It's all analytics, part 3 the applications of ai, analytics, and data science
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Routledge
[2024]
|
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009784596906719 |
Tabla de Contenidos:
- Cover
- Half Title
- Title
- Copyright
- Contents
- Acknowledgement
- Bios of Authors
- Preface-Basis for This Book and the Series
- 1 Introduction
- 1.1 Introduction
- 1.2 Why You Should Read This Book
- 1.3 How This Book Is Organized
- 2 Data and Analytics Maturity Models
- 2.1 Contrasting Some Analytics Maturity Models
- 2.1.1 Another View of Analytics Maturity
- 2.2 Assessment Practices
- 2.2.1 Analytic Processes Maturity Model (APMM)
- 2.2.2 Analytics Maturity Assessment or Delta Plus Method
- 2.2.3 Gartner's Maturity Model for Data and Analytics
- 2.2.4 SAS Analytics Maturity Scorecard
- 2.2.5 TDWI Analytics Maturity Model
- 2.2.5.1 Determining an Organization's Maturity Level
- 2.3 The Analytics Model Moving Forward in This Book
- 2.3.1 Level 1 Explorers, Chapter Three
- 2.3.2 Level 2 Novices, Chapter 4
- 2.3.3 Level 3 Practitioners, Chapter 5
- 2.3.4 Level 4 Leaders, Chapter 6
- 2.3.5 Level 5 Innovators, Chapter 7
- References
- 3 The Explorers
- 3.1 Introduction
- 3.2 Some Characteristics of Explorers
- 3.3 Common Technology Employed by Explorers
- 3.3.1 Where Explorers Are Gaining the Most from Data and Analytics
- 3.4 Healthcare Explorers
- 3.4.1 Self-Improvement Is Often the Best Improvement- How Can Analytics Help?
- 3.5 Manufacturing Explorers
- 3.5.1 A Mom-and-Pop Shop Learns Statistical Process Control (SPC)
- 3.6 Retail Explorers
- 3.6.1 Small Plant Nursery Benefits from Marketing Analytics
- 3.6.2 Auto Parts Retailer Discovers Demand Forecasting and Inventory Management Analytics
- 3.7 Energy Explorers
- 3.7.1 Green Energy Company Leverages the Power of Financial Simulation
- 3.8 Financial Services and Insurance Explorers
- 3.8.1 A Small Credit Union Enters the Analytics Space
- 3.9 Other Explorers
- 3.9.1 Smart Apps Enabling "Low-Tech" Industries from Farmers and Ranchers.
- 3.9.1.1 AI-Enabled Smart App for Cattle Weighing
- 3.9.1.2 AI-Enabled Smart Apps for Plant and Animal Identification
- 3.10 Challenges from Moving Explorers to Novices
- 3.10.1 Separating the Urgent from the Important
- 3.10.2 Projects
- 3.10.3 Separating AI and Analytics Hype from Reality
- 3.10.4 Getting Data Right and Consistent
- 3.10.5 Budget
- 3.10.6 People
- 3.11 Conclusion and Next Steps for Explorers
- References
- 4 The Novices
- 4.1 Introduction
- 4.2 Some Characteristics of Novices
- 4.3 Common Technology Employed by Novices
- 4.4 Where Novices Are Gaining the Most for Data and Analytics
- 4.5 Healthcare Novices
- 4.5.1 Medium-Sized Physician Group Gets Boost in Revenue Cycle Management with Analytics Platform
- 4.5.2 Fetal Ultrasound and Estimated Weight Accuracy
- 4.6 Manufacturing Novices
- 4.6.1 A Large Food Products Company Innovates with ML and Visual Dashboards
- 4.6.2 Engineering and Construction Firm Uses Simulation to Improve Reliability
- 4.7 Retail Novices
- 4.7.1 Family-Owned Grocery Chain Starts CRM Program
- 4.7.2 Small Business Dream of Website and Internet Sales Happens
- 4.8 Energy Novices
- 4.8.1 Small Electric Co-op Improves Inspections with Analytics
- 4.9 Financial Services and Insurance Novices
- 4.9.1 Medium-Sized Bank Capitalizes on Workflow and Analytics
- 4.9.2 Medium-Sized Mortgage Company Cleans Data and Begins Forecasting
- 4.10 Other Novices
- 4.10.1 Dallas Auto Body and Painting Company Forecasting Labor
- 4.11 Challenges from Moving Novices to Practitioners
- 4.11.1 Team Changes May Be Required to Move to the Next Level of AI and Analytics Maturity
- 4.12 Conclusion
- 5 The Practitioners
- 5.1 Introduction
- 5.2 Some Characteristics of Practitioners
- 5.3 Common Technology Employed by Practitioners
- 5.3.1 Work Your Strategy Roadmap Backwards.
