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
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Bingley, UK
Emerald Publishing
2020
|
| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | DE-634 DE-1043 DE-M347 DE-92 DE-523 DE-91 DE-473 DE-19 DE-355 DE-703 DE-20 DE-706 DE-824 DE-29 DE-739 DE-1050 DE-1046 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (xxv, 252 Seiten) |
| ISBN: | 9781789739633 9781789739619 |
| DOI: | 10.1108/9781789739619 |
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| 245 | 1 | 0 | |a Fundamentals of HR analytics |b a manual on becoming HR analytical |c Fermin Diez, Mark Bussin, Vanessa Lee |
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| 505 | 8 | |a Content: 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
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Datensatz im Suchindex
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|---|---|
| adam_text | |
| any_adam_object | |
| author | Diez, Fermin |
| author_GND | (DE-588)1045185701 |
| author_facet | Diez, Fermin |
| author_role | aut |
| author_sort | Diez, Fermin |
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| building | Verbundindex |
| bvnumber | BV046295411 |
| classification_rvk | QV 578 |
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| contents | Content: 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 |
| ctrlnum | (OCoLC)1135395519 (DE-599)BVBBV046295411 |
| discipline | Wirtschaftswissenschaften |
| doi_str_mv | 10.1108/9781789739619 |
| edition | First edition |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2025-01-15T19:00:44Z |
| institution | BVB |
| isbn | 9781789739633 9781789739619 |
| language | English |
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| physical | 1 Online-Ressource (xxv, 252 Seiten) |
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| spelling | Diez, Fermin Verfasser (DE-588)1045185701 aut Fundamentals of HR analytics a manual on becoming HR analytical Fermin Diez, Mark Bussin, Vanessa Lee First edition Bingley, UK Emerald Publishing 2020 1 Online-Ressource (xxv, 252 Seiten) txt rdacontent c rdamedia cr rdacarrier Content: 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 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 Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Personalinformationssystem (DE-588)4045259-1 gnd rswk-swf Humanvermögen (DE-588)4240300-5 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Humanvermögen (DE-588)4240300-5 s Statistik (DE-588)4056995-0 s Datenverarbeitung (DE-588)4011152-0 s Personalinformationssystem (DE-588)4045259-1 s 1\p DE-604 Bussin, Mark Sonstige oth Lee, Venessa Sonstige oth Erscheint auch als Druck-Ausgabe 978-1-78973-964-0 https://doi.org/10.1108/9781789739619 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Diez, Fermin Fundamentals of HR analytics a manual on becoming HR analytical Content: 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 Datenverarbeitung (DE-588)4011152-0 gnd Personalinformationssystem (DE-588)4045259-1 gnd Humanvermögen (DE-588)4240300-5 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4011152-0 (DE-588)4045259-1 (DE-588)4240300-5 (DE-588)4056995-0 |
| title | Fundamentals of HR analytics a manual on becoming HR analytical |
| title_auth | Fundamentals of HR analytics a manual on becoming HR analytical |
| title_exact_search | Fundamentals of HR analytics a manual on becoming HR analytical |
| title_full | Fundamentals of HR analytics a manual on becoming HR analytical Fermin Diez, Mark Bussin, Vanessa Lee |
| title_fullStr | Fundamentals of HR analytics a manual on becoming HR analytical Fermin Diez, Mark Bussin, Vanessa Lee |
| title_full_unstemmed | Fundamentals of HR analytics a manual on becoming HR analytical Fermin Diez, Mark Bussin, Vanessa Lee |
| title_short | Fundamentals of HR analytics |
| title_sort | fundamentals of hr analytics a manual on becoming hr analytical |
| title_sub | a manual on becoming HR analytical |
| topic | Datenverarbeitung (DE-588)4011152-0 gnd Personalinformationssystem (DE-588)4045259-1 gnd Humanvermögen (DE-588)4240300-5 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Datenverarbeitung Personalinformationssystem Humanvermögen Statistik |
| url | https://doi.org/10.1108/9781789739619 |
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