It’s All Analytics – Part II: Designing an Integrated AI, Analytics, and Data Science Architecture for Your Organization
- Length: 296 pages
- Edition: 1
- Language: English
- Publisher: Productivity Press
- Publication Date: 2021-09-29
- ISBN-10: 0367359715
- ISBN-13: 9780367359713
- Sales Rank: #9121806 (See Top 100 Books)
Up to 70% and even more of corporate Analytics Efforts fail!!! Even after these corporations have made very large investments, in time, talent, and money, in developing what they thought were good data and analytics programs. Why? Because the executives and decision makers and the entire analytics team have not considered the most important aspect of making these analytics efforts successful. In this Book II of It’s All Analytics! series, we describe two primary things: 1) What this most important aspect consists of, and 2) How to get this most important aspect at the center of the analytics effort and thus make your analytics program successful.
This Book II in the series is divided into three main parts:
Part I,
Organizational Design for Success, discusses
……. The need for a complete company / organizational Alignment of the entire company and its analytics team for making its analytics successful. This means attention to the culture – the company culture culture!!! To be successful, the CEO’s and Decision Makers of a company / organization must be fully cognizant of the cultural focus on ‘establishing a center of excellence in analytics’. Simply, culture – company culture is the most important aspect of a successful analytics program. The focus must be on innovation, as this is needed by the analytics team to develop successful algorithms that will lead to greater company efficiency and increased profits.
Part II,
Data Design for Success, discusses
….. Data is the cornerstone of success with analytics. You can have the best analytics algorithms and models available, but if you do not have good data, efforts will at best be mediocre if not a complete failure. This Part II also goes further into data with descriptions of things like Volatile Data Memory Storage and Non-Volatile Data Memory Storage, in addition to things like data structures and data formats, plus considering things like Cluster Computing, Data Swamps, Muddy Data, Data Marts, Enterprise Data Warehouse, Data Reservoirs, and Analytic Sandboxes, and additionally Data Virtualization, Curated Data, Purchased Data, Nascent & Future Data, Supplemental Data, Meaningful Data, GIS (Geographic Information Systems) & Geo Analytics Data, Graph Databases, and Time Series Databases. Part II also considers Data Governance including Data Integrity, Data Security, Data Consistency, Data Confidence, Data Leakage, Data Distribution, and Data Literacy.
Part III, Analytics Technology Design for Success, discusses …. Analytics Maturity and aspects of this maturity, like Exploratory Data Analysis, Data Preparation, Feature Engineering, Building Models, Model Evaluation, Model Selection, and Model Deployment. Part III also goes into the nuts and bolts of modern predictive analytics, discussing such terms as AI = Artificial Intelligence, Machine Learning, Deep Learning, and the more traditional aspects of analytics that feed into modern analytics like Statistics, Forecasting, Optimization, and Simulation. Part III also goes into how to Communicate and Act upon Analytics, which includes building a successful Analytics Culture within your company / organization.
All-in-all, if your company or organization needs to be successful using analytics, this book will give you the basics of what you need to know to make it happen.
Cover Half Title Title Page Copyright Page Dedication Table of Contents Foreword and Tribute to the Authors Preface Authors SECTION I: DESIGNING FOR ORGANIZATIONAL SUCCESS 1 Some Say It Starts with Data—It Doesn’t Introduction Organizational Alignment Start with the End in Mind Remove the Cultural Divide and Establish a Center of Excellence Innovation-Oriented Cultures CoE Team Structure Full Service Team Members Functionally Oriented Team Members Data and Analytic Project Team Roles Data and Analytics Literacy What Is Data Literacy? Data Literacy vs Analytics Literacy Designing the Organization for Program Success Analytics Success Involves More than Technology People and Process – Not Merely Technology Ethics Governance Technology Data and Analytics Platform Service Areas Data and Analytics Architecture Summary References Additional Resources 2 The Anatomy of a Business Decision The Anatomy of a Business Decision What Is a Business Decision? The Value of a Decision Which Uses Data and Analytics? Before Analytics After Analytics Types of Decisions Strategic Decisions Tactical Decisions Operational Decisions Human vs. Automated Decisions Speed Is Everything Well Why Does It Matter? Summary References 3 Trustworthy AI Introduction Don’t Be Creepy – Be Fair, Unbiased, Explainable, and Transparent Creepiness Fairness and Bias Explainable and Transparent Ethics Framework for Trustworthy Analytics Ethical Foundations for Trustworthy AI Key Requirements for Trustworthy AI Other AI Ethical Frameworks Summary References Additional Resources SECTION II: DESIGNING FOR DATA SUCCESS 4 Data Design for Success Introduction Why Is Data So Important? Data Is the Cornerstone of Improvement Processes Are Everywhere The Problem – Issues with Data Continue to Persist Firms Are Failing to Be Data Driven Data and Analytics Explosion On a Personal Note The Potential of Data = Analytics Framework for Data and Analytics – Some Fundamentals The Typical Story of Data Growth, Data Complexity, and Data Needs Data Volume Data Variety Data Velocity Data Value Data Veracity The Pieces Are Interdependent and Circular – Keep Looking Forward for Next Generation Data The Value of Data and Analytics Data and Analytics Literacy Are Requirements to Successful Programs Summary How This Part Is Organized References Additional Resources Data and Analytics Literacy References Additional Terms Related to This Chapter Process and Data Quality References 5 Data in Motion, Data Pipes, APIs, Microservices, Streaming, Events, and More Introduction APIs and Microservices The