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Pro Salesforce Analytics Cloud: A Guide to Wave Platform, Builder, and Explorer

Book Description

This book explains Salesforce Analytics Cloud and provides a holistic view of different analytical capabilities and how they fit into the overall information architecture. It features real-world industry use cases and demonstrates how Salesforce’s Analytics Cloud solves business challenges and brings real value to the organization.

The Salesforce Analytics Cloud represents the rethinking of analytics for the business user. The Analytics Cloud is a cloud-based platform designed for the business user to have access to analytics "on the go," providing answers to questions instantly on any device. This mobile-ready capability of the Salesforce Analytics Cloud means users can immediately collaborate and share insights with team members right inside Salesforce.

Pro Salesforce Analytics Cloud provides actionable guidance on creating analytical capabilities using Salesforce Analytics Cloud. The book offers:

  • A practical guide to Salesforce Analytics Cloud, including Builder and Explorer.
  • Detailed business analytics use cases in various industries (e.g., manufacturing).
  • Advanced architectures and best practices for integration and data discovery.
  • Table of Contents

    1. Cover
    2. Title
    3. Copyright
    4. Dedication
    5. Contents at a Glance
    6. Contents
    7. About the Authors
    8. About the Technical Reviewers
    9. Acknowledgments
    10. Introduction
    11. Part I: A Complete Guide to the Salesforce Analytics Cloud
      1. Chapter 1: An Overview of the Salesforce Analytics Cloud
        1. Evolution of Business Intelligence and Reporting Systems
        2. The Challenge of Data Variety
          1. Data Uncertainty
          2. The Scarcity of Personnel
        3. Data Discovery
          1. Self-Service Application Development and Analytic Interface
          2. A Data Server
          3. Data-Loading Interfaces and APIs
          4. An Alternative to the Data Lake
        4. Introduction to the Salesforce Analytics Cloud
          1. The Mobile Platform
          2. The Desktop Platform
        5. Advantages of the Salesforce Analytics Cloud
          1. The Cloud Architecture
          2. Mobile First
          3. Integration with the Salesforce Ecosystem
        6. Conclusion
      2. Chapter 2: The Salesforce Analytics Cloud Explorer
        1. What Is Cloud Explorer?
          1. The Two Versions of the Salesforce Analytics Cloud
        2. Getting Started with Cloud Explorer
          1. Launching Cloud Explorer
        3. Datasets
          1. Row-Level Security for Datasets
        4. Lenses
          1. Measures
          2. Dimensions
          3. Dates
          4. Bringing It All Together
          5. Learn by Doing
          6. Lens Modification History, Undo, and Replay
          7. Exploring the Data
          8. Mobile Lens Creation
        5. Dashboards
          1. Widgets
          2. Data Display Widgets
          3. Selector Widgets
          4. Display Widgets
          5. The Dashboard Development Cycle
          6. Link Back to Lens
          7. Dashboard Wrap-Up
        6. Apps
          1. My Private App
          2. The Share Button
          3. Playground Data
        7. What Wave Lacks
          1. Text Processing and Analysis
          2. Map and Geospatial Data
          3. Advanced Math and Statistical Functions
        8. Conclusion
      3. Chapter 3: Analytics Cloud Builder
        1. Building a Simple Dashboard
          1. Loading Dataset
          2. Creating a Dashboard
        2. Dashboard Design Aesthetics
          1. Steps to Design Aesthetic Dashboards
        3. Mobile Dashboard Designer
          1. Upload Dataset Through Mobile Device
          2. Create Dashboard Using Mobile Designer
        4. Advanced Dashboards
          1. Anatomy of a dashboard
          2. Advanced Dashboard with SAQL
        5. Summary
      4. Chapter 4: Salesforce Analytics Platform
        1. Methods of Data Integration
        2. External Data API
          1. Step 1. Create .CSV File
          2. Step 2. Create JSON Metadata
          3. Step 3. Upload Files
          4. Step 4. System Job Is Created and Run
          5. Step 5. Dataset Is Created
          6. Restrictions on Use
          7. Supporting Documents
        3. Extract Transform Load Tools
          1. Step 1. Connect to Source
          2. Step 2. Extract Data
          3. Step 3. Sort, Filter, and Transform
          4. Step 4. Determine the Dataset
          5. Step 5. Write the Dataset
        4. Wave REST API
        5. Conclusion
    12. Part II: Business Analytics Solutions Using the Salesforce Analytics Cloud
      1. Chapter 5: Critical Decision Making and the Salesforce Analytics Cloud
        1. Critical Decision Making and Cognitive Bias
        2. The Use Case Manufacturer
          1. Early Glass Making
          2. Early Instrumentation and Increased Statistical Process Control
          3. Automatic Control via Computer Technology
          4. Data Networks Offer Increased Integration
        3. Company Structure and Facilities
          1. Float Glass Plants
          2. Glass Fabrication Plants
          3. Automotive Encapsulation Facilities
          4. The Organization of the Company’s Plants
        4. Key Performance Indicators
        5. Facility-Level Reporting
          1. Diagnosis of Machine Problem
          2. Eliminating Cognitive Bias with Data
        6. Corporate-Level Reporting
          1. Assessing Plant Manager Performance
        7. Conclusion
      2. Chapter 6: Mobile Enterprise Data Discovery
        1. Overview of Our Use Case
          1. The Traditional Approach to Reporting
          2. An Alternative to the Traditional Approach
        2. The Use Case Architecture
          1. Step 1 – Output to .CSV File
          2. Step 2 – User Access of .CSV File on Dropbox
          3. Step 3 – Modifying the Metadata and Creating the Dataset
          4. Step 4 – Creating Lenses and Dashboards
        3. Data Used in Our Use Case
          1. Sales Amount Data
          2. Date Data
          3. Product Data
          4. Reseller Data
        4. Dashboard Development Cycle
          1. Digging Deeper
          2. Adding Other Dimensions: Evaluating Individual Contributors
          3. Analyzing Other Dimensions: Resellers
        5. Security and Shared Public File Services
        6. Conclusion
      3. Chapter 7: Advanced Data Acquisition and Processing
        1. Data Acquisition—Tools and Technologies
          1. Web Scrapers
          2. Web Crawlers
          3. Text Analytics Processors
          4. Applications of These Tools
        2. Data Processing—Terms and Concepts
          1. Data Wrangling
          2. Data Standards
          3. Data Quality
          4. Data Cleansing
        3. Python: A Programming Language for Data Processing
        4. Two Examples Using Python
          1. Data Cleansing Example 1
          2. Data Cleansing Example 2
          3. Python Resources
        5. Conclusion
    13. Index