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.
Author Donald Farmer, principal of TreeHive Strategy, shows business users how to improve decision-making without becoming analytic specialists. You'll explore different techniques for exchanging data, insights, and events between analytic 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.
Most of us are familiar today with Business Intelligence (BI). At one time, it was a new and exciting capability, but now, thanks to self-service technologies, the cloud and the power of in-memory processing, richly featured analytic applications, data visualisations, reports and dashboards are available to almost any business user who wants wants them.
However, each of these capabilities typically depend on separate applications. To perform business analysis, you need to open your BI suite. If you want to create a special charting, you may a look to a data visualisation application. Embedded analytics takes a somewhat different approach. The aim of embedding is to integrate visualisations, dashboards, reports and even predictive analytics or Artificial Intelligence (AI) capabilities inside your everyday business applications. So if you are managing a production line, preparing a budget or reviewing HR issues you can have analytic insights ready to hand to guide you.
Another source of data for an embedded system may be the output of a data integration pipeline. That is to say, rather than reading data from a table in an in-memory system or a data warehouse or an operational database, that data will be read from a pipeline running in a data science environment. The pipeline may perform numerous operations of integration, cleansing and preparation on the data from whatever source it comes. This is a very popular scenario for data science. But it is limited in use for embedded analytics, because the process is a little more fragile and a little more difficult to govern than a data warehouse or an in-memory system. This is because the pipeline is more dynamic and more volatile, only delivering data while it is running.
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 interact with traditional analytics
Who Should Read This Book:
We hope this book will work for a wide range of professionals who are involved in designing, building, or managing software applications that feature embedded analytics.
If you are responsible for developing software applications, this book will help you understand how to design and build applications with embedded analytics features. You will learn how to create applications that are more intuitive and efficient for the end user, as well as how to integrate analytics into the application development process.
If you are responsible for the overall design and architecture of software applications, this book will help you understand how to integrate analytics into your overall design strategy. You will learn how to create more intelligent and effective applications that are better suited to the needs of the user.
To get the most out of this book, readers should have a basic understanding of data analytics and software application design. Familiarity with programming languages and application development tools could also be helpful, but is not required. Overall, this book is designed to provide practical, actionable insights and advice to help professionals in a variety of roles create more intelligent and effective software applications that feature embedded analytics.

lgli/Embedded Analytics_Integrating Analysis (Final).epub

lgrsnf/Embedded Analytics_Integrating Analysis (Final).epub

zlib/Computers/Algorithms and Data Structures/Donald Farmer and Jim Horbury/Embedded Analytics: Integrating Analysis With the Business Workflow (Final Release)_25078505.epub

United States, United States of America

2022

{"isbns":["1098120930","1098145690","9781098120931","9781098145699"],"last_page":162,"publisher":"O'Reilly Media"}

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
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
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
About the Authors

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 performanceExplore advantages and disadvantages of different embedded analytics platformsDevelop a strategy for embedded analytics in an organization or productDefine the architecture of an embedded solutionSelect vendors, platforms, and tools to implement your architecture Hire or train developers and architects to build the embedded solutions you needUnderstand how embedded analytics interacts with traditional analytics

2023-05-16