Adaptive conjoint analysis

, 193–194

Aggressive (optimistic) strategy

, 212

Alternative hypothesis (H1)

, 87

Analytics technology

cognitive technology

, 44

HR data warehouse

, 41–42

Human Resources Information System (HRIS)

, 41

reporting technology

, 42–43

statistical analysis and machine learning technology

, 43

visualisation technology

, 43–44

Benefits

, 188–190

Building models

blueprint

, 88–91

testing hypotheses

, 87–88

Business problem

, 12–14

Business strategy

, 144

Career management

, 85

Career movements

, 220

Career planning

, 242

decision trees. See Decision trees

mobility possibilities

, 208–210

skills matching

, 220

Central limit theorem

, 174–175

Clustering

, 91

Coefficient of determination

, 92

Cognitive ability tests

, 184

Cognitive technology

, 44

Compensation

, 241

diversity

, 200

merit pay differentials

, 199

pay effectiveness

, 198

pay mix

, 199–200

target setting

, 199

team vs individual incentives

, 198–199

thinking analytically

, 197–200

Confidence intervals

, 175–176

Conjoint analysis

, 190

adaptive conjoint analysis

, 193–194

maximum difference conjoint analysis

, 193

methods

, 190

Multivariate Analysis of Variance (MANOVA)

, 197

rewards programs

, 201–205

self-explicated conjoint analysis

, 192

two-item tradeoff analysis

, 192

Conservative (pessimistic) strategy

, 213

Costs cutting

, 238–239

Customer satisfaction data

, 78

Customer service

, 78

Cyclical effects

, 154

Data analysis

, 107–112

Data audits

, 49–50

Data challenges

data outliers

, 55–56

missing data

, 52–53

no data available

, 54–55

outdated data

, 53–54

Data collection

, 15–16

challenges and solutions

, 52–56

sources

, 47–51

tidying

, 56–63

Data gathering

, 107–112

Data outliers

, 55–56

Decision trees

decision strategies

definition

, 210

example

, 210–212

types

, 210

career paths

, 214–218

outcome probabilities with

, 213–214

outcome probabilities without

, 212–213

Delphi method

, 153

Demand gaps

, 149–152

Design framework

building models

, 86–91

data analysis questions

, 85–86

scope

, 73–76

source of problem

, 69–72

supervised and unsupervised methods

, 91–93

value chain

, 84–85

variables to business measures

, 76–84

Diminishing returns

, 127

Educational institution

, 110–111

Eight-step approach

business problem

, 12–14

data collection

, 15–16

derive insights

, 17

evaluation

, 19–20

execution

, 19–20

formulate hypotheses

, 14–15

recommendations

, 17

storytelling

, 17–19

Employee Assistance Program (EAP)

, 79

Employee loyalty analysis

, 170–171

Employee profiling

, 169–170

Employee sampling methods

central limit theorem

, 174–175

confidence intervals

, 175–176

sampling distributions

, 174–175

sampling plans

, 172–174

Employee value proposition (EVP)

full-time employees

, 231

primary levers

, 231

second key hypothesis

, 228

Explanatory models

, 157–158

External drivers

competition

, 106

overseas opportunities

, 106

pay levels

, 107–108

Finance

cost-related terms

, 23

market and performance measures

, 22–23

profit

, 22

Financial projections

, 144

Forecasting techniques

, 152

explanatory/causal models

, 157–158

indicators and indexes

, 153

qualitative and judgemental techniques

, 152–153

regression-based forecasting

, 155–156

statistical time series model

, 153–155

Functional training

, 131

Gamification

, 171–172

Gender

, 111–112

Google

, 72

Grade point average (GPA)

, 167

Hard data

, 77

Hiring formula

, 178–180

HR Business Partner (HRBP)

