Data Analytics for Organisational Development: Unleashing the Potential of Your Data
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Newark
John Wiley & Sons, Incorporated
2021
|
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (364 pages) |
| ISBN: | 9781119758310 |
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| 245 | 1 | 0 | |a Data Analytics for Organisational Development |b Unleashing the Potential of Your Data |
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| 505 | 8 | |a Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- About the Authors -- Introduction: Why Data Analytics is Important -- Why This Book Has Been Written -- How This Book Is Structured -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- What Tools Are Used -- Activating and Using MS Excel's Analysis ToolPak -- Downloading and Using MS Power BI -- Downloading and Using R and R Studio -- What Is Provided -- Which Cases Should I Study? -- References -- List of Figures and Tables -- Chapter 1 Introduction to Data Analytics and Data Science -- Components of Data Analytics -- Big Data and its Relationship to Data Analytics -- Data Analytics - The Foundation for Data Science and Artificial Intelligence -- Practice -- Phases of Data Analytics -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Analytical Story Telling -- Deploying Analytics Tools -- Practice -- Competencies of a Data Scientist -- Competencies Needed in Data Analytics Phases -- Key Roles of Today's Managers and Leaders -- References -- List of Figures and Tables -- Chapter 2 Customer Domain - Customer Analytics -- Why Customer Analytics? -- Listen to the Voice of Your Existing Customers -- Understanding Customer Expectations -- Studying the Complete Customer Experience -- Designing Customer Surveys -- Determine the Purpose of your Survey -- Use Proven Questionnaires -- Use Proven Scales -- Test Your Survey Questionnaire -- Decide on the Distribution of the Questionnaire -- Select an Appropriate Timing for your Survey -- Begin with the End in Mind -- Some More Considerations -- Conclusion -- Practice -- Case 1: Great, We Have Improved . . . or Not? -- The Problem with Sampling -- Understanding Confidence Intervals -- Means Are Lies -- Business Question | |
| 505 | 8 | |a Data Collection -- Data Processing -- Data Analysis -- Business Decision -- Analytical Storytelling -- What If We Had All The Data? -- Deploying Analytics Tools -- Practice -- Case 2: What Drives our Patient Satisfaction? -- Patient Satisfaction in an Outpatient Clinic -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Analytical Storytelling -- Practice -- Deploying Analytics Tools -- Case 3: How to Create a Patient Satisfaction Dashboard -- Deciding about Metrics to Illustrate our Clinic Performance -- Building a Clinic Dashboard with MS Power BI and R -- Using MS Power BI for Analytical Storytelling -- Conclusion -- Practice -- References -- List of Figures andTables -- Chapter 3 Process Domain - Operations Analytics -- Why Operations Analytics? -- Dimensions of Operations Analytics -- Process Design Using Analytics -- Defining Measures for Analytics -- Process Management Using Analytics -- Process Improvement Using Analytics - The Power of DMAIC -- Roles and Deployment of Operations Analytics -- Conclusion -- Practice Questions -- Case 4: Which Supplier has the Better Product Quality? -- Business Question -- Data Acquisition -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- Case 5: Why Does Finance Pay Our Vendors Late? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- Case 6: Why Are We Wasting Blood? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- References -- List of Figures and Tables -- Chapter 4 Workforce Domain - Workforce Analytics -- Why Workforce Analytics? -- Why has the topic "workforce analytics" developed into a priority? -- Dimensions of Workforce Analytics | |
| 505 | 8 | |a Putting Workforce Analytics into Practice -- Using Descriptive and Predictive Workforce Analytics in Workforce Planning -- Workforce Planning for Transactional Processes -- Workforce Planning for Less Transactional Processes -- Workforce Planning from the Workforce Perspective -- Getting the Intent Right -- a) Connect HR Data and Business Outcomes -- b) Determine Information Needed and Collect Data -- c) Analyse the Data -- d) Derive and Formulate a Business Answer - Tell a Story -- Workforce Analysts' Paradise is Employees' Nightmare - Managing the Change -- Summary -- Practice -- Case 7: Do We Have Enough People to Run Our Organisation? - Workforce Planning Inside-Out -- Data Acquisition and Data Wrangling -- Understanding the Demand Pattern -- Predicting a Potential Future Problem -- Understanding the Activity Pattern -- Planning the Workforce -- "Fighting Variation" -- Rethinking and Innovating the Process -- Conclusion -- Practice -- Deploying Analytics Tools -- Case 8: What Makes Our Staff Innovate? