Predictive intelligence for data-driven managers : process model, assessment-tool, IT-blueprint, competence model and case studies / / Uwe Seebacher

• Autore: Seebacher Uwe G.
• Pubblicazione: Cham, Switzerland : , : Springer, , [2021]; ©2021
• Descrizione fisica: 1 online resource (275 pages)
• Disciplina: 133.5
• Soggetto topico: Predictive analytics; Business - Data processing; Strategic planning
• Nota di contenuto: Intro -- Preface -- Reference -- Contents -- 1: Predictive Intelligence and the Basic Economic Principles -- 1.1 Where Do We Come from? -- 1.2 How Industrial Management Came About -- 1.3 The Separation of Ownership and Management -- 1.4 What Are the Current Challenges? -- 1.5 The Basic Economic Principles Are also Disrupted -- 1.6 What Role Does the Corona Pandemic Play? -- 1.7 What Do We Know? -- Further Reading -- 2: Predictive Intelligence at a Glance -- 2.1 What Is Predictive Intelligence? -- 2.2 The Maturity Model for Predictive Intelligence -- 2.3 The Predictive Intelligence Self-Assessment (PI-SA) -- 2.4 The Advantages of Predictive Intelligence -- 2.5 The Conclusion -- Further Reading -- 3: The Predictive Intelligence Ecosystem -- 3.1 Introduction -- 3.1.1 A/B Tests -- 3.1.2 Artificial Intelligence (AI) -- 3.1.3 Artificial Neural Network (ANN) -- 3.1.4 Association Analysis -- 3.1.5 Evaluation Metrics -- 3.1.6 Big Data -- 3.1.7 Business Analytics (BA) -- 3.1.8 Business Intelligence (BI) -- 3.1.9 Cloud Analytics -- 3.1.10 Cloud-Based Social Media Analytics (CSMA) -- 3.1.11 Cloud Sourcing -- 3.1.12 Clustering -- 3.1.13 Data Analysis -- 3.1.14 Data Cleansing -- 3.1.15 Date Lake -- 3.1.16 Data Mining -- 3.1.17 Data Science -- 3.1.18 Data Scientist -- 3.1.19 Deep Learning -- 3.1.19.1 Excursus -- 3.1.20 Descriptive Analytics -- 3.1.21 Descriptive Analysis -- 3.1.22 Descriptive Models -- 3.1.23 Exception Reporting -- 3.1.24 Extrapolation -- 3.1.25 Functional Models or Modeling -- 3.1.26 Hadoop Cluster -- 3.1.27 Harvesting -- 3.1.28 Principal Component Analysis (PCA) -- 3.1.29 In-Sample -- 3.1.30 k-Means-Clustering -- 3.1.31 k-Nearest Neighbors -- 3.1.32 Classification -- 3.1.33 Louvain Method -- 3.1.34 Machine Learning -- 3.1.35 Feature Extraction -- 3.1.36 Modeling -- 3.1.37 Model Monitoring -- 3.1.38 Sample Units -- 3.1.39 Neural Networks. ; 3.1.40 Out-of-Sample -- 3.1.41 Parameters -- 3.1.42 Predictive Analysis -- 3.1.43 Predictive Models or Modeling -- 3.1.44 Predictors -- 3.1.45 Prescriptive Analytics -- 3.1.46 Predictive Marketing -- 3.1.47 Procurement Intelligence -- 3.1.48 Random Forest -- 3.1.49 Regression Analysis -- 3.1.50 Regularization -- 3.1.51 Reinforcement Learning -- 3.1.52 Supervised Learning -- 3.1.53 Training Patterns -- 3.1.54 Unsupervised Learning -- 3.1.55 Validation -- 3.1.56 Variables -- 3.2 The Dynamics of the PI Ecosystem -- Further Reading -- 4: The Predictive Intelligence Maturity Model -- 4.1 Why Do We Need a PI Maturity Model? -- 4.2 How the PI Maturity Model Was Created? -- 4.2.1 The Game Begins -- 4.2.2 The Data Cube Is Created -- 4.3 The Three Dimensions of the PI Maturity Model -- 4.3.1 Validity of the Data -- 4.3.1.1 Therefore, Examine Whoever Binds Forever! -- 4.3.1.2 The Profit Lies in Purchasing! -- 4.3.2 Time Required for Analyses -- 4.3.3 Cost of the Data -- 4.4 The Predictive Intelligence Maturity Model (PIMM) -- 4.4.1 Stage 1: Reactive-Static Business Analytics -- 4.4.1.1 What Does Reactive Business Analytics Mean? -- 4.4.1.2 What Does Static Business Analytics Mean? -- 4.4.2 Level 2: Proactive-Situational Business Analytics -- 4.4.2.1 Why a Proactive Approach Is Such an Important Step? -- 4.4.2.2 Why Does It Have to Be Situational? -- 4.4.3 Level 3: Interactive-Dynamic Business Analytics -- 4.4.4 Stage 4: Dynamic Modeling Predictive Intelligence -- 4.4.4.1 What Does Predictive Intelligence Mean? -- 4.4.4.2 What Do ABM and Crawler Mean? -- 4.5 What Is the Decisive Factor? -- Further Reading -- 5: The Predictive Intelligence Self-Assessment -- 5.1 The Necessity for the PI-SA -- 5.2 What Are the Main Areas of the PI-SA? -- 5.2.1 The PI Potential Index -- 5.2.2 The Value Chain Index -- 5.2.2.1 The Different Markets -- 5.2.3 The Cost Efficiency Index. ; 5.2.4 The Structure Index -- 5.2.5 The Strategy Index -- 5.2.6 The Distribution Index -- 5.2.7 The PI Infrastructure Index -- 5.2.8 The PI Competence Index -- 