{"product_id":"people-analytics-for-dummies-isbn-9781119434764","title":"People Analytics For Dummies","description":"\u003cp\u003e\u003cb\u003eMaximize performance with better data\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDeveloping 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.\u003c\/p\u003e \u003cp\u003ePeople 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eStart a people analytics project\u003c\/li\u003e \u003cli\u003eWork with qualitative data\u003c\/li\u003e \u003cli\u003eCollect data via communications \u003c\/li\u003e \u003cli\u003eFind the right tools and approach for analyzing data\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIf your organization is ready to better understand why high performers leave, why one department has more personnel issues than another, and why employees violate, \u003ci\u003ePeople Analytics For Dummies\u003c\/i\u003e makes it easier. \u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction\u003c\/b\u003e \u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbout This Book 1\u003c\/p\u003e \u003cp\u003eFoolish Assumptions 2\u003c\/p\u003e \u003cp\u003eIcons Used in This Book 3\u003c\/p\u003e \u003cp\u003eHow This Book is Organized 3\u003c\/p\u003e \u003cp\u003ePart 1: Getting Started with People Analytics 3\u003c\/p\u003e \u003cp\u003ePart 2: Elevating Your Perspective 4\u003c\/p\u003e \u003cp\u003ePart 3: Quantifying the Employee Journey 4\u003c\/p\u003e \u003cp\u003ePart 4: Improving Your Game Plan with Science and Statistics 5\u003c\/p\u003e \u003cp\u003ePart 5: The Part of Tens 5\u003c\/p\u003e \u003cp\u003eBeyond the Book 5\u003c\/p\u003e \u003cp\u003eWhere to Go from Here 7\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Getting Started With People Analytics\u003c\/b\u003e\u003cb\u003e 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Introducing People Analytics\u003c\/b\u003e\u003cb\u003e 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining People Analytics 12\u003c\/p\u003e \u003cp\u003eSolving business problems by asking questions 14\u003c\/p\u003e \u003cp\u003eUsing people data in business analysis 19\u003c\/p\u003e \u003cp\u003eApplying statistics to people management 20\u003c\/p\u003e \u003cp\u003eCombining people strategy, science, statistics, and systems 21\u003c\/p\u003e \u003cp\u003eBlazing a New Trail for Executive Influence and Business Impact 22\u003c\/p\u003e \u003cp\u003eMoving from old HR to new HR 22\u003c\/p\u003e \u003cp\u003eUsing data for continuous improvement 24\u003c\/p\u003e \u003cp\u003eAccounting for people in business results 24\u003c\/p\u003e \u003cp\u003eCompeting in the New Management Frontier 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Making the Business Case for People Analytics\u003c\/b\u003e\u003cb\u003e 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Executives to Buy into People Analytics 29\u003c\/p\u003e \u003cp\u003eGetting started with the ABCs 29\u003c\/p\u003e \u003cp\u003eCreating clarity is essential 30\u003c\/p\u003e \u003cp\u003eBusiness case dreams are made of problems, needs, goals 30\u003c\/p\u003e \u003cp\u003eTailoring to the decision maker 31\u003c\/p\u003e \u003cp\u003ePeeling the onion 32\u003c\/p\u003e \u003cp\u003eIdentifying people problems 34\u003c\/p\u003e \u003cp\u003eTaking feelings seriously 35\u003c\/p\u003e \u003cp\u003eSaving time and money 36\u003c\/p\u003e \u003cp\u003eLeading the field (analytically) 37\u003c\/p\u003e \u003cp\u003ePeople Analytics as a Decision Support Tool 38\u003c\/p\u003e \u003cp\u003eFormalizing the Business Case 40\u003c\/p\u003e \u003cp\u003ePresenting the Business Case 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Contrasting People Analytics Approaches\u003c\/b\u003e\u003cb\u003e 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFiguring Out What You Are After: Efficiency or Insight 