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Book Description

Organizations are rapidly consuming more data than ever before, and to drive their competitive advantage, they’re demanding interactive visualizations and interactive analyses of that data be embedded in their applications and business processes. This will enable them to make faster and more effective decisions based on data, not guesses.

This practical book examines the considerations that software developers, product managers, and vendors need to take into account when making visualization and analytics a seamlessly integrated part of the applications they deliver, as well as the impact of migrating their applications to modern data platforms.

Authors Federico Castanedo (Vodafone Group) and Andy Oram (O’Reilly Media) explore the basic requirements for embedding domain expertise with fast, powerful, and interactive visual analytics that will delight and inform customers more than spreadsheets and custom-generated charts. Particular focus is placed on the characteristics of effective visual analytics for big and fast data.

  • Learn the impact of trends driving embedded analytics
  • Review examples of big data applications and their analytics requirements in retail, direct service, cybersecurity, the Internet of Things, and logistics
  • Explore requirements for embedding visual analytics in modern data environments, including collection, storage, retrieval, data models, speed, microservices, parallelism, and interactivity
  • Take a deep dive into the characteristics of effective visual analytics and criteria for evaluating modern embedded analytics tools
  • Use a self-assessment rating chart to determine the value of your organization’s BI in the modern data setting

Table of Contents

  1. 1. Delivering Embedded Analytics in Modern Applications
    1. Overview of Trends Driving Embedded Analytics
    2. The Impact of Trends on Embedded Analytics
    3. Modern Applications of Big Data
      1. Industries and Use Cases Ripe for Modern Data-Driven Solutions
      2. Cybersecurity
      3. Internet of Things
      4. Logistics
    4. Considerations for Embedding Visual Analytics into Modern Data Environments
      1. Collection
      2. Storage
      3. Retrieval
      4. Data Models
      5. Output
      6. Speed
      7. Microservices
      8. Parallelism
      9. Interactive Visualizations
      10. Summary
    5. Deep Dive: Visualizations
      1. Flexibility
      2. Ease of Use
      3. Filters for Guided Analytics
      4. Switch Between Real-Time and Historical Data
      5. Embedded Visualizations
    6. Conclusion
  2. A. Self-Assessment Rating Chart