book

It's All Analytics, Part III

Name: It's All Analytics, Part III
ISBN: 9781000928440

by Scott Burk, Gary Miner

September 2023

Intermediate to advanced

260 pages

6h 41m

English

Productivity Press

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Cover
Half Title
Title
Copyright
Contents
Acknowledgement
Bios of Authors
Preface—Basis for This Book and the Series
1 Introduction
1.1 Introduction1.2 Why You Should Read This Book1.3 How This Book Is Organized
2 Data and Analytics Maturity Models
2.1 Contrasting Some Analytics Maturity Models2.1.1 Another View of Analytics Maturity2.2 Assessment Practices2.2.1 Analytic Processes Maturity Model (APMM)2.2.2 Analytics Maturity Assessment or Delta Plus Method2.2.3 Gartner's Maturity Model for Data and Analytics2.2.4 SAS Analytics Maturity Scorecard2.2.5 TDWI Analytics Maturity Model2.2.5.1 Determining an Organization's Maturity Level2.3 The Analytics Model Moving Forward in This Book2.3.1 Level 1 Explorers, Chapter Three2.3.2 Level 2 Novices, Chapter 42.3.3 Level 3 Practitioners, Chapter 52.3.4 Level 4 Leaders, Chapter 62.3.5 Level 5 Innovators, Chapter 7References