44\u003c\/p\u003e \u003cp\u003eEfficiency 44\u003c\/p\u003e \u003cp\u003eInsight 45\u003c\/p\u003e \u003cp\u003eHaving your cake and eating it too 46\u003c\/p\u003e \u003cp\u003eDeciding on a Method of Planning 47\u003c\/p\u003e \u003cp\u003eWaterfall project management 47\u003c\/p\u003e \u003cp\u003eAgile project management 47\u003c\/p\u003e \u003cp\u003eChoosing a Mode of Operation 50\u003c\/p\u003e \u003cp\u003eCentralized 51\u003c\/p\u003e \u003cp\u003eDistributed 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Elevating Your Perspective\u003c\/b\u003e\u003cb\u003e 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Segmenting for Perspective\u003c\/b\u003e\u003cb\u003e 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSegmenting Based on Basic Employee Facts 58\u003c\/p\u003e \u003cp\u003e“Just the facts, ma’am” 58\u003c\/p\u003e \u003cp\u003eThe brave new world of segmentation is psychographic and social 62\u003c\/p\u003e \u003cp\u003eVisualizing Headcount by Segment 62\u003c\/p\u003e \u003cp\u003eAnalyzing Metrics by Segment 63\u003c\/p\u003e \u003cp\u003eUnderstanding Segmentation Hierarchies 65\u003c\/p\u003e \u003cp\u003eCreating Calculated Segments 68\u003c\/p\u003e \u003cp\u003eCompany tenure 68\u003c\/p\u003e \u003cp\u003eMore calculated segment examples 72\u003c\/p\u003e \u003cp\u003eCross-Tabbing for Insight 74\u003c\/p\u003e \u003cp\u003eSetting up a dataset for cross-tabs 74\u003c\/p\u003e \u003cp\u003eGetting started with cross-tabs 75\u003c\/p\u003e \u003cp\u003eGood Advice for Segmenting 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Finding Useful Insight in Differences\u003c\/b\u003e\u003cb\u003e 79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Strategy 80\u003c\/p\u003e \u003cp\u003eFocusing on product differentiators 83\u003c\/p\u003e \u003cp\u003eIdentifying key jobs 85\u003c\/p\u003e \u003cp\u003eIdentifying the characteristics of key talent 86\u003c\/p\u003e \u003cp\u003eMeasuring If Your Company is Concentrating Its Resources 87\u003c\/p\u003e \u003cp\u003eConcentrating spending on key jobs 88\u003c\/p\u003e \u003cp\u003eConcentrating spending on highest performers 88\u003c\/p\u003e \u003cp\u003eFinding Differences Worth Creating 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Estimating Lifetime Value\u003c\/b\u003e\u003cb\u003e 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing Employee Lifetime Value 96\u003c\/p\u003e \u003cp\u003eUnderstanding Why ELV Is Important 97\u003c\/p\u003e \u003cp\u003eApplying ELV 99\u003c\/p\u003e \u003cp\u003eCalculating Lifetime Value 101\u003c\/p\u003e \u003cp\u003eEstimating human capital ROI 102\u003c\/p\u003e \u003cp\u003eEstimating average annual compensation cost per segment 103\u003c\/p\u003e \u003cp\u003eEstimating average lifetime tenure per segment 103\u003c\/p\u003e \u003cp\u003eCalculating the simple ELV per segment by multiplying 104\u003c\/p\u003e \u003cp\u003eRefining the simple ELV calculation 106\u003c\/p\u003e \u003cp\u003eIdentifying the highest-value-producing employee segments 107\u003c\/p\u003e \u003cp\u003eMaking Better Time-and-Resource Decisions with ELV 108\u003c\/p\u003e \u003cp\u003eDrawing Some Bottom Lines 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7: Activating Value \u003c\/b\u003e\u003cb\u003e111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing Activated Value 113\u003c\/p\u003e \u003cp\u003eThe Origin and Purpose of Activated Value 114\u003c\/p\u003e \u003cp\u003eThe imitation trap 114\u003c\/p\u003e \u003cp\u003eThe need to streamline your efforts 116\u003c\/p\u003e \u003cp\u003eMeasuring Activation 118\u003c\/p\u003e \u003cp\u003eThe calculation nitty-gritty 121\u003c\/p\u003e \u003cp\u003eCombining Lifetime Value and Activation with Net Activated Value (NAV) 126\u003c\/p\u003e \u003cp\u003eUsing Activation for Business Impact 128\u003c\/p\u003e \u003cp\u003eGaining