- 5.4 Healthcare Practitioners
- 5.4.1 Smart Medical Devices
- 5.4.2 Time to Diagnosis
- 5.4.3 Electronic Health Records (EHRs), the Electronic Medical Records (EMRs)
- 5.4.4 Using AI and Analytics to Determine Dental Group Practice Quality
- 5.4.5 Some Ways Various Healthcare Professionals Are Using Textual Analytics
- 5.5 Manufacturing Practitioners
- 5.5.1 Predictive Maintenance to Proactively Identify and Address Impending Equipment Failure
- 5.6 Retail Practitioners
- 5.7 Telecommunication Practitioners
- 5.8 Energy Practitioners
- 5.8.1 Major Energy Producer Unites Data Scientists and Subject Matter Experts
- 5.8.2 Large Utility Company Adds AI Project to Its Digital Transformation Project
- 5.9 Insurance Practitioners
- 5.9.1 CEO of Property and Casualty Insurance Company Delivers Analytics Mandate
- 5.10 Other Practitioners
- 5.11 Conclusion and Next Steps for Practitioners
- Reference
- 6 The Leaders
- 6.1 Introduction
- 6.2 Some Characteristics of Leaders
- 6.2.1 Organizational and Structural Differences
- 6.2.1.1 Notes on Organizational Structure and Design
- 6.2.2 Data Excellence
- 6.2.2.1 The Use of Unstructured Data-New Horizon Leaders Are Tapping Into
- 6.2.3 Self-Service That Enables Everyone in the Organization
- 6.3 Healthcare Leaders
- 6.3.1 Using Regulations and Mandates as a Catalyst for Innovation
- 6.4 Manufacturing Leaders
- 6.4.1 Smarter Identification of Defects with Text Analytics
- 6.5 Retail Leaders
- 6.5.1 Assortment
- 6.5.2 Right Value Proposition
- 6.5.3 Right Time/Location/Delivery
- 6.6 Energy Leaders
- 6.6.1 Global Top Five Oil Producer Manages Equipment with Text Analytics
- 6.7 Financial Services and Insurance Leaders
- 6.7.1 Insurance Holding Company Reduces Fraud Risk and Improves Operations with Text Mining (Analytics)
- 6.7.1.1 The Pilot
- 6.7.1.2 Results after Two Years.
- 6.8 Other Leaders
- 6.9 Challenges from Moving Leaders to Innovators
- References
- 7 The Innovators
- 7.1 Introduction
- 7.2 Some Characteristics of Innovators
- 7.3 Hot Topics of Innovators
- 7.3.1 ChatGPT
- 7.3.2 TinyML
- 7.3.3 Geospatial Machine-Learning Satellite
- 7.3.3.1 Finally, the New Sensor Suites Are Special Purpose
- 7.3.4 Flexible, Scalable, Adaptable Architectures for Data, AI, People
- 7.4 Simulation and Synthetic Data
- 7.4.1 Simulation
- 7.4.2 Synthetic Data
- 7.5 Causal Inference
- 7.5.1 Predictive Models vs Prescriptive Models-They Are Not the Same!
