- ホーム
- > 洋書
- > 英文書
- > Business / Economics
Full 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.
Contents
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
