Embedded Analytics

Book description

Over the past 10 years, data analytics and data visualization have become essential components of an enterprise information strategy. And yet, the adoption of data analytics has remained remarkably static, reaching no more than 30% of potential users. This book explores the most important techniques for taking that adoption further: embedding analytics into the workflow of our everyday operations.

Authors Donald Farmer and Jim Horbury show business users how to improve decision making without becoming analytics specialists. You'll explore different techniques for exchanging data, insights, and events between analytics platforms and hosting applications. You'll also examine issues including data governance and regulatory compliance and learn best practices for deploying and managing embedded analytics at scale.

  • Learn how data analytics improves business decision making and performance
  • Explore advantages and disadvantages of different embedded analytics platforms
  • Develop a strategy for embedded analytics in an organization or product
  • Define the architecture of an embedded solution
  • Select vendors, platforms, and tools to implement your architecture
  • Hire or train developers and architects to build the embedded solutions you need
  • Understand how embedded analytics interacts with traditional analytics

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Table of contents

  1. Preface
    1. Who Should Read This Book
    2. Navigating This Book
    3. Conventions Used in This Book
    4. O’Reilly Online Learning
    5. How to Contact Us
    6. Acknowledgments
  2. 1. Introduction to Embedded Analytics
    1. Analytics for Business Users and Consumers
    2. What Success Looks Like
      1. Measurable Business Outcomes
      2. Engagement and Adoption
      3. Spreadsheets and Analytics
    3. A Game Plan for Embedded Analytics
      1. Understanding Where You Are
      2. Setting a Goal
      3. Mapping Out the Journey to Success
  3. 2. Analytics and Decision Making
    1. Executive and Strategic Decisions
    2. Operational Decisions
    3. Tactical Management Decisions
    4. A Design Pattern for the Analytic Experience
      1. Orientation
      2. Glimpsing
      3. Examining
      4. Deciding
    5. Ambiguity and Analytics
    6. Summary
  4. 3. Architectures for Embedded Analytics
    1. Elements of Embedded Analytics
      1. Data Connectivity
      2. The Analytics Engine
      3. Branding the User Experience
      4. Developer Resources
      5. Scalability
      6. Security
      7. Administration Tools
    2. Embedded Analytics Platforms
      1. Component Libraries
      2. Enterprise Reporting Platforms
      3. Business Intelligence Applications
      4. Purpose-Built Embedded Platforms
      5. Embedded Self-Service
    3. Summary
  5. 4. Data for Embedded Analytics
    1. CSV and Other Text Files
    2. Operational Data Sources
    3. Analytic Data Sources
      1. Data Warehouses
      2. In-Memory Engines
      3. Data Lakes
    4. Data Integration Pipelines
    5. Writing Back to Sources
    6. Summary
  6. 5. Embedding Analytics Objects
    1. What Can We Embed?
      1. Key Performance Indicators
      2. Data Visualizations
      3. Tabular Data
      4. Dynamic Text and NLG Content
    2. Adding Interactivity
      1. Interaction Examples
      2. Interaction as a Value-Add
      3. Technical Considerations
    3. Embedding Objects with iframes
      1. Using iframes to Our Advantage
      2. Cross-Domain Limitations
    4. Current Embedded Analytics Trends
      1. Using Embedded Analytics to Share Data
      2. Transformative Best-Practice Visualization
      3. The Look and Feel of Embedded Experiences
    5. Putting It All Together
      1. Embedding Workflow
      2. A Typical Reporting Automation Workflow
      3. Using Embedded Analytics to Power Prescriptive Analytics
      4. Operationalization (or “Write-Back”) of Data
      5. Business Case Integrations
      6. Management and Governance Integrations
    6. Conclusion
  7. 6. Administration of Embedded Analytics
    1. Deploying Embedded Analytics
      1. On-Premises Deployment
      2. Cloud Deployment
    2. IT Operations and DevOps for Embedded Analytics
    3. Security for Embedded Analytics
      1. Security Priorities for an Embedded Analytics Solution
      2. Open and Closed Systems
      3. Single Sign-on
      4. Summary of Security Practices
    4. Other Administrative Considerations
      1. Scheduling
      2. Version Management
      3. Report Bursting
      4. The Administrative Console
    5. Conclusion
  8. 7. Governance and Compliance
    1. Governance, Compliance, Security, and Privacy
      1. Privacy and Security
      2. Governance and Compliance
    2. Policies and Practices
      1. If Compliance Is Critical, You Need a Compliance Team
      2. Look for Secondary Benefits of Good Governance
      3. Commit to Openness, Awareness, and Training
    3. Governing Your Governance
      1. A Security and Privacy Cross-Functional Team
      2. Governance in the Cloud
      3. Business Continuity Is a Security and Privacy Issue
    4. Developing a Governance Strategy
    5. Measuring the Success of Governance
    6. Summary
  9. 8. Beyond the Spreadsheet
    1. Setting the Stage
    2. Let There Be Spreadsheets
    3. Are Spreadsheets Analytics Platforms?
      1. The Ubiquity of Excel
      2. What Excel Doesn’t Do
      3. Almost an Answer
    4. Beyond Excel
      1. Simple Reporting and Analytics
      2. Integration and Collaboration
      3. Project Management and Workflow
      4. Computational Notebooks
    5. Putting Spreadsheets in Context
      1. Choose the Spreadsheet Tool Carefully
      2. Don’t Break the Paradigm of Visual Analytics
      3. Pursue Consistent Methods for Interaction Where Possible
      4. Consider Highly Targeted Use Cases for Tables
    6. Conclusion
  10. 9. Data Science, Machine Learning, and Embedded Analytics
    1. DSML in Practice
      1. DSML Is Hard
      2. The Power of Storytelling
      3. When Things Go Wrong
    2. The DSML-Driven Call Center
      1. Propensity Modeling
      2. Training a Model
      3. Putting It into Practice
      4. Closing the Loop
    3. Other Typical Use Cases
    4. The Rise of Generative Language Services
    5. Conclusion
  11. 10. Analytics as a Line of Business
    1. Data as an Asset
    2. Data Products
    3. Product Analytics for Embedded Analytics Technologies
    4. Self-Service as a Feature
    5. Tiering an Analytics Product
    6. Pricing Embedded Analytics
    7. Supporting Embedded Analytics
    8. Launching Your Product
    9. Conclusion
  12. Index
  13. About the Authors

Product information

  • Title: Embedded Analytics
  • Author(s): Donald Farmer, Jim Horbury
  • Release date: May 2023
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098120931