5.3 The Evaluation of the PI-SA -- 5.4 Knowing Where You Are -- Further Reading -- 6: The Process Model for Predictive Intelligence -- 6.1 Every Path Begins with the First Step -- 6.2 The Preparation -- 6.2.1 Understanding the Batch Situation -- 6.2.2 Include the Allies -- 6.3 Phase 1: Reactive-Static Business Analytics -- 6.3.1 Where Does the Shoe Pinch? -- 6.3.2 Who Delivers What? -- 6.3.3 To Be or Not to Be -- 6.3.4 What a Basic Data Model Has to Achieve? -- 6.4 Phase 2: Proactive-Situational Business Analytics -- 6.4.1 Dashboards: Let´s Get Fancy! -- 6.4.2 It Is Time for Customer Data! -- 6.4.2.1 Why to Start from Zero Again! -- 6.4.2.2 How to Get to a CRM Quickly? -- 6.4.2.3 Why the Turbo Is Data Convergence? -- 6.4.3 With the Value Chain to the Relevant Market -- 6.4.3.1 How to Proceed? -- 6.4.4 How the Evaluations Are Becoming More and More Informative? -- 6.5 Phase 3: Interactive-Dynamic Business Intelligence -- 6.5.1 How a Key User Network Is Established? -- 6.5.2 How the 360 Data Panorama Is Achieved? -- 6.5.3 With Tertiary Processes into a Completely New Dimension -- 6.6 Phase 4: Dynamic-Modeling Predictive Intelligence -- 6.6.1 How Template-Based Intelligence Saves the Consultant? -- 6.6.2 With AI It Goes into the Cloud -- 6.7 What the Quintessence Is? -- Further Reading -- 7: The Predictive Intelligence TechStack (PITechStack) -- 7.1 Is Predictive Intelligence an IT Topic? -- 7.2 IT: Quo Vadis? -- 7.2.1 What Are the Benefits of Edge Computing? -- 7.2.2 With the Blockchain to the PI-Portal -- 7.2.3 Software-Based Virtualization Is on the Rise -- 7.3 Where IT Stands in 2030 -- 7.3.1 The Sale Is Dead, Because It Is About H2H -- 7.3.2 More Agile with Networks. ; 7.3.3 Full Speed Ahead into the Netflix Economy -- 7.3.4 From Big Data to Smart Data -- 7.3.5 From Resources to Smart Materials -- 7.4 What We Can Learn from the MarTechstack? -- 7.4.1 Why the Consulting Industry Is Disrupted? -- 7.4.2 What MarTech and SalesTech Teach Us? -- 7.4.3 Away from the ``Or´´ to the ``And,´´ and Yet Less Is More! -- 7.5 The Three Phases of the PITechStack -- 7.5.1 The Excelling -- 7.5.2 The Connecting -- 7.5.3 The Shopping -- 7.6 The PITechStack-Blueprint at a Glance -- 7.6.1 What Do Data and Information Applications Do? -- 7.6.2 Visualization and Processing Applications -- 7.6.3 Integration and Evaluation Applications -- 7.6.4 AdHoc Consultants -- 7.7 What Is The Environment of an Effective PITechStack? -- Further Reading -- 8: The Predictive Intelligence Team -- 8.1 Is Predictive Intelligence Also a Cathedral? -- 8.2 What Managers Need to Know -- 8.2.1 What Does It Mean to Live Predictive Intelligence? -- 8.2.2 How Can Data Be Managed as a Strategic Resource? -- 8.2.3 How Do You Build a Data-Driven Management System? -- 8.2.4 How to Create Competitive Advantages Through Predictive Intelligence -- 8.3 What the Perfect PI Team Looks Like -- 8.3.1 What Humanontogenetics Teaches Us -- 8.3.2 Why the Good Is so Obvious -- 8.3.3 Demand Determines the Dynamics -- 8.3.4 Why the PI Team Sets the Direction -- 8.4 The Predictive Intelligence Competence Model -- 8.4.1 Industry and Product Competence (IPC) -- 8.4.2 Analytical Thinking (AT) -- 8.4.3 Data Management -- 8.4.4 Data Exploration -- 8.4.5 Data Algorithmics -- 8.4.6 Data Visualization -- 8.4.7 Technology Management -- 8.4.8 Strategic Thinking -- 8.4.9 Leadership -- 8.5 The Predictive Intelligence Competence Matrix -- Further Reading -- 9: The Predictive Intelligence Case Studies -- 9.1 Why Predictive Intelligence Is Not Rocket Science. ; 9.2 Where Does the Case Study Company Come from? -- 9.2.1 Why the Situation Became Difficult -- 9.2.2 Instead of the Watering Can Ray Needs the Spear -- 9.2.3 You Do Not Have to Be Everybody´s Darling! -- 9.2.4 Start Small and Show Results -- 9.3 Showcase 1: Short-Term, Operational Predictive Intelligence -- 9.4 Showcase 2: Medium-Term Operational-Strategic Predictive Intelligence -- 9.5 Showcase 3: Long-Term Strategic-Tactical Predictive Intelligence -- 9.6 Why a Glass Ball Is Not Needed -- Further Reading -- 10: Why It Remains Exciting -- Further Reading -- Index.
• Titolo autorizzato: Predictive intelligence for data-driven managers
• ISBN: 3-030-69403-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910483118303321
• Lo trovi qui: Univ. Federico II
• Serie: Future of business and finance.