Five Architectural Constraints of REST APIs Other APIs – RPC and SOAP API Benefits and Drawbacks Benefits (Primarily to Developers) Drawbacks Microservices Microservice Benefits and Drawbacks Benefits Drawbacks Events, Event-Driven Architectures and Streaming Some Drivers and Examples of Events, Streaming Events, and CEP (Complex Event Processing) IoT Is a Big Driver of Real-Time Events Event Processing Advantages How Businesses Benefit from Event Processing Improved Customer Service Reduction of Costs and More Efficient Use of Resources Optimized Operations ETL and ELT Summary References Additional Resources Basic Terms Useful in This Chapter Additional Relevant Terms 6 Data Stores, Warehouses, Big Data, Lakes, and Cloud Data Introduction Why Data Is so Crucial to the Success of an Enterprise Data Storage – Two Designations – Volatile and Nonvolatile Memory Primer on Data Structures and Formats Data Stores Topology Local File Systems and Network Data Storage Operational Data Stores Data Marts and the EDWs Benefits and Drawbacks of the EDW Benefits of an EDW Drawbacks of an EDW Cluster Computing and Big Data What Is Big Data? Big Data as a Concept Big Data as a Technology Why the Push to Big Data? Why Is Big Data Technology Attractive for Data Science? Pivotal Changes in Big Data Technology Optimized Big Data Cloud Data – What It Is, What You Can Do, Benefits, and Drawbacks Cloud Benefits and Drawbacks Cloud Storage “Other Big Data Promises”, Data Lakes, Data Swamps, Reservoirs, Muddy Water, Analytic Sandboxes, and Whatever We Can Think to Call It Tomorrow Summary References Additional Resources Data Lakes and Architecture Some Terms to Consider Exploring 7 Data Virtualization Introduction The Typical Story of Data Growth, Data Complexity, and Data Needs DV – What Is It? A Platform Connecting to Hundreds of Data Sources A Platform with Searchable Data and Rich Metadata A Collaboration Tool for Functional Areas and Users A Pathway for New Systems and System Migration An IT Tool for Rapid Prototyping A System for Enhanced Security of Data The Continuing Quest for the “Single Versions of the Truth” – Motivation beyond the EDW What Are the Advantages of DV? A Sustainable Architecture for the Ever-Increasing Complexity of Data Simplified User Experience More Collaborative and Productive User Experience Data in Near Real Time Source Data and Combine Data Easily No Need to Replicate and Make Physical Copies of Data Improved Security and Administration Positive Impact on the EDW, IT, and the Business Governance and Data Quality DV Is Scalable – Scales Up and Scales Out Enabling Future Data and Even Technology What Are the Drawbacks of DV? Some of the Major Disadvantages of DV Are You Ready for DV? Summary References Additional Resources 8 Data Governance and Data Management Introduction Data Governance – Policies, Procedure, and Process Goals of Data Governance Data Integrity Data Security Data Consistency Data Confidence Compliance to Regulations, Data Privacy Laws Adherence to Organizational Ethics and Standards Risk Management of Data Leakage Data Distribution Value of Good Data Moving Data Quality Upstream Reduces Costs Data Literacy Education Technology to Support Data Management and Governance Data Management Master Data Reference Data Data Quality Security Summary References Additional Resources Some Terms Related to This Chapter to Consider Exploring Data Quality Resource 9 Miscellanea – Curated, Purchased, Nascent, and Future Data Introduction Data Outside Your Organization Supplemental Data Meaningful Data Data for Free Publically Available Data Data Available from Commercial Entities and Universities Data for Sale Data Syndicators Data Brokers Data Exchange and Data Exchange Platforms Data Marketplaces Should You Monetize Your Data? Future Data Keep an Eye Out for Nascent Technologies and Trends in Applications of Analytics GIS and Geo Analytics Graph Databases Time Series Databases Today Is the Time to Start Collecting Data for the Future Data Strategy and Data Paradigms Summary References Additional Resources What Is DataOps? SECTION III: DESIGNING FOR ANALYTICS SUCCESS 10 Technology to Create Analytics Introduction Analytics Maturity Architectural Considerations for the Data Scientist Data Discovery and Acquisition Exploratory Data Analysis Data Preparation Feature Engineering Model Build and Selection Model Evaluation and Testing Model Deployment Model Monitoring Legality and Ethical Use of Data Automation and ML The Real World is Different than University Do You Know How to Bake Bread? Analytical Capabilities and Architectural Considerations Data Management as a Prerequisite Starting with the Data Starting with the Analytics Data and Analytics Architecture Data Sources Data Management Analytics Model Building Reporting and Dashboards Data Science AI, ML, Deep Learning – Oh My! Model Training Model Inference Model Management Governance Streaming Analytics IoT and Edge Analytics Cloud Ecosystems and Frameworks A Few Example Architectures Uber Facebook An Evolution of CRISP-DM Feature Stores Technology Cost Considerations Other Open Source Considerations Technical Debt in Data Science and ML Model Dependencies Data Dependencies Feedback Anti-patterns or Poor Coding Habits Summary References Additional Resources 11 Technology to Communicate and Act Upon Analytics Introduction An Analytics Confluence Data Storytelling Building an Analytics Culture Model Ops How Is Analytics Different? Why Does an Organization Need Model Ops? Model Ops Capabilities Model Visibility Model Repository Model Performance Metrics Contextualized Collaboration Framework Governance Summary References Additional Resources Keywords 12 To Build, Buy, or Outsource Analytics Platform Introduction Analytics Infrastructure Components What Really Matters (In Your Business)? Build vs. Buy Considerations Strategy and Competitive Advantage Costs Scale and Complexity Commoditization, Flexibility, and Change Time In-House Expertise Risks Support Structure Operational Factors Intellectual Property Outsourcing Build vs. Buy vs. Outsource Guidelines Summary References Additional Resources Index
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