- 7.6 Healthcare Innovators
- 7.6.1 Genetic Diagnostics
- 7.6.2 Integrated Genetic-Phenotypic Diagnostics
- 7.6.3 Drug Discovery and Development
- 7.6.4 RNA Therapies
- 7.6.5 Infectious Disease Profiling, Epidemiology, and Therapeutics
- 7.6.6 Agricultural Biology
- 7.6.7 Conservation
- 7.6.7.1 Medical Innovators Using AI and Sound for Better Care and Outcomes
- 7.6.7.2 Artificial Intelligence Could Soon Diagnose Illness Based on the Sound of Your Voice
- 7.6.7.3 Large Academic Medical Center in Texas Uses Simulation for Outcomes
- 7.7 Manufacturing Innovators
- 7.8 Retail Innovators
- 7.8.1 Large US Retailer Improves the Customer Experience with AI
- 7.9 Energy Innovators
- 7.9.1 Predictive Maintenance
- 7.9.2 Optimization of Energy Generation and Distribution
- 7.9.3 Data Analysis and Visualization
- 7.9.4 Cybersecurity
- 7.9.5 ML Modeling Wind for More Efficient Energy Use
- 7.10 Financial Services and Insurance Innovators
- 7.11 Where Do Innovators Go from Here?
- 8 Data and Analytics Readiness
- 8.1 Introduction
- 8.2 Sources of Failure
- 8.3 Determining an Organization's Readiness to Execute with Data and Analytics
- 8.3.1 Conducting a Data and Analytics Readiness Assessment
- 8.3.1.1 The Framework Overview.
- 8.3.1.2 Goals of Meetings with Senior Leadership
- 8.3.1.3 Meetings with Key Leaders
- 8.3.1.4 Execute the Communication Process
- 8.3.1.5 Conduct Interviews with Designated Leaders, Managers, and High-Priority Staff
- 8.3.1.6 Provide a Written Questionnaire for Remaining Designated Staff
- 8.3.1.7 Perform an Extensive Analysis of Findings
- 8.3.1.8 Creation of a Written Report with Detailed Study Findings
- 8.3.1.9 Present Findings to Leadership and Determine Next Steps
- 8.3.1.10 Presentation to the Organization
- 8.4 Redesigning an Organization to Execute with Data and Analytics
- 8.4.1 Business Knowledge
- 8.4.2 Data Knowledge
- 8.4.3 AI and Analytics Knowledge
- 8.4.4 Technology Stack-Architecture, Platforms, Systems
- 8.4.5 Culture
- 8.4.6 People
- 8.5 Conclusion
- References
- 9 Additional Stories on the Data Front
- 9.1 Managing the End-to-End Process of Patient Experience-Two Case Studies by Carol Maginn
- 9.1.1 Patient Case A
- 9.1.2 Patient Case B
- 9.1.3 Pricing Offers an Intuitively Simple Problem That Provides Use of the Most Complicated Mathematical Models
- 9.2 Textual Extraction, Transformation, and Learning
- 9.2.1 Some Sources of Textual Data
- 9.2.1.1 Voice Recordings
- 9.2.1.2 Printed Text
- 9.2.1.3 Internet and Social Media
- 9.2.1.4 Email
- 9.2.1.5 Electronic Texts as a Source
- 9.2.1.6 What Is a Taxonomy?
- 9.2.1.7 What Is an Ontology?
- 9.2.1.8 Language and Taxonomies
- 9.2.1.9 Textual ETL and Taxonomies
- 9.2.2 Two Examples of Analysis Types
- 9.2.3 Correlative Analysis-An Introduction
- 9.2.4 Sentiment Analysis-An Introduction
- 9.2.5 The Semantic Layer
- 9.3 Less Tech Is Often Better than More Tech in Pricing Solutions
- 9.4 Data + Planning Is the Key to COVID Vaccine Center
- 9.5 Data Science Provider-Partner Relationships Greatly More Productive than Adversarial.
- 9.6 A Data Day in the Life of a Software Development Manager.