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision and Storytelling -- Deploying Analytics Tools -- Background -- Case 9: What Does Our Engagement Survey Result Mean? -- Why We Should not Trust this Data Easily -- Performing a Proper Data Analysis -- Making a Better Decision -- Practice -- Deploying Analytics Tools -- Case 10: What Drives Our Staff Out? - Logistic Regression for Prediction and Decision Making -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Summary -- Practice -- Deploying Analytics Tools -- References -- Table of Equations, Figures, Tables -- Chapter 5 Implementing Data Analytics for Organisational Development -- Making Better Decisions - Knowing the Risk of Being Wrong -- There is No Difference, and We Decide There Is None | |
| 505 | 8 | |a There is No Difference, and We Decide There Is One - Type I Error -- There Is a Difference, and We Decide There Is One -- There Is a Difference, but We Decide There Is None - Type II Error -- Making Better Decisions - Do not Trust Statistics Blindly -- Significant Difference Does Not Mean Important Difference -- A Non-Significant Difference Could Be Important for The Organisation -- Data Analytics Does Not Take Over Decision Making -- Ensuring the Success of Your Data Analytics Journey -- Steps for Implementing Data Analytics -- Ensuring the Management Walks and Talks Analytics -- Creating Excitement for Data Analytics and its Benefits -- Developing a Body of Knowledge - Start Small -- Using Analytics to Breakdown Silos -- Closing the Analytics Loop - Sustaining the Gains -- Calibrating Your Data Analytics Implementation -- Outlook -- References -- List of Figures and Tables -- Materials for Download -- Index -- EULA. | |
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Datensatz im Suchindex
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| author | Kaufmann, Uwe H. |
| author_facet | Kaufmann, Uwe H. |
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| author_sort | Kaufmann, Uwe H. |
| author_variant | u h k uh uhk |
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| bvnumber | BV048223326 |
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| contents | Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- About the Authors -- Introduction: Why Data Analytics is Important -- Why This Book Has Been Written -- How This Book Is Structured -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- What Tools Are Used -- Activating and Using MS Excel's Analysis ToolPak -- Downloading and Using MS Power BI -- Downloading and Using R and R Studio -- What Is Provided -- Which Cases Should I Study? -- References -- List of Figures and Tables -- Chapter 1 Introduction to Data Analytics and Data Science -- Components of Data Analytics -- Big Data and its Relationship to Data Analytics -- Data Analytics - The Foundation for Data Science and Artificial Intelligence -- Practice -- Phases of Data Analytics -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Analytical Story Telling -- Deploying Analytics Tools -- Practice -- Competencies of a Data Scientist -- Competencies Needed in Data Analytics Phases -- Key Roles of Today's Managers and Leaders -- References -- List of Figures and Tables -- Chapter 2 Customer Domain - Customer Analytics -- Why Customer Analytics? -- Listen to the Voice of Your Existing Customers -- Understanding Customer Expectations -- Studying the Complete Customer Experience -- Designing Customer Surveys -- Determine the Purpose of your Survey -- Use Proven Questionnaires -- Use Proven Scales -- Test Your Survey Questionnaire -- Decide on the Distribution of the Questionnaire -- Select an Appropriate Timing for your Survey -- Begin with the End in Mind -- Some More Considerations -- Conclusion -- Practice -- Case 1: Great, We Have Improved . . . or Not? -- The Problem with Sampling -- Understanding Confidence Intervals -- Means Are Lies -- Business Question Data Collection -- Data Processing -- Data Analysis -- Business Decision -- Analytical Storytelling -- What If We Had All The Data? -- Deploying Analytics Tools -- Practice -- Case 2: What Drives our Patient Satisfaction? -- Patient Satisfaction in an Outpatient Clinic -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Analytical Storytelling -- Practice -- Deploying Analytics Tools -- Case 3: How to Create a Patient Satisfaction Dashboard -- Deciding about Metrics to Illustrate our