Augmented Analytics - Helion

ebook

Autor: Willi Weber, Tobias Zwingmann
ISBN: 9781098151683
stron: 292, Format: ebook
Data wydania: 2024-05-31
Księgarnia: Helion

Cena książki: 194,65 zł (poprzednio: 226,34 zł)
Oszczędzasz: 14% (-31,69 zł)

Osoby, które kupiły tę książkę, wybierały także »

Augmented Analytics isn't just another book on data and analytics; it's a holistic resource for reimagining the way your entire organization interacts with information to become insight-driven.

Moving beyond traditional, limited ways of making sense of data, Augmented Analytics provides a dynamic, actionable strategy for improving your organization's analytical capabilities. With this book, you can infuse your workflows with intelligent automation and modern artificial intelligence, empowering more team members to make better decisions.

You'll find more in these pages than just how to add another forecast to your dashboard; you'll discover a complete approach to achieving analytical excellence in your organization.

You'll explore:

Key elements and building blocks of augmented analytics, including its benefits, potential challenges, and relevance in today's business landscape
Best practices for preparing and implementing augmented analytics in your organization, including analytics roles, workflows, mindsets, tool sets, and skill sets
Best practices for data enablement, liberalization, trust, and accessibility
How to apply a use-case approach to drive business value and use augmented analytics as an enabler, with selected case studies

This book provide a clear, actionable path to accelerate your journey to analytical excellence.

Osoby które kupowały "Augmented Analytics", wybierały także:

Windows Media Center. Domowe centrum rozrywki 66,67 zł, (8,00 zł -88%)
Ruby on Rails. Ćwiczenia 18,75 zł, (3,00 zł -84%)
Przywództwo w świecie VUCA. Jak być skutecznym liderem w niepewnym środowisku 58,64 zł, (12,90 zł -78%)
Scrum. O zwinnym zarządzaniu projektami. Wydanie II rozszerzone 58,64 zł, (12,90 zł -78%)
Od hierarchii do turkusu, czyli jak zarządzać w XXI wieku 58,64 zł, (12,90 zł -78%)

Spis treści

Augmented Analytics eBook -- spis treści

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