, 125

HR data

, 47–48

HR data warehouse

, 41–42

HR dimensions

, 115

HRIS data

, 48–49

HR policies

, 223–235, 242

Human Capital Analytics (HCA) program

, 96

Human Resources (HR)

analytics

, 4–5

architects

, 9–12

changing nature

, 3–4

changing requests

, 5

descriptive analysis

, 6

diagnostics

, 6

eight-step approach

, 13–20

finance

, 21–22

function

, 4

maturity

, 7

people analytics (PA)

, 5

predictive analysis

, 6

statistics concepts

, 23–27

talent acquisition

, 29

talent deployment

, 29–30

talent development

, 29

talent engagement

, 29

talent retention

, 30

types

, 6–9

workforce planning

, 28

Human Resources Information System (HRIS)

, 41

Individual drivers

educational institution

, 110–111

gender

, 111–112

tenure

, 111

Infrastructure as a Service (IaaS)

, 40

Job classification

, 115

Kirkpatrick model

, 137–140

Manager dissatisfaction

, 108–110

Maximum difference conjoint analysis

, 193

Multiple regressions

HR metrics

, 225

talent metrics

, 225

Multivariate Analysis of Variance (MANOVA)

, 197

Opportunity

loss strategy

, 213

training

, 130

Optimisation

metric interaction

, 124–125, 127–128

mixture

, 124, 126–127

saturation

, 124, 127

segmentation

, 124–126

time line

, 125, 128–129

Organizational drivers

dissatisfaction with managers

, 108–110

performance ratings

, 108

promotion opportunities

, 110

Outdated data

, 53–54

Pay levels

, 107–108

Performance ratings

, 108

Personality assessments

, 184

Platform as a Service (PaaS)

, 40

Productivity

, 239–240

Profits

, 223–235, 239–240

Promotion opportunities

, 110

Random behaviour

, 154

Recruitment

, 84, 150, 241

employee loyalty analysis

, 170–171

employee profiling

, 169–170

employee sampling methods. See Employee sampling methods

gamification

, 171–172

grade point average (GPA)

, 167

hiring formula

, 178–180

HR analytics

, 168–169

segmentation

, 169–170

three levels analysing talent

, 177–178

using tests

, 181–183

workplace assessments

, 183–184

Regression-based forecasting

, 155–156

Remuneration

, 85

Reporting technology

, 42–43

Retailco case studies

, 232–235

Return on investment

definition

, 122–123

formula

, 123

training

, 129–140

Revenue

, 239–240

Rewards programs

, 201–205

Seasonal effect

, 155

Skills overlaps

, 221

Soft data

, 78

Software as a service (SaaS)

, 40

Sources of data

data audits

, 49–50

HR data

, 47–48

HRIS data

, 48–49

non-HR data

, 49

structured data

, 50–51

unstructured data

, 50–51

Statistical analysis and machine learning technology

, 43

Storytelling

, 17–19

Strategic resourcing

business strategy

, 144

demand gaps

, 149–152

forecasting techniques

, 152

supply gaps

, 149–152

workforce demand

, 145–149

workforce supply

, 145–149

Supervised and unsupervised methods

defining

, 91

model performance

, 92–93

types of algorithms

, 91

Talent acquisition

, 29

Talent deployment

, 29–30

Talent development

, 29

Talent engagement

, 29

Talent retention

, 30

Technology options

cloud based

, 38–39

definition

, 37–38

on-premise solutions

, 38

Tenure

, 111

Tidy data

checking data tips

, 59–60

definition

, 56–57

general principles

, 57–58

mistakes

, 58, 60–61

plots

, 61–63

Training

, 240

optimisation. See Optimisation

purposes

, 121–122

return on investment

, 122–123

ROI

, 129–140

Trends

, 154

Turnover

, 240

semiconductor companies

, 102–103

data analysis

, 107–112

data gathering

, 107–112

design

, 103–104

hypotheses/drivers

, 104–107

insights

, 112

Uncertainty

, 92

Workforce demand

, 145–149

Workforce planning

, 28, 240–241

Workforce supply

, 145–149

Workplace assessments

, 183–184