Augmented Analytics

Spis treści książki

• Foreword
• Preface
  • Who Should Read This Book?
  • Learning Objectives
  • Navigating This Book
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. The Business Transformation
  • Why Businesses Are Transforming
    • Factor 1: The Speed of Change
    • Factor 2: The Convergence of Multiple Technologies
    • Factor 3: The Importance of Data
    • Factor 4: Changing Consumer Behavior and Customer Centricity
  • Industries Heavily Impacted by Digital Transformation
  • The Consequences for Your Business
    • Theres No Analytics Transformation Without Augmented Analytics
    • A Data-Driven Culture
    • The People Problem and the Limits of Upskilling
  • Conclusion
• 2. The Analytics Problem
  • Finding Your Analytics Purpose
    • Competition and Customer Expectations
    • Operational Efficiency
    • Availability and User Friendliness
    • Innovation
    • Regulatory Compliance
    • How to Start Your Analytics Journey
  • Industry Examples
    • Ecommerce
    • Healthcare
    • Manufacturing
    • Financial Services
    • Government
    • Commercial Insurance
  • The Concept of Analytical Maturity
  • Determine Your Currentand FutureData Maturity
    • Stage 1: Data Reactive
      • Current status
        • Strategy
        • People and organization
        • Data ecosystem
        • Cultural change
      • Moving to the next level
    • Stage 2: Data Active
      • Current status
        • Strategy
        • People and organization
        • Data ecosystem
        • Cultural change
      • Moving to the next level
    • Stage 3: Data Progressive
      • Current status
        • Strategy
        • People and organization
        • Data ecosystem
        • Cultural change
      • Moving to the next level
    • Stage 4: Data Fluent
      • Current status
        • Strategy
        • People and organization
        • Data ecosystem
        • Cultural change
      • Moving to the final stage: Crossing the analytics chasm
  • Conclusion
• 3. Understanding Augmented Analytics
  • Definition
  • The Five Is of Augmented Analytics
  • Overcoming the Limitations of Traditional Analytics Approaches
  • Augmented Workflows
  • The Benefits of Augmented Analytics
    • AA Gives Nonexpert Users a Better Experience
    • Automated Integration Provides More Complete Insights
    • AA Gives Faster, More Efficient Insights
    • Standardization Reduces Human Errors and Bias for Better Insights
    • AA Tools Are Easier to Scale Up
    • AA Reaches Further Afield to Generate Unexpected Insights
  • Overcoming Bias
  • Key Enablers of Augmented Analytics
    • Automation and AI
    • Artificial Intelligence: The Five Archetypes
      • Supervised machine learning
      • Natural language processing
      • Speech processing
      • Computer vision
      • Generative AI
  • The Limitations of Augmented Analytics
  • The Challenges of Augmented Analytics
  • Conclusion
• 4. Preparing People and the Organization for Augmented Analytics
  • Tailoring Augmented Analytics for Different Organizational Roles
    • Analytics Leader
      • Role and responsibilities
      • Necessary skills
      • Mindset
      • Importance in driving transformation
    • Analytics Translator
      • Role and responsibilities
      • Necessary skills
      • Mindset
      • Importance in driving transformation
    • Analytics User
      • Role and responsibilities
      • Necessary skills
      • Mindset
      • Personas
      • Importance in driving the transformation
    • Analytics Professional
      • Role and responsibilities
      • Necessary skills
      • Mindset
      • Importance in driving transformation
      • Technical specialist personas
      • Support specialist personas
    • Analytics Transformation Manager
      • Role and responsibilities
      • Strategy
      • Measurement
      • Execution
      • Enablement
      • Necessary skills
      • Mindset
      • Importance in driving transformation
    • Summary of Key Roles
  • The Center of Excellence
    • Creating a Center of Excellence
    • Approaches to Organizing a CoE
      • The decentralized approach
      • The centralized approach
      • The federated approach
  • Driving Transformational Change with the Influence Model
    • Fostering Understanding and Conviction
    • Reinforcing with Formal Mechanisms
    • Developing Talent and Skills
    • Role Modeling
  • Cultivating a Data-Literate Culture
    • Cultivating Analytics Awareness
    • Storytelling with Data
    • Embracing Data-Driven Management
    • Leading in the Age of AI
  • The Enablement Program
    • Training Formats for Analytics Leaders
      • Initial workshop
      • Follow-up programs
    • Training Formats for Analytics Translators
      • Initial workshop
