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1. |
Record Nr. |
UNINA9910554855403321 |
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Autore |
Kaufmann Uwe H. |
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Titolo |
Data analytics for organisational development : unleashing the potential of your data / / Uwe H. Kaufmann, Amy BC Tan |
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Pubbl/distr/stampa |
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West Sussex, England : , : Wiley, , [2021] |
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©2021 |
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ISBN |
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1-119-75837-8 |
9781119758334 |
1-119-75832-7 |
1-119-75831-9 |
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Descrizione fisica |
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1 online resource (364 pages) |
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Disciplina |
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Soggetti |
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Business enterprises - Data processing |
Organizational change |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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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 |
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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: |
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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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2. |
Record Nr. |
UNINA9910792261503321 |
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Autore |
Goeschel Christian |
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Titolo |
Suicide in Nazi Germany [[electronic resource] /] / Christian Goeschel |
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Pubbl/distr/stampa |
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Oxford ; ; New York, : Oxford University Press, 2009 |
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ISBN |
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0-19-160891-2 |
0-19-960611-0 |
9786612053238 |
1-282-05323-X |
0-19-156756-6 |
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Descrizione fisica |
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1 online resource (262 p.) |
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Disciplina |
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362.28/2094309043 |
943.085 |
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Soggetti |
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Suicide - Germany - History |
Germany Politics and government 1933-1945 |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. [223]-242) and index. |
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Nota di contenuto |
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Contents; Acknowledgements; List of Tables; List of Figures; List of Abbreviations; Note to the reader; Introduction; 1. The Weimar Background; 2. Suicide under the Swastika, 1933-1939; 3. Suicides of German Jews, 1933-1945; 4. Wartime suicides, 1939-1944; 5. Downfall; Conclusion; Notes; Statistical Appendix; Bibliography; Index; |
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Sommario/riassunto |
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The Third Reich met its end in the spring of 1945 in an unparalleled wave of suicides. Goeschel analyses the Third Reich's self-destructiveness and the suicides of ordinary people and Nazis in Germany from 1918 until 1945, including the mass suicides of German Jews during the Holocaust. - ;The Third Reich met its end in the spring of 1945 in an unparalleled wave of suicides. Hitler, Goebbels, Bormann, Himmler and later Goering all killed themselves. These deaths represent only the tip of an iceberg of a massive wave of suicides that also touched upon ordinary lives. As this suicide epidemic ha |
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