business buy-in on the people analytics research plan 128\u003c\/p\u003e \u003cp\u003eAnalyzing problems and designing solutions 129\u003c\/p\u003e \u003cp\u003eSupporting managers 130\u003c\/p\u003e \u003cp\u003eSupporting organizational change 130\u003c\/p\u003e \u003cp\u003eTaking Stock 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: Quantifying the Employee Journey \u003c\/b\u003e\u003cb\u003e131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8: Mapping the Employee Journey\u003c\/b\u003e\u003cb\u003e 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStanding on the Shoulders of Customer Journey Maps 135\u003c\/p\u003e \u003cp\u003eWhy an Employee Journey Map? 141\u003c\/p\u003e \u003cp\u003eCreating Your Own Employee Journey Map 143\u003c\/p\u003e \u003cp\u003eMapping your map 143\u003c\/p\u003e \u003cp\u003eGetting data 144\u003c\/p\u003e \u003cp\u003eUsing Surveys to Get a Handle on the Employee Journey 145\u003c\/p\u003e \u003cp\u003ePre-Recruiting Market Research Survey 145\u003c\/p\u003e \u003cp\u003ePre-Onsite-Interview survey 148\u003c\/p\u003e \u003cp\u003ePost-Onsite-Interview survey 148\u003c\/p\u003e \u003cp\u003ePost-Hire Reverse Exit Interview survey 149\u003c\/p\u003e \u003cp\u003e14-Day On-Board survey 150\u003c\/p\u003e \u003cp\u003e90-Day On-Board Survey 151\u003c\/p\u003e \u003cp\u003eOnce-Per-Quarter Check-In survey 152\u003c\/p\u003e \u003cp\u003eOnce-Per-Year Check-In survey 153\u003c\/p\u003e \u003cp\u003eKey Talent Exit Survey 155\u003c\/p\u003e \u003cp\u003eMaking the Employee Journey Map More Useful 157\u003c\/p\u003e \u003cp\u003eUsing the Feedback You Get to Increase\u003c\/p\u003e \u003cp\u003eEmployee Lifetime Value 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9: Attraction: Quantifying the Talent Acquisition Phase\u003c\/b\u003e\u003cb\u003e 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing Talent Acquisition 160\u003c\/p\u003e \u003cp\u003eMaking the case for talent acquisition analytics 161\u003c\/p\u003e \u003cp\u003eSeeing what can be measured 162\u003c\/p\u003e \u003cp\u003eGetting Things Moving with Process Metrics 163\u003c\/p\u003e \u003cp\u003eAnswering the volume question 164\u003c\/p\u003e \u003cp\u003eAnswering the efficiency question 172\u003c\/p\u003e \u003cp\u003eAnswering the speed question 177\u003c\/p\u003e \u003cp\u003eAnswering the cost question 182\u003c\/p\u003e \u003cp\u003eAnswering the quality question 184\u003c\/p\u003e \u003cp\u003eUsing critical-incident technique 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10: Activation: Identifying the ABCs of a Productive Worker\u003c\/b\u003e\u003cb\u003e 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAnalyzing Antecedents, Behaviors, and Consequences 194\u003c\/p\u003e \u003cp\u003eLooking at the ABC framework in action 195\u003c\/p\u003e \u003cp\u003eExtrapolating from observed behavior 196\u003c\/p\u003e \u003cp\u003eIntroducing Models 198\u003c\/p\u003e \u003cp\u003eBusiness models 199\u003c\/p\u003e \u003cp\u003eScientific models 200\u003c\/p\u003e \u003cp\u003eMathematical\/statistical models 200\u003c\/p\u003e \u003cp\u003eData models 201\u003c\/p\u003e \u003cp\u003eSystem models 203\u003c\/p\u003e \u003cp\u003eEvaluating the Benefits and Limitations of Models 204\u003c\/p\u003e \u003cp\u003eUsing Models Effectively 206\u003c\/p\u003e \u003cp\u003eGetting Started with General People Models 209\u003c\/p\u003e \u003cp\u003eActivating employee performance 209\u003c\/p\u003e \u003cp\u003eUsing models to clarify fuzzy ideas about people 215\u003c\/p\u003e \u003cp\u003eThe Culture Congruence model 216\u003c\/p\u003e \u003cp\u003eClimate 218\u003c\/p\u003e \u003cp\u003eEngagement 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11: Attrition: Analyzing Employee Commitment and Attrition\u003c\/b\u003e\u003cb\u003e 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Beyond