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Index
Preface
Who This Book Is For O’Reilly Online Learning How to Contact Us Acknowledgments from the Authors Acknowledgments from Paul Zikopoulos
1. What in the AI? How Did We Get Here?
Collecting Data in Real Time, but Understanding It in Stale Time The Modality of Everything and the Data Collection Curve Even Steeper: The Future of the Data Collection Curve Where We Are Now—Haystacks, Needles, and More Data How to Displace Today’s Disruptors Let’s Get Ready for a Climb!
2. The Journey to AI
What Is Artificial Intelligence, Anyway?
Types of AI Data Models
Where AI Has Been What Does AI Mean for Business? The Journey to AI All Radically New Technologies Face Resistance Where Are We Now? And Where Are We Going? Moving Forward
3. How to Overcome AI Failures and Challenges
AI’s Emergence in Business Today
Data Computing Power Investment
Early Examples of AI Success
Example: Vodafone’s TOBi Transforms the Customer Experience Example: How a French Bank Built on Its Strength of Quality Customer Service
Early AI Failures AI Challenges: Data, Talent, Trust
AI Challenge: Data AI Challenge: Talent AI Challenge: Trust
Overcoming Challenges with Advanced Research and Products Overcoming Challenges with the Right Partner
4. The AI Ladder: A Path to Organizational Transformation
Suitability of AI Determining the Right Business Problems to Solve with AI Building a Data Team Putting the Budget in Place Developing an Approach There Is No AI Without IA The AI Ladder
Collect Organize Analyze Infuse
Simplify, Automate, and Transform
5. Modernize Your Information Architecture
A Modern Infrastructure for AI
All Parts Are Visible Legacy Systems Are Made Accessible or Eliminated All Parts of the System Are Continuously Monitored Inefficiencies Are Identified and Removed New Architectures for IT
Data: The Fuel; Cloud: The Means
To the Cloud, and Beyond: Cloud as Capability Fuel for the Fire
From Databases to Data Warehouses, Data Marts, and Data Lakes
Example: Wireless Carrier Architects a Solution Using Both a Data Lake and a Data Warehouse
Data Virtualization
Unifying Access to Data Through Big SQL Object Storage as the Preferred Fabric Open Data Stores and Open Data Formats Next-Generation Databases
The Power of an AI Database Streaming Data Get the Right Tools The Importance of Open Source Technologies
Community Thinking and Culture High Code and Component Quality
Real Examples of Modernizing IT Infrastructure
Example: Siemens Looks to the Cloud to Unify Its Data Processes Example: Fannie Mae Transforms with a Governed and Centralized Data Environment
Don’t Neglect the Foundation!
6. Collect Your Data
What Needs to Happen on the Collect Rung
Example: EMC Develops a Data Collection Strategy
Start with a Data Census: Learn What’s Out There Understand Data in a Business Context, and Partner with SMEs Getting Beyond Transactional Data The Challenges of Collecting New Sources of High-Volume Unstructured Data Organizational Aspects of Data Access
Example: Procter & Gamble Avoid Data Silos Using a Central Data Warehouse Example: eBay Eliminates Data Silos by Publishing Business Processes as APIs
Ownership, Stewardship, Regulatory Compliance, and Discipline
Example: Owens-Illinois
Collecting Data: You Can Win This Battle!
7. Organize Your Data
Poor Data Leads to Poor AI Regulation Demands Quality Data What Needs to Happen on the Organize Rung Cleaning Data Documenting and Cataloging Data Understanding Data: The “Seller” Gong Show Metadata for Models Maintaining the Catalog Governing Data
Enterprise Performance Management Example: ANZ Banking Group Embeds Sound Data Management and Governance Policies
DataOps Now That Your Data Is Trustworthy, on to Analysis!
8. Analyze Your Data
Why Organizations Need an End-to-End AI Lifecycle Build
Example: Using Machine Learning, an Insurer Cuts Costs and Boosts Productivity
Run Manage
Aligning Model Output with Business Metrics Learning, Iterating, Learning Example: Risk Management Company Gets Creative to Offset the Expense of Training Models
Automating the AI Lifecycle
AutoAI NeuNetS
Incorporating AI into DevOps Processes Emphasizing Trust and Transparency
Example: By Shining Light on Data Attributes, a Bank’s AI System Demonstrates Integrity, Fairness, Explainability, and Resiliency Example: Avoiding the “Black Box” Dilemma Avoiding the Piecemeal Approach Example: SaaS Company Gleans New Insights by Applying AI to Historical Data
Ready to Infuse...
9. Infuse AI Throughout the Business
Customer Service Financial Operations Risk and Compliance IT Operations Business Operations Themes Across All Intelligent Workflows Building the Next-Generation C-Suite
10. Tips and Best Practices on How to Get Started
Manage Organization-Wide Change
Change in Daily Tasks Change in Overall Business Processes Change in Thinking About Data
Make Data a Team Sport (And Some Cool History About Car Racing)
Subject Matter Experts Data Scientists Data Operations (DataOps) Specialists Data Engineers Training for Career Development
Embrace AI Centers of Excellence
Example: Honda Sets Up Knowledge Hubs to Build Minimum Viable Products, Organize Training, Share Data
Build Ethics Into Your Process
Privacy Safety Fairness Building Trust in AI
Choose Projects Selectively, and Embrace Failure
Example: Insurer Tracks Metrics to Communicate Success of Its Model
Beware of False Negatives
11. The Future of AI
AI Themes to Take Us Through the Next Five Years
Theme #1: AI Is Not a Fad Theme #2: Data-Generating Sensors Will Proliferate Theme #3: Data Will Be Processed at the Edge Theme #4: AI Will Spread Everywhere Theme #5: AI Will Disappear into the Background and Become Boring
Future AI Use Cases for Business
Cybersecurity Autonomous Driving, Autonomous Everything Conversational Digital Agents and Personal Assistants Real Estate Retail Insurance Customer Service
The Future of Work in an AI-Driven World A Deeper Dive into AI and Edge Computing
Using the Edge and AI for Good
Conclusion
Index
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