3 The Explorers
3.1 Introduction3.2 Some Characteristics of Explorers3.3 Common Technology Employed by Explorers3.3.1 Where Explorers Are Gaining the Most from Data and Analytics3.4 Healthcare Explorers3.4.1 Self-Improvement Is Often the Best Improvement—How Can Analytics Help?3.5 Manufacturing Explorers3.5.1 A Mom-and-Pop Shop Learns Statistical Process Control (SPC)3.6 Retail Explorers3.6.1 Small Plant Nursery Benefits from Marketing Analytics3.6.2 Auto Parts Retailer Discovers Demand Forecasting and Inventory Management Analytics3.7 Energy Explorers3.7.1 Green Energy Company Leverages the Power of Financial Simulation3.8 Financial Services and Insurance Explorers3.8.1 A Small Credit Union Enters the Analytics Space3.9 Other Explorers3.9.1 Smart Apps Enabling “Low-Tech” Industries from Farmers and Ranchers3.9.1.1 AI-Enabled Smart App for Cattle Weighing3.9.1.2 AI-Enabled Smart Apps for Plant and Animal Identification3.10 Challenges from Moving Explorers to Novices3.10.1 Separating the Urgent from the Important3.10.2 Projects3.10.3 Separating AI and Analytics Hype from Reality3.10.4 Getting Data Right and Consistent3.10.5 Budget3.10.6 People3.11 Conclusion and Next Steps for ExplorersReferences
4 The Novices
4.1 Introduction4.2 Some Characteristics of Novices4.3 Common Technology Employed by Novices4.4 Where Novices Are Gaining the Most for Data and Analytics4.5 Healthcare Novices4.5.1 Medium-Sized Physician Group Gets Boost in Revenue Cycle Management with Analytics Platform4.5.2 Fetal Ultrasound and Estimated Weight Accuracy4.6 Manufacturing Novices4.6.1 A Large Food Products Company Innovates with ML and Visual Dashboards4.6.2 Engineering and Construction Firm Uses Simulation to Improve Reliability4.7 Retail Novices4.7.1 Family-Owned Grocery Chain Starts CRM Program4.7.2 Small Business Dream of Website and Internet Sales Happens4.8 Energy Novices4.8.1 Small Electric Co-op Improves Inspections with Analytics4.9 Financial Services and Insurance Novices4.9.1 Medium-Sized Bank Capitalizes on Workflow and Analytics4.9.2 Medium-Sized Mortgage Company Cleans Data and Begins Forecasting4.10 Other Novices4.10.1 Dallas Auto Body and Painting Company Forecasting Labor4.11 Challenges from Moving Novices to Practitioners4.11.1 Team Changes May Be Required to Move to the Next Level of AI and Analytics Maturity4.12 Conclusion
5 The Practitioners
5.1 Introduction5.2 Some Characteristics of Practitioners5.3 Common Technology Employed by Practitioners5.3.1 Work Your Strategy Roadmap Backwards5.4 Healthcare Practitioners5.4.1 Smart Medical Devices5.4.2 Time to Diagnosis5.4.3 Electronic Health Records (EHRs), the Electronic Medical Records (EMRs)5.4.4 Using AI and Analytics to Determine Dental Group Practice Quality5.4.5 Some Ways Various Healthcare Professionals Are Using Textual Analytics5.5 Manufacturing Practitioners5.5.1 Predictive Maintenance to Proactively Identify and Address Impending Equipment Failure5.6 Retail Practitioners5.7 Telecommunication Practitioners5.8 Energy Practitioners5.8.1 Major Energy Producer Unites Data Scientists and Subject Matter Experts5.8.2 Large Utility Company Adds AI Project to Its Digital Transformation Project5.9 Insurance Practitioners5.9.1 CEO of Property and Casualty Insurance Company Delivers Analytics Mandate5.10 Other Practitioners5.11 Conclusion and Next Steps for PractitionersReference
6 The Leaders
6.1 Introduction6.2 Some Characteristics of Leaders6.2.1 Organizational and Structural Differences6.2.1.1 Notes on Organizational Structure and Design6.2.2 Data Excellence6.2.2.1 The Use of Unstructured Data—New Horizon Leaders Are Tapping Into6.2.3 Self-Service That Enables Everyone in the Organization6.3 Healthcare Leaders6.3.1 Using Regulations and Mandates as a Catalyst for Innovation6.4 Manufacturing Leaders6.4.1 Smarter Identification of Defects with Text Analytics6.5 Retail Leaders6.5.1 Assortment6.5.2 Right Value Proposition6.5.3 Right Time/Location/Delivery6.6 Energy Leaders6.6.1 Global Top Five Oil Producer Manages Equipment with Text Analytics6.7 Financial Services and Insurance Leaders6.7.1 Insurance Holding Company Reduces Fraud Risk and Improves Operations with Text Mining (Analytics)6.7.1.1 The Pilot6.7.1.2 Results after Two Years6.8 Other Leaders6.9 Challenges from Moving Leaders to InnovatorsReferences
7 The Innovators
7.1 Introduction7.2 Some Characteristics of Innovators7.3 Hot Topics of Innovators7.3.1 ChatGPT7.3.2 TinyML7.3.3 Geospatial Machine-Learning Satellite7.3.3.1 Finally, the New Sensor Suites Are Special Purpose7.3.4 Flexible, Scalable, Adaptable Architectures for Data, AI, People7.4 Simulation and Synthetic Data7.4.1 Simulation7.4.2 Synthetic Data7.5 Causal Inference7.5.1 Predictive Models vs Prescriptive Models—They Are Not the Same!7.6 Healthcare Innovators7.6.1 Genetic Diagnostics7.6.2 Integrated Genetic-Phenotypic Diagnostics7.6.3 Drug Discovery and Development7.6.4 RNA Therapies7.6.5 Infectious Disease Profiling, Epidemiology, and Therapeutics7.6.6 Agricultural Biology7.6.7 Conservation7.6.7.1 Medical Innovators Using AI and Sound for Better Care and Outcomes7.6.7.2 Artificial Intelligence Could Soon Diagnose Illness Based on the Sound of Your Voice7.6.7.3 Large Academic Medical Center in Texas Uses Simulation for Outcomes7.7 Manufacturing Innovators7.8 Retail Innovators7.8.1 Large US Retailer Improves the Customer Experience with AI7.9 Energy Innovators7.9.1 Predictive Maintenance7.9.2 Optimization of Energy Generation and Distribution7.9.3 Data Analysis and Visualization7.9.4 Cybersecurity7.9.5 ML Modeling Wind for More Efficient Energy Use7.10 Financial Services and Insurance Innovators7.11 Where Do Innovators Go from Here?