the Common Misconceptions about Attrition 226\u003c\/p\u003e \u003cp\u003eMeasuring Employee Attrition 230\u003c\/p\u003e \u003cp\u003eCalculating the exit rate 231\u003c\/p\u003e \u003cp\u003eCalculating the annualized exit rate 233\u003c\/p\u003e \u003cp\u003eRefining exit rate by type classification 233\u003c\/p\u003e \u003cp\u003eCalculating exit rate by any exit type 236\u003c\/p\u003e \u003cp\u003eSegmenting for Insight 236\u003c\/p\u003e \u003cp\u003eMeasuring Retention Rate 238\u003c\/p\u003e \u003cp\u003eMeasuring Commitment 239\u003c\/p\u003e \u003cp\u003eCommitment Index scoring 240\u003c\/p\u003e \u003cp\u003eCommitment types 241\u003c\/p\u003e \u003cp\u003eCalculating intent to stay 241\u003c\/p\u003e \u003cp\u003eUnderstanding Why People Leave 243\u003c\/p\u003e \u003cp\u003eCreating a better exit survey 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: Improving Your Game Plan with Science and Statistics\u003c\/b\u003e\u003cb\u003e 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12: Measuring Your Fuzzy Ideas with Surveys\u003c\/b\u003e\u003cb\u003e 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDiscovering the Wisdom of Crowds through Surveys 252\u003c\/p\u003e \u003cp\u003eO, the Things We Can Measure Together 253\u003c\/p\u003e \u003cp\u003eSurveying the many types of survey measures 254\u003c\/p\u003e \u003cp\u003eLooking at survey instruments 256\u003c\/p\u003e \u003cp\u003eGetting Started with Survey Research 257\u003c\/p\u003e \u003cp\u003eDesigning Surveys 258\u003c\/p\u003e \u003cp\u003eWorking with models 259\u003c\/p\u003e \u003cp\u003eConceptualizing fuzzy ideas 260\u003c\/p\u003e \u003cp\u003eOperationalizing concepts into measurements 260\u003c\/p\u003e \u003cp\u003eDesigning indexes (scales) 261\u003c\/p\u003e \u003cp\u003eTesting validity and reliability 263\u003c\/p\u003e \u003cp\u003eManaging the Survey Process 266\u003c\/p\u003e \u003cp\u003eGetting confidential: Third-party confidentiality 266\u003c\/p\u003e \u003cp\u003eEnsuring a good response rate 267\u003c\/p\u003e \u003cp\u003ePlanning for effective survey communications 270\u003c\/p\u003e \u003cp\u003eComparing Survey Data 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13: Prioritizing Where to Focus\u003c\/b\u003e\u003cb\u003e 275\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDealing with the Data Firehose 276\u003c\/p\u003e \u003cp\u003eIntroducing a Two-Pronged Approach to Survey Design and Analysis 278\u003c\/p\u003e \u003cp\u003eGoing with KPIs 278\u003c\/p\u003e \u003cp\u003eTaking the KDA route 278\u003c\/p\u003e \u003cp\u003eEvaluating Survey Data with Key Driver Analysis (KDA) 279\u003c\/p\u003e \u003cp\u003eHaving a Look at KDA Output 286\u003c\/p\u003e \u003cp\u003eOutlining Key Driver Analysis 287\u003c\/p\u003e \u003cp\u003eLearning the Ins and Outs of Correlation 288\u003c\/p\u003e \u003cp\u003eVisualizing associations 288\u003c\/p\u003e \u003cp\u003eQuantifying the strength of a relationship 290\u003c\/p\u003e \u003cp\u003eComputing correlation in Excel 291\u003c\/p\u003e \u003cp\u003eInterpreting the strength of a correlation 292\u003c\/p\u003e \u003cp\u003eMaking associations between binary variables 293\u003c\/p\u003e \u003cp\u003eRegressing to conclusions with least squares 296\u003c\/p\u003e \u003cp\u003eCautions 299\u003c\/p\u003e \u003cp\u003eImproving Your Key Driver Analysis Chops 299\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14: Modeling HR Data with Multiple Regression Analysis\u003c\/b\u003e\u003cb\u003e 303\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTaking Baby Steps with Linear Regression 304\u003c\/p\u003e \u003cp\u003eMastering Multiple Regression Analysis: The Bird’s-Eye View 307\u003c\/p\u003e \u003cp\u003eDoing a Multiple Regression in Excel 309\u003c\/p\u003e \u003cp\u003eInterpreting the Summary Output