Embedded Analytics: Integrating Analysis with the Business Workflow

https://lavozdelascostureras.com/imot2ihso1g Preface Who Should Read This Book Navigating This Book Conventions Used in This Book O’Reilly Online Learning How to Contact Us Acknowledgments 1. Introduction to Embedded Analytics Analytics for Business Users and Consumers What Success Looks Like Measurable Business Outcomes Engagement and Adoption Spreadsheets and Analytics A Game Plan for Embedded Analytics Understanding Where You Are Setting a Goal Mapping Out the Journey to Success 2. Analytics and Decision Making Executive and Strategic Decisions Operational Decisions Tactical Management Decisions A Design Pattern for the Analytic Experience Orientation Glimpsing Examining Deciding Ambiguity and Analytics Summary 3. Architectures for Embedded Analytics Elements of Embedded Analytics Data Connectivity The Analytics Engine Branding the User Experience Developer Resources Scalability Security Administration Tools Embedded Analytics Platforms Component Libraries Enterprise Reporting Platforms Business Intelligence Applications Purpose-Built Embedded Platforms Embedded Self-Service Summary 4. Data for Embedded Analytics CSV and Other Text Files Operational Data Sources Analytic Data Sources Data Warehouses In-Memory Engines Data Lakes Data Integration Pipelines Writing Back to Sources Summary 5. Embedding Analytics Objects What Can We Embed? Key Performance Indicators Data Visualizations Tabular Data Dynamic Text and NLG Content Adding Interactivity Interaction Examples Interaction as a Value-Add Technical Considerations Embedding Objects with iframes Using iframes to Our Advantage Cross-Domain Limitations Current Embedded Analytics Trends Using Embedded Analytics to Share Data Transformative Best-Practice Visualization The Look and Feel of Embedded Experiences Putting It All Together Embedding Workflow A Typical Reporting Automation Workflow Using Embedded Analytics to Power Prescriptive Analytics Operationalization (or “Write-Back”) of Data Business Case Integrations Management and Governance Integrations Conclusion 6. Administration of Embedded Analytics Deploying Embedded Analytics On-Premises Deployment Cloud Deployment IT Operations and DevOps for Embedded Analytics Security for Embedded Analytics Security Priorities for an Embedded Analytics Solution Open and Closed Systems Single Sign-on Summary of Security Practices Other Administrative Considerations Scheduling Version Management Report Bursting The Administrative Console Conclusion 7. Governance and Compliance Governance, Compliance, Security, and Privacy Privacy and Security Governance and Compliance Policies and Practices If Compliance Is Critical, You Need a Compliance Team Look for Secondary Benefits of Good Governance Commit to Openness, Awareness, and Training Governing Your Governance A Security and Privacy Cross-Functional Team Governance in the Cloud Business Continuity Is a Security and Privacy Issue Developing a Governance Strategy Measuring the Success of Governance Summary 8. Beyond the Spreadsheet Setting the Stage Let There Be Spreadsheets Are Spreadsheets Analytics Platforms? The Ubiquity of Excel What Excel Doesn’t Do Controlling the proliferation of content Ability to support computation on large volumes of data Security and authorization Sharing and embedding Almost an Answer Beyond Excel Simple Reporting and Analytics Integration and Collaboration Project Management and Workflow Computational Notebooks Putting Spreadsheets in Context Choose the Spreadsheet Tool Carefully Don’t Break the Paradigm of Visual Analytics Pursue Consistent Methods for Interaction Where Possible Consider Highly Targeted Use Cases for Tables Conclusion 9. Data Science, Machine Learning, and Embedded Analytics DSML in Practice DSML Is Hard The Power of Storytelling When Things Go Wrong The DSML-Driven Call Center Propensity Modeling Training a Model Preparing the data Utilizing an ML service Training and testing a model Putting It into Practice Closing the Loop Other Typical Use Cases The Rise of Generative Language Services Conclusion 10. Analytics as a Line of Business Data as an Asset Data Products Product Analytics for Embedded Analytics Technologies Self-Service as a Feature Tiering an Analytics Product Pricing Embedded Analytics Supporting Embedded Analytics Launching Your Product Conclusion Index