      • Follow-up programs
    • Data Literacy Training
    • Technical Training
  • Conclusion
• 5. Augmented Workflows
  • Types of Workflow Augmentation
    • Fixed-Rule, High-Confidence Augmentation
    • Idea and Insight Enrichment
    • Conversational Augmentation
    • Contextual Augmentation
    • Collaborative Augmentation
  • The Analytics Use-Case Approach: Finding Workflows to Augment
    • Phase 1. Idea: The Initial Spark
      • Deliverables
      • Risk and ROI
      • Project maturity
    • Phase 2. Concept: Structuring the Idea
      • Assessing a use cases dimensions
      • Deliverables
      • Risk and ROI
      • Project maturity
    • Phase 3. Proof of Concept: Testing the Waters
      • Assessing business value
      • Assessing feasibility
      • Deliverables
      • Risk and ROI
      • Project maturity
    • Phase 4. Prototyping: Shaping the Concept
      • Deliverables
      • Risk and ROI
      • Project maturity
    • Phase 5. Pilot: The Test Run
      • Deliverables
      • Risk and ROI
      • Project maturity
    • Phase 6. Product: Full Deployment
      • Deliverables
      • Risk and ROI
      • Project maturity
    • Making the Make-or-Buy Decision
      • Its not a binary choice
      • Use-case stage
      • Strategic value
      • External solution performance
      • Internal resources
    • Decision Scenarios
      • Scenario 1: Prototyping a use case with low strategic value
      • Scenario 2: Prototyping a use case with high strategic value
      • Scenario 3: Productionizing a use case with low strategic value
      • Scenario 4: Productionizing a use case with high strategic value
    • Overarching Success Factors
  • Balancing Automation and Integration
  • The Use-Case Library
  • Technical Requirements for Implementing AA
    • Infrastructure Setup Challenges
    • IT System Integration Challenges
      • Dealing with legacy systems
      • Project dependencies
    • Governance Challenges
  • Conclusion
• 6. Augmented Frames
  • Business Objects and Frame Units
  • Understanding Frames
    • Key Features of Frames
    • Frame Types
  • Frame Engines
    • Frame Engine Types
    • Attribute Aggregation
    • Engine Interfaces
    • Result Objects
    • Implementation Challenges
  • Frame Agent
    • Dissolving Frames
    • Identifying Types
    • Translating Frame Units
    • Enriching Frames
    • Orchestrating Calls
    • Standardizing Results
    • Central Repository
    • Monitoring and Performance Analysis
    • User Access and Security
    • User Interface
  • Frame Dissolver
  • Frame Adapter
    • Dealing with Group Variables
    • Dealing with Bottom-up Business Object Structures
    • Dealing with Unconnected Business Objects
  • Frame Creator
  • Case Study: AP/TP Frame Engine
  • Infrastructure and Technology
  • An Iterative Approach to Introducing Augmented Frames
    • Iteration 1: Free Frames and Frame Engines
    • Iteration 2: A Frame Agent and Frame Adapter
    • Iteration 3: The Frame Dissolver, ID Frames, and Indexed Frames
    • Iteration 4: Static Frames
    • Iteration 5: Dynamic Frames
    • Iteration 6: The Frame Creator
    • Iteration Wrap-up
  • Conclusion
• 7. Applied Examples
  • The Underwriting Process
    • Types of Augmented Workflows in Underwriting
    • The Workflows in Detail
  • Example 1: Location Workflow
    • Situation and Problem Statement
    • Solution Overview
    • Solution Breakdown
      • Location-data quality assessment
      • Value imputation
      • Georeferencing
      • Matching
      • Natural catastrophe assessment
    • Example Summary
  • Example 2: Benchmarking Workflow
    • Situation and Problem Statement
    • Solution Overview
    • Solution Breakdown
      • Identifying peers
      • Gathering knowledge about peers
      • Evaluating existing policies
    • Example Summary
  • Example 3: Proposal Workflow
    • Situation and Problem Statement
    • Solution Overview
    • Solution Breakdown
      • Data collection and integration
      • User interaction and customization
      • Applying LLMs and predefined modules
      • Structured reporting and documentation
      • Output generation and communication
    • Example Summary
  • Example 4: Improved Forecasting in Agile Projects
    • Situation and Problem Statement
    • Solution Overview
    • Solution Breakdown
      • Data layer
      • Analysis layer
      • User layer
    • Example Summary
  • Example 5: Quick Sales Intelligence
    • Situation and Problem Statement
    • Solution Overview
    • Solution Breakdown
      • Data layer
      • Analysis layer
        • Information handling
        • Task orchestration
      • User layer
    • Example Summary
  • Conclusion
• Index

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