of a Multiple Regression 312\u003c\/p\u003e \u003cp\u003eRegression statistics 313\u003c\/p\u003e \u003cp\u003eMultiple R 313\u003c\/p\u003e \u003cp\u003eR-Square 314\u003c\/p\u003e \u003cp\u003eAdjusted R-square 314\u003c\/p\u003e \u003cp\u003eStandard Error 315\u003c\/p\u003e \u003cp\u003eAnalysis of variance (ANOVA) 315\u003c\/p\u003e \u003cp\u003eSignificance F 316\u003c\/p\u003e \u003cp\u003eCoefficients Table 317\u003c\/p\u003e \u003cp\u003eMoving from Excel to a Statistics Application 320\u003c\/p\u003e \u003cp\u003eDoing a Binary Logistic Regression in SPSS 321\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15: Making Better Predictions\u003c\/b\u003e\u003cb\u003e 331\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePredicting in the Real World 333\u003c\/p\u003e \u003cp\u003eIntroducing the Key Concepts 334\u003c\/p\u003e \u003cp\u003eIndependent and dependent variables 335\u003c\/p\u003e \u003cp\u003eDeterministic and probabilistic methods 335\u003c\/p\u003e \u003cp\u003eStatistics versus data science 337\u003c\/p\u003e \u003cp\u003ePutting the Key Concepts to Use 337\u003c\/p\u003e \u003cp\u003eUnderstanding Your Data Just in Time 339\u003c\/p\u003e \u003cp\u003ePredicting exits from time series data 340\u003c\/p\u003e \u003cp\u003eDealing with exponential (nonlinear) growth 344\u003c\/p\u003e \u003cp\u003eChecking your work with training and validation periods 345\u003c\/p\u003e \u003cp\u003eDealing with short-term trends, seasonality, and noise 347\u003c\/p\u003e \u003cp\u003eDealing with long-term trends 350\u003c\/p\u003e \u003cp\u003eImproving Your Predictions with Multiple Regression 354\u003c\/p\u003e \u003cp\u003eLooking at the nuts-and-bolts of multiple regression analysis 356\u003c\/p\u003e \u003cp\u003eRefining your multiple regression analysis strategy 358\u003c\/p\u003e \u003cp\u003eInterpreting the Variables in the Equation\u003c\/p\u003e \u003cp\u003e(SPSS Variable Summary Table) 361\u003c\/p\u003e \u003cp\u003eApplying Learning from Logistic Regression\u003c\/p\u003e \u003cp\u003eOutput Summary Back to Individual Data 364\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16: Learning with Experiments\u003c\/b\u003e\u003cb\u003e 369\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing Experimental Design 370\u003c\/p\u003e \u003cp\u003eAnalytics for description 371\u003c\/p\u003e \u003cp\u003eAnalytics for insight 371\u003c\/p\u003e \u003cp\u003eBreaking down theories into hypotheses and experiments 372\u003c\/p\u003e \u003cp\u003ePaying attention to practical and ethical considerations 374\u003c\/p\u003e \u003cp\u003eDesigning Experiments 375\u003c\/p\u003e \u003cp\u003eUsing independent and dependent variables 375\u003c\/p\u003e \u003cp\u003eRelying on pre-measurements and post-measurements 376\u003c\/p\u003e \u003cp\u003eWorking with experimental and control groups 377\u003c\/p\u003e \u003cp\u003eSelecting Random Samples for Experiments 378\u003c\/p\u003e \u003cp\u003eIntroducing probability sampling 379\u003c\/p\u003e \u003cp\u003eRandomizing samples 380\u003c\/p\u003e \u003cp\u003eMatching or producing samples that meet the needs of a quota 383\u003c\/p\u003e \u003cp\u003eAnalyzing Data from Experiments 384\u003c\/p\u003e \u003cp\u003eGraphing sample data with error bars 385\u003c\/p\u003e \u003cp\u003eUsing \u003ci\u003et\u003c\/i\u003e-tests to determine statistically significant differences between means 389\u003c\/p\u003e \u003cp\u003ePerforming a \u003ci\u003et\u003c\/i\u003e-test in Excel 390\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5: The Part of Tens\u003c\/b\u003e\u003cb\u003e 395\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17: Ten Myths of People Analytics\u003c\/b\u003e\u003cb\u003e 397\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMyth 1: Slowing Down for People Analytics Will Slow You Down 398\u003c\/p\u003e \u003cp\u003eMyth 2: Systems Are the First Step 399\u003c\/p\u003e \u003cp\u003eMyth 3: More Data Is Better 400\u003c\/p\u003e \u003cp\u003eMyth 4: Data Must Be Perfect 401\u003c\/p\u003e \u003cp\u003eMyth 5: People Analytics Responsibility Can be Performed by the IT or HRIT Team 402\u003c\/p\u003e \u003cp\u003eMyth 6: Artificial Intelligence Can Do People Analytics Automatically 403\u003c\/p\u003e \u003cp\u003eMyth 7: People Analytics Is Just for the Nerds 404\u003c\/p\u003e \u003cp\u003eMyth 8: There are Permanent HR Insights and HR Solutions 405\u003c\/p\u003e \u003cp\u003eMyth 9: The More Complex the Analysis, the Better the Analyst 405\u003c\/p\u003e \u003cp\u003eMyth 10: Financial Measures are the Holy Grail 407\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18: Ten People Analytics Pitfalls\u003c\/b\u003e\u003cb\u003e 409\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePitfall 1: Changing People is Hard 409\u003c\/p\u003e \u003cp\u003ePitfall 2: Missing the People Strategy Part of the People Analytics Intersection 411\u003c\/p\u003e \u003cp\u003eMeasuring everything that is easy to measure 412\u003c\/p\u003e \u003cp\u003eMeasuring everything everyone else is measuring 412\u003c\/p\u003e \u003cp\u003ePitfall 3: Missing the Statistics Part of the People Analytics intersection 413\u003c\/p\u003e \u003cp\u003ePitfall 4: Missing the Science Part of the People Analytics Intersection 413\u003c\/p\u003e \u003cp\u003ePitfall 5: Missing the System Part of the People Analytics Intersection 414\u003c\/p\u003e \u003cp\u003ePitfall 6: Not Involving Other People in the Right Ways 416\u003c\/p\u003e \u003cp\u003ePitfall 7: Underfunding People Analytics 417\u003c\/p\u003e \u003cp\u003ePitfall 8: Garbage In, Garbage Out 419\u003c\/p\u003e \u003cp\u003ePitfall 9: Skimping on New Data Development 420\u003c\/p\u003e \u003cp\u003ePitfall 10: Not Getting Started at All 422\u003c\/p\u003e \u003cp\u003eIndex 423\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eMike West\u003c\/b\u003e 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.  \t  \t \u003c\/p\u003e\u003cul\u003e \u003cli\u003eLearn how top companies use people analytics\u003c\/li\u003e \u003cli\u003eAnalyze data to quantify your investment in people\u003c\/li\u003e \u003cli\u003eUse feedback to create a winning people culture\u003c\/li\u003e \u003c\/ul\u003e\t \t \u003cp\u003e\u003cb\u003eGet more from your #1 investment - your employees \u003c\/b\u003e\t \t\u003c\/p\u003e\u003cp\u003eDeveloping a successful workforce requires more than instinct. Data helps guide decisions on how to hire quality employees and keep them satisfied. This book shows how to build a people analytics strategy and apply your findings toward creating a more engaged workforce. If your organization wants to understand why you miss headcount targets, why high performers leave, or why one department has more production issues than another, this book is for you. \t\u003c\/p\u003e\u003cp\u003e\u003cb\u003eInside...\u003c\/b\u003e \t\u003c\/p\u003e\u003cul\u003e \u003cli\u003eQuantify the employee journey\u003c\/li\u003e \u003cli\u003eMeasure employee lifetime value\u003c\/li\u003e \u003cli\u003eCreate more commitment,  alignment, and motivation\u003c\/li\u003e \u003cli\u003eFind where to focus resources\u003c\/li\u003e \u003cli\u003eMake staffing predictions\u003c\/li\u003e \u003cli\u003eLearn what works for your firm with experiments\u003c\/li\u003e \u003cli\u003eAvoid people analytics pitfalls\u003c\/li\u003e \u003c\/ul\u003e","brand":"For Dummies","offers":[{"title":"Default Title","offer_id":47989759836389,"sku":"NP9781119434764","price":34.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119434764.jpg?v=1761785382","url":"https:\/\/k12savings.com\/es\/products\/people-analytics-for-dummies-isbn-9781119434764","provider":"K12savings","version":"1.0","type":"link"}