8 Data and Analytics Readiness
8.1 Introduction8.2 Sources of Failure8.3 Determining an Organization's Readiness to Execute with Data and Analytics8.3.1 Conducting a Data and Analytics Readiness Assessment8.3.1.1 The Framework Overview8.3.1.2 Goals of Meetings with Senior Leadership8.3.1.3 Meetings with Key Leaders8.3.1.4 Execute the Communication Process8.3.1.5 Conduct Interviews with Designated Leaders, Managers, and High-Priority Staff8.3.1.6 Provide a Written Questionnaire for Remaining Designated Staff8.3.1.7 Perform an Extensive Analysis of Findings8.3.1.8 Creation of a Written Report with Detailed Study Findings8.3.1.9 Present Findings to Leadership and Determine Next Steps8.3.1.10 Presentation to the Organization8.4 Redesigning an Organization to Execute with Data and Analytics8.4.1 Business Knowledge8.4.2 Data Knowledge8.4.3 AI and Analytics Knowledge8.4.4 Technology Stack—Architecture, Platforms, Systems8.4.5 Culture8.4.6 People8.5 ConclusionReferences
9 Additional Stories on the Data Front
9.1 Managing the End-to-End Process of Patient Experience—Two Case Studies by Carol Maginn9.1.1 Patient Case A9.1.2 Patient Case B9.1.3 Pricing Offers an Intuitively Simple Problem That Provides Use of the Most Complicated Mathematical Models9.2 Textual Extraction, Transformation, and Learning9.2.1 Some Sources of Textual Data9.2.1.1 Voice Recordings9.2.1.2 Printed Text9.2.1.3 Internet and Social Media9.2.1.4 Email9.2.1.5 Electronic Texts as a Source9.2.1.6 What Is a Taxonomy?9.2.1.7 What Is an Ontology?9.2.1.8 Language and Taxonomies9.2.1.9 Textual ETL and Taxonomies9.2.2 Two Examples of Analysis Types9.2.3 Correlative Analysis—An Introduction9.2.4 Sentiment Analysis—An Introduction9.2.5 The Semantic Layer9.3 Less Tech Is Often Better than More Tech in Pricing Solutions9.4 Data + Planning Is the Key to COVID Vaccine Center9.5 Data Science Provider-Partner Relationships Greatly More Productive than Adversarial9.6 A Data Day in the Life of a Software Development Manager9.7 A Digital Marketing “Grinder” Knows There is Value in Analytics9.8 Taming Unstructured and Semi-Structured Information9.9 Data Sharing Drives Citizen Engagement9.10 The Onion Peel, Everyone Uses Data9.11 Constraints of Federal Regulations Tie States Ability to Provide Important Services9.12 Oncology Decisions Based on More than Clinical Factors—Quality of Life Matters9.13 The Greatest Successes in Analytics Are Often the Simplest9.14 Three Simple Truths with a Seasoned Actuary and Data Scientist9.14.1 As Posted by Scott (Author) on Linkedln, November 21, 20229.15 Once Again, the Most Sophisticated or Complex Models Do Not Always Win9.16 One Success a Pattern Does Not Make; AI and Analytics Are Not Plug and Play!9.16.1 One Success Does Not Ensure Future Success9.17 Does the Data Support the Allegations?9.18 Precision Analytics and the Internet of Things (IoT) Bolster Life Science and Lifesaving Products9.19 Using Data and Evidence to Improve Federal Programs9.20 Healthcare Leaders Save Face at the Expense of Data Accuracy9.21 Healthcare Data Requires Subject Matter Expertise, Not Just Technical Skills9.22 Work Satisfaction, Improving Lives9.23 National Restaurant Change Uses Data for Planning Initiatives9.24 Global Chip Shortage May Encourage Cloud Migration, but the Cloud Is Not Impervious to Interruption9.25 Do Not Trust Data Just Because It Comes from a Web App or Browser9.26 The Value of Domain Knowledge Is Critical to AI Success!9.27 We Want to Minimize the Amount of Data Collected9.27.1 Interesting Government Websites Related to Data and Analytics9.28 AI Demand Must Scale to Costs, Entrepreneurial Lessons9.29 Human Out of the Loop Analytics!—A Major, Uncomfortable Step9.30 The Last Mile of AI and Analytics9.31 Public Data Enables Anyone to Be a Real Estate Expert9.32 AI and Analytics Should Not Be Done in Isolation—Sepsis Failure9.33 Today's MLOps Platforms Are Much More Powerful9.34 Executive Has an Epiphany in 2011 on Compute, the Cloud, and Open SourceReferences
10 Wrapping Up
10.1 What a Ride—In the End, It Is All AnalyticsReferences
Index

Content preview from It's All Analytics, Part III

***Chapter 5*The Practitioners**

DOI: 10.4324/9780429343971-5

5.1 Introduction

In this chapter we discuss experiences, AI and analytics use cases, and lessons learned from practitioners.

Practitioners are in the center stage of the AI and analytics maturity continuum. They have an established track record and an overall positive return on investment for their programs. They also have had several missteps or failures that have added to their knowledge and thus gained the ability to overcome future setbacks.

We will cover detailed characteristics of practitioners in this chapter, ...

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Publisher Resources

ISBN: 9781000928440

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It's All Analytics, Part III

by Scott Burk, Gary Miner

***Chapter 5*The Practitioners**

5.1 Introduction

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.