Clinic Performance -- Building a Clinic Dashboard with MS Power BI and R -- Using MS Power BI for Analytical Storytelling -- Conclusion -- Practice -- References -- List of Figures andTables -- Chapter 3 Process Domain - Operations Analytics -- Why Operations Analytics? -- Dimensions of Operations Analytics -- Process Design Using Analytics -- Defining Measures for Analytics -- Process Management Using Analytics -- Process Improvement Using Analytics - The Power of DMAIC -- Roles and Deployment of Operations Analytics -- Conclusion -- Practice Questions -- Case 4: Which Supplier has the Better Product Quality? -- Business Question -- Data Acquisition -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- Case 5: Why Does Finance Pay Our Vendors Late? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- Case 6: Why Are We Wasting Blood? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- References -- List of Figures and Tables -- Chapter 4 Workforce Domain - Workforce Analytics -- Why Workforce Analytics? -- Why has the topic "workforce analytics" developed into a priority? -- Dimensions of Workforce Analytics Putting Workforce Analytics into Practice -- Using Descriptive and Predictive Workforce Analytics in Workforce Planning -- Workforce Planning for Transactional Processes -- Workforce Planning for Less Transactional Processes -- Workforce Planning from the Workforce Perspective -- Getting the Intent Right -- a) Connect HR Data and Business Outcomes -- b) Determine Information Needed and Collect Data -- c) Analyse the Data -- d) Derive and Formulate a Business Answer - Tell a Story -- Workforce Analysts' Paradise is Employees' Nightmare - Managing the Change -- Summary -- Practice -- Case 7: Do We Have Enough People to Run Our Organisation? - Workforce Planning Inside-Out -- Data Acquisition and Data Wrangling -- Understanding the Demand Pattern -- Predicting a Potential Future Problem -- Understanding the Activity Pattern -- Planning the Workforce -- "Fighting Variation" -- Rethinking and Innovating the Process -- Conclusion -- Practice -- Deploying Analytics Tools -- Case 8: What Makes Our Staff Innovate? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision and Storytelling -- Deploying Analytics Tools -- Background -- Case 9: What Does Our Engagement Survey Result Mean? -- Why We Should not Trust this Data Easily -- Performing a Proper Data Analysis -- Making a Better Decision -- Practice -- Deploying Analytics Tools -- Case 10: What Drives Our Staff Out? - Logistic Regression for Prediction and Decision Making -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Summary -- Practice -- Deploying Analytics Tools -- References -- Table of Equations, Figures, Tables -- Chapter 5 Implementing Data Analytics for Organisational Development -- Making Better Decisions - Knowing the Risk of Being Wrong -- There is No Difference, and We Decide There Is None There is No Difference, and We Decide There Is One - Type I Error -- There Is a Difference, and We Decide There Is One -- There Is a Difference, but We Decide There Is None - Type II Error -- Making Better Decisions - Do not Trust Statistics Blindly -- Significant Difference Does Not Mean Important Difference -- A Non-Significant Difference Could Be Important for The Organisation -- Data Analytics Does Not Take Over Decision Making -- Ensuring the Success of Your Data Analytics Journey -- Steps for Implementing Data Analytics -- Ensuring the Management Walks and Talks Analytics -- Creating Excitement for Data Analytics and its Benefits -- Developing a Body of Knowledge - Start Small -- Using Analytics to Breakdown Silos -- Closing the Analytics Loop - Sustaining the Gains -- Calibrating Your Data Analytics Implementation -- Outlook -- References -- List of Figures and Tables -- Materials for Download -- Index -- EULA. |
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| id | DE-604.BV048223326 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:50:38Z |
| indexdate | 2024-07-10T09:32:27Z |
| institution | BVB |
| isbn | 9781119758310 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033604059 |
| oclc_num | 1263026409 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (364 pages) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE_Kauf |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | John Wiley & Sons, Incorporated |
| record_format | marc |
