV calculation 106
Identifying the highest-value-producing employee segments 107
Making Better Time-and-Resource Decisions with ELV 108
Drawing Some Bottom Lines 109
Chapter 7: Activating Value 111
Introducing Activated Value 113
The Origin and Purpose of Activated Value 114
The imitation trap 114
The need to streamline your efforts 116
Measuring Activation 118
The calculation nitty-gritty 121
Combining Lifetime Value and Activation with Net Activated Value (NAV) 126
Using Activation for Business Impact 128
Gaining business buy-in on the people analytics research plan 128
Analyzing problems and designing solutions 129
Supporting managers 130
Supporting organizational change 130
Taking Stock 130
Part 3: Quantifying the Employee Journey 131
Chapter 8: Mapping the Employee Journey 133
Standing on the Shoulders of Customer Journey Maps 135
Why an Employee Journey Map? 141
Creating Your Own Employee Journey Map 143
Mapping your map 143
Getting data 144
Using Surveys to Get a Handle on the Employee Journey 145
Pre-Recruiting Market Research Survey 145
Pre-Onsite-Interview survey 148
Post-Onsite-Interview survey 148
Post-Hire Reverse Exit Interview survey 149
14-Day On-Board survey 150
90-Day On-Board Survey 151
Once-Per-Quarter Check-In survey 152
Once-Per-Year Check-In survey 153
Key Talent Exit Survey 155
Making the Employee Journey Map More Useful 157
Using the Feedback You Get to Increase
Employee Lifetime Value 158
Chapter 9: Attraction: Quantifying the Talent Acquisition Phase 159
Introducing Talent Acquisition 160
Making the case for talent acquisition analytics 161
Seeing what can be measured 162
Getting Things Moving with Process Metrics 163
Answering the volume question 164
Answering the efficiency question 172
Answering the speed question 177
Answering the cost question 182
Answering the quality question 184
Using critical-incident technique 185
Chapter 10: Activation: Identifying the ABCs of a Productive Worker 193
Analyzing Antecedents, Behaviors, and Consequences 194
Looking at the ABC framework in action 195
Extrapolating from observed behavior 196
Introducing Models 198
Business models 199
Scientific models 200
Mathematical/statistical models 200
Data models 201
System models 203
Evaluating the Benefits and Limitations of Models 204
Using Models Effectively 206
Getting Started with General People Models 209
Activating employee performance 209
Using models to clarify fuzzy ideas about people 215
The Culture Congruence model 216
Climate 218
Engagement 221
Chapter 11: Attrition: Analyzing Employee Commitment and Attrition 225
Getting Beyond the Common Misconceptions about Attrition 226
Measuring Employee Attrition 230
Calculating the exit rate 231
Calculating the annualized exit rate 233
Refining exit rate by type classification 233
Calculating exit rate by any exit type 236
Segmenting for Insight 236
Measuring Retention Rate 238
Measuring Commitment 239
Commitment Index scoring 240
Commitment types 241
Calculating intent to stay 241
Understanding Why People Leave 243
Creating a better exit survey 243
Part 4: Improving Your Game Plan with Science and Statistics 249
Chapter 12: Measuring Your Fuzzy Ideas with Surveys 251
Discovering the Wisdom of Crowds through Surveys 252
O, the Things We Can Measure Together 253
Surveying the many types of survey measures 254
Looking at survey instruments 256
Getting Started with Survey Research 257
Designing Surveys 258
Working with models 259
Conceptualizing fuzzy ideas 260
Operationalizing concepts into measurements 260
Designing indexes (scales) 261
Testing validity and reliability 263
Managing the Survey Process 266
Getting confidential: Third-party confidentiality 266
Ensuring a good response rate 267
Planning for effective survey communications 270
Comparing Survey Data 272
Chapter 13: Prioritizing Where to Focus 275
Dealing with the Data Firehose 276
Introducing a Two-Pronged Approach to Survey Design and Analysis 278
Going with KPIs 278
Taking the KDA route 278
Evaluating Survey Data with Key Driver Analysis (KDA) 279
Having a Look at KDA Output 286
Outlining Key Driver Analysis 287
Learning the Ins and Outs of Correlation 288
Visualizing associations 288
Quantifying the strength of a relationship 290
Computing correlation in Excel 291
Interpreting the strength of a correlation 292
Making associations between binary variables 293
Regressing to conclusions with least squares 296
Cautions 299
Improving Your Key Driver Analysis Chops 299
Chapter 14: Modeling HR Data with Multiple Regression Analysis 303
Taking Baby Steps with Linear Regression 304
Mastering Multiple Regression Analysis: The Bird's-Eye View 307
Doing a Multiple Regression in Excel 309
Interpreting the Summary Output of a Multiple Regression 312
Regression statistics 313
Multiple R 313
R-Square 314
Adjusted R-square 314
Standard Error 315
Analysis of variance (ANOVA) 315
Significance F 316
Coefficients Table 317
Moving from Excel to a Statistics Application 320
Doing a Binary Logistic Regression in SPSS 321
Chapter 15: Making Better Predictions 331
Predicting in the Real World 333
Introducing the Key Concepts 334
Independent and dependent variables 335
