LEADER 02443nam 2200673 450 001 9910453433003321 005 20200520144314.0 010 $a3-11-135669-8 024 7 $a10.1515/9783111356693 035 $a(CKB)2550000001199013 035 $a(EBL)3045251 035 $a(OCoLC)894508234 035 $a(SSID)ssj0001128292 035 $a(PQKBManifestationID)12483338 035 $a(PQKBTitleCode)TC0001128292 035 $a(PQKBWorkID)11065717 035 $a(PQKB)11279400 035 $a(SSID)ssj0000654018 035 $a(PQKBManifestationID)11415029 035 $a(PQKBTitleCode)TC0000654018 035 $a(PQKBWorkID)10655967 035 $a(PQKB)22141088 035 $a(MiAaPQ)EBC3045251 035 $a(DE-B1597)120669 035 $a(OCoLC)1013941676 035 $a(OCoLC)900846219 035 $a(DE-B1597)9783111356693 035 $a(Au-PeEL)EBL3045251 035 $a(CaPaEBR)ebr10829482 035 $a(EXLCZ)992550000001199013 100 $a20740305d1966 uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA linguistic analysis of Akkadian /$fby Erica Reiner, University of Chicago 205 $aReprint 2013 210 1$aThe Hague :$cMouton & Company,$d1966. 215 $a1 online resource (155 p.) 225 0 $aJanua Linguarum. Series Practica ;$v21 225 0$aJanua linguarum.$pSeries practica ;$v21 300 $aDescription based upon print version of record. 311 $a3-11-100028-1 320 $aIncludes bibliographical references. 327 $tFrontmatter -- $tACKNOWLEDGMENTS -- $tTABLE OF CONTENTS -- $tABBREVIATIONS -- $tINTRODUCTION -- $t1. THE CORPUS -- $t2. THE WRITING SYSTEM -- $t3. PHONOLOGY -- $t4. PHONOTACTICS -- $t5. MORPHOLOGY -- $t6. MORPHOPHONEMIC ALTERNATIONS -- $t7. PHONOTACTIC ALTERNATIONS -- $tAPPENDIX -- $tGLOSSARY OF AKKADIAN FORMS -- $tGLOSSARY OF SELECTED LINGUISTIC TERMS -- $tBIBLIOGRAPHY -- $tBackmatter 410 0$aJanua linguarum.$pSeries practica ;$v21. 606 $aAkkadian language 608 $aElectronic books. 615 0$aAkkadian language. 676 $a492.1 700 $aReiner$b Erica$f1924-2005.$01045601 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910453433003321 996 $aA linguistic analysis of Akkadian$92472030 997 $aUNINA LEADER 08153nam 2200529 450 001 9910554855403321 005 20220727132804.0 010 $a1-119-75837-8 010 $a9781119758334 010 $a1-119-75832-7 010 $a1-119-75831-9 035 $a(CKB)4100000011991221 035 $a(MiAaPQ)EBC6686361 035 $a(Au-PeEL)EBL6686361 035 $a(OCoLC)1263026409 035 $a(EXLCZ)994100000011991221 100 $a20220413d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData analytics for organisational development $eunleashing the potential of your data /$fUwe H. Kaufmann, Amy BC Tan 210 1$aWest Sussex, England :$cWiley,$d[2021] 210 4$d©2021 215 $a1 online resource (364 pages) 300 $aIncludes index. 311 $a1-119-75833-5 327 $aCover -- 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. 327 $aData 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. 327 $aPutting 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. 327 $aThere 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. 606 $aBusiness enterprises$xData processing 606 $aOrganizational change 608 $aElectronic books. 615 0$aBusiness enterprises$xData processing. 615 0$aOrganizational change. 676 $a658.0557 700 $aKaufmann$b Uwe H.$01218078 702 $aTan$b Amy B. C. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910554855403321 996 $aData Analytics for Organisational Development$92816878 997 $aUNINA