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Fundamentals of HR Analytics : a Manual on Becoming HR Analytical.
Providing practical, hands-on approaches to connect data to HR policies and practices to help influence overall business performance, this book is an essential resource for aspiring, new and experienced HR professionals across a wide range of industrial contexts.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Bingley :
Emerald Publishing Limited,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; FUNDAMENTALS OF HR ANALYTICS; PRAISE FOR FUNDAMENTALS OF HR ANALYTICS; FUNDAMENTALS OF HR ANALYTICS: A Manual on Becoming HR Analytical; Copyright; TABLE OF CONTENTS; LIST OF FIGURES; FOREWORD; ACKNOWLEDGMENTS; INTRODUCTION; Part I: The Basics of HR Analytics; 1. Basics of Finance, Statistics and Data-analytic Thinking; 1.1 Learning Objectives of This Chapter; 1.2 The Changing Nature of HR; 1.3 Why HR Analytics Now?; 1.4 Types of Analysis; 1.5 HR Analysts as Architects; 1.6 An Eight-step Approach to HR Analytics; 1.6.1 Define the Business Problem; 1.6.2 Formulate Hypotheses
- 1.6.3 Collect Data1.6.4 Analyse Data; 1.6.5 Derive Insights; 1.6.6 Build Recommendations; 1.6.7 Visualise and Tell a Story; 1.6.8 Execute and Evaluate; 1.7 Why Do Some Analytics Projects Fail?; 1.8 Finance for HR Professionals; 1.8.1 Profit Measures; 1.8.2 Market and Other Common Performance Measures; 1.8.3 Cost-related Terms; 1.9 Recap on Statistics Concepts; 1.9.1 Categorical and Continuous Variables; 1.9.2 Measures of Central Location; 1.9.3 Measures of Dispersion; 1.9.4 Measures of Association; 1.9.5 Population and Sampling; 1.9.6 Statistical Forecasting Models; Summary; Questions
- Workforce PlanningTalent Acquisition; Talent Engagement; Talent Development; Talent Deployment; Leading and Managing Talent; Talent Retention; 2. Tools for HR Analytics; 2.1 Technology Options; 2.1.1 On-premise; 2.1.2 Cloud Based; 2.2 Cost of Implementing On-premise vs Cloud; 2.3 Software as a Service; 2.4 Components of Analytics Technology; 2.4.1 Human Resources Information System; 2.4.2 HR Data Warehouse; 2.4.3 Reporting Technology; 2.4.4 Statistical Analysis and Machine Learning Technology; 2.4.5 Visualisation Technology; 2.4.6 Cognitive Technology; Summary; Questions; 3. Data Collection
- 3.1 Sources of Data3.1.1 HR Data; 3.1.2 HRIS Data; 3.1.3 Non-HR Data; 3.1.4 Data Audits; 3.1.5 Structured and Unstructured Data; 3.2 Common Data Challenges and Solutions; 3.2.1 Missing Data; 3.2.2 Outdated Data; 3.2.3 No Data Available; 3.2.4 Data Outliers; 3.3 Tidying the Data; 3.3.1 General Principles of Tidy Data; 3.3.2 Common Mistakes When Tidying Data; 3.3.3 Tips on Checking Data; 3.3.4 Common Mistakes When Checking the Data; 3.3.5 Plots Are Better than Summaries; Summary; Questions; Caselet; 4. HR Analytics Modelling; 4.1 Details of Analytics Design Framework
- 4.1.1 Source of Problem or Opportunity4.1.2 Scoping the Project; 4.1.3 Linking HR Variables to Business Measures; 4.2 Data Analysis Question Types; 4.3 Building Models; 4.3.1 Testing Hypotheses; 4.3.2 Blueprint for Analytics; 4.4 Supervised and Unsupervised Methods; 4.4.1 Defining Supervised vs Unsupervised Methods; 4.4.2 Common Types of Algorithms; 4.4.3 Evaluating Model Performance; Summary; Questions; Examples: Insights through Analytics; Interpreting a Workforce Map; Part II: Applications; 5. Turnover; 5.1 Simple HR Analytics Are Useful Too; 5.2 Case of a Semiconductor Company in India