Deterministic and probabilistic methods 335
Statistics versus data science 337
Putting the Key Concepts to Use 337
Understanding Your Data Just in Time 339
Predicting exits from time series data 340
Dealing with exponential (nonlinear) growth 344
Checking your work with training and validation periods 345
Dealing with short-term trends, seasonality, and noise 347
Dealing with long-term trends 350
Improving Your Predictions with Multiple Regression 354
Looking at the nuts-and-bolts of multiple regression analysis 356
Refining your multiple regression analysis strategy 358
Interpreting the Variables in the Equation
(SPSS Variable Summary Table) 361
Applying Learning from Logistic Regression
Output Summary Back to Individual Data 364
Chapter 16: Learning with Experiments 369
Introducing Experimental Design 370
Analytics for description 371
Analytics for insight 371
Breaking down theories into hypotheses and experiments 372
Paying attention to practical and ethical considerations 374
Designing Experiments 375
Using independent and dependent variables 375
Relying on pre-measurements and post-measurements 376
Working with experimental and control groups 377
Selecting Random Samples for Experiments 378
Introducing probability sampling 379
Randomizing samples 380
Matching or producing samples that meet the needs of a quota 383
Analyzing Data from Experiments 384
Graphing sample data with error bars 385
Using t-tests to determine statistically significant differences between means 389
Performing a t-test in Excel 390
Part 5: The Part of Tens 395
Chapter 17: Ten Myths of People Analytics 397
Myth 1: Slowing Down for People Analytics Will Slow You Down 398
Myth 2: Systems Are the First Step 399
Myth 3: More Data Is Better 400
Myth 4: Data Must Be Perfect 401
Myth 5: People Analytics Responsibility Can be Performed by the IT or HRIT Team 402
Myth 6: Artificial Intelligence Can Do People Analytics Automatically 403
Myth 7: People Analytics Is Just for the Nerds 404
Myth 8: There are Permanent HR Insights and HR Solutions 405
Myth 9: The More Complex the Analysis, the Better the Analyst 405
Myth 10: Financial Measures are the Holy Grail 407
Chapter 18: Ten People Analytics Pitfalls 409
Pitfall 1: Changing People is Hard 409
Pitfall 2: Missing the People Strategy Part of the People Analytics Intersection 411
Measuring everything that is easy to measure 412
Measuring everything everyone else is measuring 412
Pitfall 3: Missing the Statistics Part of the People Analytics intersection 413
Pitfall 4: Missing the Science Part of the People Analytics Intersection 413
Pitfall 5: Missing the System Part of the People Analytics Intersection 414
Pitfall 6: Not Involving Other People in the Right Ways 416
Pitfall 7: Underfunding People Analytics 417
Pitfall 8: Garbage In, Garbage Out 419
Pitfall 9: Skimping on New Data Development 420
Pitfall 10: Not Getting Started at All 422
Index 423
Introduction
You might already be familiar with how the power of data analytics has transformed the fields of marketing, sales, supply chain management, or finance. You may also be familiar with the idea that people are a company's greatest investment. Well, like peanut butter and chocolate eventually found their way into a delicious treat, these two ideas found their way together, too - the happy result is called people analytics.
Welcome to People Analytics For Dummies, a book written for people open to the idea that there need not be any contradiction between what makes companies great places to work and great at producing business results. People analytics is built on the premise that what makes companies great is people, and that what can make more companies great when it comes to people is data analysis. Not any kind of analysis - specifically, the analysis of people at work.
In this book, you'll find an introduction to the data, metrics, and analysis at the basis of this new field called people analytics. Because it's a new field, this may be the first time you're hearing anything at all about it or, like most of the people doing the work today, you're figuring it out as you go along. In any case, even if you're familiar with people analytics already, this book may introduce you to new ways of approaching your work and may also provide you with some tips on how best to explain to others exactly what you do. (It never hurts to be able to express clearly and succinctly to others the importance of the work you do.)
About This Book
This is a book about making important management decisions about people by using data analysis rather than whim or instinct. This is a book about getting great business results while at the same time creating a great place for people to work. This is a book about finding a way to be a great company that relies on continuous feedback and learning rather than a mediocre company that's satisfied with either doing it the way it's always been done or that tries to keep up by slavishly copying the competition. This book is the recipe for getting the highest possible individual, team, and company performance while also making employees happier!