| spelling | Kaufmann, Uwe H. Verfasser aut Data Analytics for Organisational Development Unleashing the Potential of Your Data Newark John Wiley & Sons, Incorporated 2021 ©2021 1 Online-Ressource (364 pages) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- About the Authors -- Introduction: Why Data Analytics is Important -- Why This Book Has Been Written -- How This Book Is Structured -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- What Tools Are Used -- Activating and Using MS Excel's Analysis ToolPak -- Downloading and Using MS Power BI -- Downloading and Using R and R Studio -- What Is Provided -- Which Cases Should I Study? -- References -- List of Figures and Tables -- Chapter 1 Introduction to Data Analytics and Data Science -- Components of Data Analytics -- Big Data and its Relationship to Data Analytics -- Data Analytics - The Foundation for Data Science and Artificial Intelligence -- Practice -- Phases of Data Analytics -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Analytical Story Telling -- Deploying Analytics Tools -- Practice -- Competencies of a Data Scientist -- Competencies Needed in Data Analytics Phases -- Key Roles of Today's Managers and Leaders -- References -- List of Figures and Tables -- Chapter 2 Customer Domain - Customer Analytics -- Why Customer Analytics? -- Listen to the Voice of Your Existing Customers -- Understanding Customer Expectations -- Studying the Complete Customer Experience -- Designing Customer Surveys -- Determine the Purpose of your Survey -- Use Proven Questionnaires -- Use Proven Scales -- Test Your Survey Questionnaire -- Decide on the Distribution of the Questionnaire -- Select an Appropriate Timing for your Survey -- Begin with the End in Mind -- Some More Considerations -- Conclusion -- Practice -- Case 1: Great, We Have Improved . . . or Not? -- The Problem with Sampling -- Understanding Confidence Intervals -- Means Are Lies -- Business Question Data Collection -- Data Processing -- Data Analysis -- Business Decision -- Analytical Storytelling -- What If We Had All The Data? -- Deploying Analytics Tools -- Practice -- Case 2: What Drives our Patient Satisfaction? -- Patient Satisfaction in an Outpatient Clinic -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Analytical Storytelling -- Practice -- Deploying Analytics Tools -- Case 3: How to Create a Patient Satisfaction Dashboard -- Deciding about Metrics to Illustrate our Clinic Performance -- Building a Clinic Dashboard with MS Power BI and R -- Using MS Power BI for Analytical Storytelling -- Conclusion -- Practice -- References -- List of Figures andTables -- Chapter 3 Process Domain - Operations Analytics -- Why Operations Analytics? -- Dimensions of Operations Analytics -- Process Design Using Analytics -- Defining Measures for Analytics -- Process Management Using Analytics -- Process Improvement Using Analytics - The Power of DMAIC -- Roles and Deployment of Operations Analytics -- Conclusion -- Practice Questions -- Case 4: Which Supplier has the Better Product Quality? -- Business Question -- Data Acquisition -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- Case 5: Why Does Finance Pay Our Vendors Late? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- Case 6: Why Are We Wasting Blood? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- References -- List of Figures and Tables -- Chapter 4 Workforce Domain - Workforce Analytics -- Why Workforce Analytics? -- Why has the topic "workforce analytics" developed into a priority? -- Dimensions of Workforce Analytics Putting Workforce Analytics into Practice -- Using Descriptive and Predictive Workforce Analytics in Workforce Planning -- Workforce Planning for Transactional Processes -- Workforce Planning for Less Transactional Processes -- Workforce Planning from the Workforce Perspective -- Getting the Intent Right -- a) Connect HR Data and Business Outcomes -- b) Determine Information Needed and Collect Data -- c) Analyse the Data -- d) Derive and Formulate a Business Answer - Tell a Story -- Workforce Analysts' Paradise is Employees' Nightmare - Managing the Change -- Summary -- Practice -- Case 7: Do We Have Enough People to Run Our Organisation? - Workforce Planning Inside-Out -- Data Acquisition and Data Wrangling -- Understanding the Demand Pattern -- Predicting a Potential Future Problem -- Understanding the Activity Pattern -- Planning the Workforce -- "Fighting Variation" -- Rethinking and Innovating the Process -- Conclusion -- Practice -- Deploying Analytics Tools -- Case 8: What Makes Our Staff Innovate? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision and Storytelling -- Deploying Analytics Tools -- Background -- Case 9: What Does Our Engagement Survey Result Mean? -- Why We Should not Trust this Data Easily -- Performing a Proper Data Analysis -- Making a Better Decision -- Practice -- Deploying Analytics Tools -- Case 10: What Drives Our Staff Out? - Logistic Regression for Prediction and Decision Making -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Summary -- Practice -- Deploying Analytics Tools -- References -- Table of Equations, Figures, Tables -- Chapter 5 Implementing Data Analytics for Organisational Development -- Making Better Decisions - Knowing the Risk of Being Wrong -- There is No Difference, and We Decide There Is None There is No Difference, and We Decide There Is One - Type I Error -- There Is a Difference, and We Decide There Is One -- There Is a Difference, but We Decide There Is None - Type II Error -- Making Better Decisions - Do not Trust Statistics Blindly -- Significant Difference Does Not Mean Important Difference -- A Non-Significant Difference Could Be Important for The Organisation -- Data Analytics Does Not Take Over Decision Making -- Ensuring the Success of Your Data Analytics Journey -- Steps for Implementing Data Analytics -- Ensuring the Management Walks and Talks Analytics -- Creating Excitement for Data Analytics and its Benefits -- Developing a Body of Knowledge - Start Small -- Using Analytics to Breakdown Silos -- Closing the Analytics Loop - Sustaining the Gains -- Calibrating Your Data Analytics Implementation -- Outlook -- References -- List of Figures and Tables -- Materials for Download -- Index -- EULA. Organisationsentwicklung (DE-588)4126887-8 gnd rswk-swf Werkzeug (DE-588)4065596-9 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Organisationsentwicklung (DE-588)4126887-8 s Datenanalyse (DE-588)4123037-1 s Werkzeug (DE-588)4065596-9 s DE-604 Tan, Amy B. C. Sonstige oth Erscheint auch als Druck-Ausgabe Kaufmann, Uwe H. Data Analytics for Organisational Development Newark : John Wiley & Sons, Incorporated,c2021 9781119758334 |
| spellingShingle | Kaufmann, Uwe H. Data Analytics for Organisational Development Unleashing the Potential of Your Data Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- About the Authors -- Introduction: Why Data Analytics is Important -- Why This Book Has Been Written -- How This Book Is Structured -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- What Tools Are Used -- Activating and Using MS Excel's Analysis ToolPak -- Downloading and Using MS Power BI -- Downloading and Using R and R Studio -- What Is Provided -- Which Cases Should I Study? -- References -- List of Figures and Tables -- Chapter 1 Introduction to Data Analytics and Data Science -- Components of Data Analytics -- Big Data and its Relationship to Data Analytics -- Data Analytics - The Foundation for Data Science and Artificial Intelligence -- Practice -- Phases of Data Analytics -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Analytical Story Telling -- Deploying Analytics Tools -- Practice -- Competencies of a Data Scientist -- Competencies Needed in Data Analytics Phases -- Key Roles of Today's Managers and Leaders -- References -- List of Figures and Tables -- Chapter 2 Customer Domain - Customer Analytics -- Why Customer Analytics? -- Listen to the Voice of Your Existing Customers -- Understanding Customer Expectations -- Studying the Complete Customer Experience -- Designing Customer Surveys -- Determine the Purpose of your Survey -- Use Proven Questionnaires -- Use Proven Scales -- Test Your Survey Questionnaire -- Decide on the Distribution of the Questionnaire -- Select an Appropriate Timing for your Survey -- Begin with the End in Mind -- Some More Considerations -- Conclusion -- Practice -- Case 1: Great, We Have Improved . . . or Not? -- The Problem with Sampling -- Understanding Confidence Intervals -- Means Are Lies -- Business Question Data Collection -- Data Processing -- Data Analysis -- Business Decision -- Analytical Storytelling -- What If We Had All The Data? -- Deploying Analytics Tools -- Practice -- Case 2: What Drives our Patient Satisfaction? -- Patient Satisfaction in an Outpatient Clinic -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Analytical Storytelling -- Practice -- Deploying Analytics Tools -- Case 3: How to Create a Patient Satisfaction Dashboard -- Deciding about Metrics to Illustrate our Clinic Performance -- Building a Clinic Dashboard with MS Power BI and R -- Using MS Power BI for Analytical Storytelling -- Conclusion -- Practice -- References -- List of Figures andTables -- Chapter 3 Process Domain - Operations Analytics -- Why Operations Analytics? -- Dimensions of Operations Analytics -- Process Design Using Analytics -- Defining Measures for Analytics -- Process Management Using Analytics -- Process Improvement Using Analytics - The Power of DMAIC -- Roles and Deployment of Operations Analytics -- Conclusion -- Practice Questions -- Case 4: Which Supplier has the Better Product Quality? -- Business Question -- Data Acquisition -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- Case 5: Why Does Finance Pay Our Vendors Late? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- Case 6: Why Are We Wasting Blood? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- References -- List of Figures and Tables -- Chapter 4 Workforce Domain - Workforce Analytics -- Why Workforce Analytics? -- Why has the topic "workforce analytics" developed into a priority? -- Dimensions of Workforce Analytics Putting Workforce Analytics into Practice -- Using Descriptive and Predictive Workforce Analytics in Workforce Planning -- Workforce Planning for Transactional Processes -- Workforce Planning for Less Transactional Processes -- Workforce Planning from the Workforce Perspective -- Getting the Intent Right -- a) Connect HR Data and Business Outcomes -- b) Determine Information Needed and Collect Data -- c) Analyse the Data -- d) Derive and Formulate a Business Answer - Tell a Story -- Workforce Analysts' Paradise is Employees' Nightmare - Managing the Change -- Summary -- Practice -- Case 7: Do We Have Enough People to Run Our Organisation? - Workforce Planning Inside-Out -- Data Acquisition and Data Wrangling -- Understanding the Demand Pattern -- Predicting a Potential Future Problem -- Understanding the Activity Pattern -- Planning the Workforce -- "Fighting Variation" -- Rethinking and Innovating the Process -- Conclusion -- Practice -- Deploying Analytics Tools -- Case 8: What Makes Our Staff Innovate? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision and Storytelling -- Deploying Analytics Tools -- Background -- Case 9: What Does Our Engagement Survey Result Mean? -- Why We Should not Trust this Data Easily -- Performing a Proper Data Analysis -- Making a Better Decision -- Practice -- Deploying Analytics Tools -- Case 10: What Drives Our Staff Out? - Logistic Regression for Prediction and Decision Making -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Summary -- Practice -- Deploying Analytics Tools -- References -- Table of Equations, Figures, Tables -- Chapter 5 Implementing Data Analytics for Organisational Development -- Making Better Decisions - Knowing the Risk of Being Wrong -- There is No Difference, and We Decide There Is None There is No Difference, and We Decide There Is One - Type I Error -- There Is a Difference, and We Decide There Is One -- There Is a Difference, but We Decide There Is None - Type II Error -- Making Better Decisions - Do not Trust Statistics Blindly -- Significant Difference Does Not Mean Important Difference -- A Non-Significant Difference Could Be Important for The Organisation -- Data Analytics Does Not Take Over Decision Making -- Ensuring the Success of Your Data Analytics Journey -- Steps for Implementing Data Analytics -- Ensuring the Management Walks and Talks Analytics -- Creating Excitement for Data Analytics and its Benefits -- Developing a Body of Knowledge - Start Small -- Using Analytics to Breakdown Silos -- Closing the Analytics Loop - Sustaining the Gains -- Calibrating Your Data Analytics Implementation -- Outlook -- References -- List of Figures and Tables -- Materials for Download -- Index -- EULA. Organisationsentwicklung (DE-588)4126887-8 gnd Werkzeug (DE-588)4065596-9 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4126887-8 (DE-588)4065596-9 (DE-588)4123037-1 |
| title | Data Analytics for Organisational Development Unleashing the Potential of Your Data |
| title_auth | Data Analytics for Organisational Development Unleashing the Potential of Your Data |
| title_exact_search | Data Analytics for Organisational Development Unleashing the Potential of Your Data |
| title_exact_search_txtP | Data Analytics for Organisational Development Unleashing the Potential of Your Data |
| title_full | Data Analytics for Organisational Development Unleashing the Potential of Your Data |
| title_fullStr | Data Analytics for Organisational Development Unleashing the Potential of Your Data |
| title_full_unstemmed | Data Analytics for Organisational Development Unleashing the Potential of Your Data |
| title_short | Data Analytics for Organisational Development |
| title_sort | data analytics for organisational development unleashing the potential of your data |
| title_sub | Unleashing the Potential of Your Data |
| topic | Organisationsentwicklung (DE-588)4126887-8 gnd Werkzeug (DE-588)4065596-9 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Organisationsentwicklung Werkzeug Datenanalyse |
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