In People Analytics For Dummies, I talk about the ways that analysis can connect human resources decisions to business strategy as well as offering an overview of some of the nuts-and-bolts of how to do the analysis. You'll find out about gathering data about your employees at different stages of their careers, detecting patterns from the data, making predictions, and measuring the consequences of the actions you take. You'll find out how to use data to continuously improve the methods you use to attract, activate, and retain talented people so that you can achieve higher levels of productivity.
When I can, I include real-world examples from companies I have worked with - big and small - so that you can learn from the real world how to collect and analyze data in ways that can help you make better business decisions across a wide variety of human resources management topics: recruiting, performance, rewards, learning and development, leadership, diversity, and attrition. These examples show you the broad variety of opportunities for a smart application of people analytics.
Whether you're an executive, a human resources professional, or an analyst, you'll find something in this book for you.
Foolish Assumptions
To get the most from this book, I assume that you
- Have worked for, are working for, or want to be working for a company large enough that establishing better decisions about how you manage people can add value
- Are willing to let data help you make decisions about how you identify, select, pay, develop, and manage people
- Are willing to try something different than what you have done in the past or than what other companies are doing
- Are comfortable reading about business strategy, systems, science, and statistics
- Have access to some people data or at least want to collect and analyze people data
- Are looking, of course, for an accessible source that keeps it as simple as possible and provides practical advice about how to get started in the real world, as opposed to what you might find in an academic textbook or scientific journal
Icons Used in This Book
Throughout this book, you'll see these little graphical icons to identify useful paragraphs:
The Tip icon marks tips and shortcuts that you can take to make a specific task easier.
The Remember icon marks the information that's especially important to know. To siphon off the most important information in each chapter, just skim these paragraphs.
The Technical Stuff icon marks information of a highly technical nature that you can safely skip over without harm.
The Warning icon tells you to watch out! It marks important information that may save you headaches. Warning: Don't skip over these warnings!
How This Book is Organized
The book is arranged into five self-contained parts, each composed of several self-contained chapters. By self-contained, I mean that I do my best to tell you everything you need to know about a single topic inside each chapter. But I admit that more than a few times I had to put references to other parts of the book when it wasn't reasonably possible to cover in one chapter everything that's important to know.
The possibilities for adventure are truly endless, but start where you are right now. Whether you're an executive, HR professional, or analyst, you'll find something worth reading in People Analytics For Dummies.
Here is an overview:
Part 1: Getting Started with People Analytics
These early chapters serve as a primer on people analytics. In this part, you learn to walk before you run, but what you find here lays the foundation for all that comes later. You'll see my definition of people analytics and find an introduction to its important concepts, applications, and options. You may be especially pleased at the nontechnical nature of the first part. Not much bit-bytes or psychobabble is necessary because, as you see in Part 1, people analytics is about business first, people second, analysis third, and systems last.
Part 2: Elevating Your Perspective
It is unfortunate that most people think of analytics as something that is necessarily abstract, complex, or foreign to what they do. In the beginning of Part 2, you get to see how simply counting people up in different ways and looking at the results can help you gain new perspectives on things you do all the time. The fact is, the methods of people analytics need not be abstract, complex, or foreign -?they can just be empirically valid ways of better doing what you always do.
If you read the entire part, you'll have learned some basic methods to get more perspective on how people produce value for businesses (or don't), have gained insight into why results vary, and have seen how, with careful attention to the right level of detail, you can focus your efforts to get value out of analytics faster. The absence of a business value orientation leads analytics into dead ends and trivial pursuits.
Part 3: Quantifying the Employee Journey
In this part, I define a universal measurement framework for human resources centered around two different but related concepts: the employee journey and something I call the triple-A framework"
- Employee journey: I call the stages employees go through from the day they become aware of the job opportunity to the day they eventually exit the company the employee journey. Taking this holistic, long-term point of view implied by this term helps you see patterns you would not otherwise have seen had you organized your analysis in any other way. Also, seeing the company through the eyes of employees can help you see the world in a totally new and different way. Sounds clichéd, but it's true.
- Triple-A framework: The employee perspective is important, but for obvious reasons it has to be paired with the needs of the business as well. The triple-A framework provides the fundamental measurements and analysis for the three big people-related problems each company needs to solve if they hope to grow as a business: attracting talent, activating talent, and controlling the rate of talent exit (attrition).
The combination of the employee journey and the triple-A framework can unify otherwise disparate and competing efforts by providing a single, unified measurement framework that relates employee and company needs with data.
After an introduction to the employee journey in Chapter 8, you'll find more detail on the methods of measurement and analysis in each of the three A's that follow: attraction (Chapter 9), activation (Chapter 10), and attrition (Chapter 11).
Part 4: Improving Your Game Plan with Science and Statistics
Analytics are all about using data to increase certainty. This is rooted in, at a minimum, math and science, but the analysis of people builds on the knowledge of diverse methods and caveats developed from hundreds of years of research in psychology, sociology, social psychology, and behavioral economics. Most of the current writing on people...
