It's All Analytics - Part II

It's All Analytics - Part II

by Scott Burk, David Sweenor, Gary Miner
Released September 2021
Publisher(s): Productivity Press
ISBN: 9781000433999

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially the last 25 years there has been an explosion of terms and methods born that automate and improve decision-making and operations.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Dedication
  5. Table of Contents
  6. Foreword and Tribute to the Authors
  7. Preface
  8. Authors
  9. Section I Designing for Organizational Success
    1. 1 Some Say It Starts with Data—It Doesn’t
      1. Introduction
      2. Organizational Alignment
        1. Start with the End in Mind
        2. Remove the Cultural Divide and Establish a Center of Excellence
        3. Innovation-Oriented Cultures
        4. CoE Team Structure
          1. Full Service Team Members
          2. Functionally Oriented Team Members
        5. Data and Analytic Project Team Roles
      3. Data and Analytics Literacy
        1. What Is Data Literacy? Data Literacy vs Analytics Literacy
        2. Designing the Organization for Program Success
      4. Analytics Success Involves More than Technology
        1. People and Process – Not Merely Technology
        2. Ethics
        3. Governance
      5. Technology
        1. Data and Analytics Platform Service Areas
        2. Data and Analytics Architecture
      6. Summary
      7. References
      8. Additional Resources
    2. 2 The Anatomy of a Business Decision
      1. The Anatomy of a Business Decision
        1. What Is a Business Decision?
        2. The Value of a Decision Which Uses Data and Analytics?
          1. Before Analytics
          2. After Analytics
        3. Types of Decisions
          1. Strategic Decisions
          2. Tactical Decisions
          3. Operational Decisions
        4. Human vs. Automated Decisions
        5. Speed Is Everything
        6. Well Why Does It Matter?
      2. Summary
      3. References
    3. 3 Trustworthy AI
      1. Introduction
      2. Don’t Be Creepy – Be Fair, Unbiased, Explainable, and Transparent
        1. Creepiness
        2. Fairness and Bias
        3. Explainable and Transparent
      3. Ethics
      4. Framework for Trustworthy Analytics
        1. Ethical Foundations for Trustworthy AI
        2. Key Requirements for Trustworthy AI
      5. Other AI Ethical Frameworks
      6. Summary
      7. References
      8. Additional Resources
  10. Section II Designing for Data Success
    1. 4 Data Design for Success
      1. Introduction
      2. Why Is Data So Important?
        1. Data Is the Cornerstone of Improvement
        2. Processes Are Everywhere
        3. The Problem – Issues with Data Continue to Persist
        4. Firms Are Failing to Be Data Driven
        5. Data and Analytics Explosion
      3. On a Personal Note
      4. The Potential of Data = Analytics
      5. Framework for Data and Analytics – Some Fundamentals
        1. The Typical Story of Data Growth, Data Complexity, and Data Needs
          1. Data Volume
          2. Data Variety
          3. Data Velocity
          4. Data Value
          5. Data Veracity
        2. The Pieces Are Interdependent and Circular – Keep Looking Forward for Next Generation Data
      6. The Value of Data and Analytics
      7. Data and Analytics Literacy Are Requirements to Successful Programs
      8. Summary
        1. How This Part Is Organized
      9. References
      10. Additional Resources
        1. Data and Analytics Literacy References
        2. Additional Terms Related to This Chapter
        3. Process and Data Quality References
    2. 5 Data in Motion, Data Pipes, APIs, Microservices, Streaming, Events, and More
      1. Introduction
      2. APIs and Microservices
        1. The Five Architectural Constraints of REST APIs
        2. Other APIs – RPC and SOAP
        3. API Benefits and Drawbacks
          1. Benefits (Primarily to Developers)
          2. Drawbacks
      3. Microservices
        1. Microservice Benefits and Drawbacks
          1. Benefits
          2. Drawbacks
      4. Events, Event-Driven Architectures and Streaming
      5. Some Drivers and Examples of Events, Streaming Events, and CEP (Complex Event Processing)
        1. IoT Is a Big Driver of Real-Time Events
      6. Event Processing Advantages
        1. How Businesses Benefit from Event Processing
          1. Improved Customer Service
          2. Reduction of Costs and More Efficient Use of Resources
          3. Optimized Operations
      7. ETL and ELT
      8. Summary
      9. References
      10. Additional Resources
        1. Basic Terms Useful in This Chapter
        2. Additional Relevant Terms
    3. 6 Data Stores, Warehouses, Big Data, Lakes, and Cloud Data
      1. Introduction
      2. Why Data Is so Crucial to the Success of an Enterprise
      3. Data Storage – Two Designations – Volatile and Nonvolatile Memory
      4. Primer on Data Structures and Formats
      5. Data Stores Topology
        1. Local File Systems and Network Data Storage
        2. Operational Data Stores
        3. Data Marts and the EDWs
        4. Benefits and Drawbacks of the EDW
          1. Benefits of an EDW
          2. Drawbacks of an EDW
      6. Cluster Computing and Big Data
        1. What Is Big Data?
          1. Big Data as a Concept
        2. Big Data as a Technology
        3. Why the Push to Big Data? Why Is Big Data Technology Attractive for Data Science?
        4. Pivotal Changes in Big Data Technology
        5. Optimized Big Data
        6. Cloud Data – What It Is, What You Can Do, Benefits, and Drawbacks
        7. Cloud Benefits and Drawbacks
          1. Cloud Storage
        8. “Other Big Data Promises”, Data Lakes, Data Swamps, Reservoirs, Muddy Water, Analytic Sandboxes, and Whatever We Can Think to Call It Tomorrow
      7. Summary
      8. References
      9. Additional Resources
      10. Data Lakes and Architecture
        1. Some Terms to Consider Exploring
    4. 7 Data Virtualization
      1. Introduction
        1. The Typical Story of Data Growth, Data Complexity, and Data Needs
      2. DV – What Is It?
        1. A Platform Connecting to Hundreds of Data Sources
        2. A Platform with Searchable Data and Rich Metadata
        3. A Collaboration Tool for Functional Areas and Users
        4. A Pathway for New Systems and System Migration
        5. An IT Tool for Rapid Prototyping
        6. A System for Enhanced Security of Data
        7. The Continuing Quest for the “Single Versions of the Truth” – Motivation beyond the EDW
        8. What Are the Advantages of DV?
          1. A Sustainable Architecture for the Ever-Increasing Complexity of Data
          2. Simplified User Experience
          3. More Collaborative and Productive User Experience
          4. Data in Near Real Time
          5. Source Data and Combine Data Easily
          6. No Need to Replicate and Make Physical Copies of Data
          7. Improved Security and Administration
          8. Positive Impact on the EDW, IT, and the Business
          9. Governance and Data Quality
          10. DV Is Scalable – Scales Up and Scales Out
          11. Enabling Future Data and Even Technology
        9. What Are the Drawbacks of DV?
          1. Some of the Major Disadvantages of DV
        10. Are You Ready for DV?
      3. Summary
      4. References
      5. Additional Resources
    5. 8 Data Governance and Data Management
      1. Introduction
      2. Data Governance – Policies, Procedure, and Process
      3. Goals of Data Governance
        1. Data Integrity
        2. Data Security
        3. Data Consistency
        4. Data Confidence
        5. Compliance to Regulations, Data Privacy Laws
        6. Adherence to Organizational Ethics and Standards
        7. Risk Management of Data Leakage
        8. Data Distribution
        9. Value of Good Data
        10. Moving Data Quality Upstream Reduces Costs
        11. Data Literacy Education
      4. Technology to Support Data Management and Governance
        1. Data Management
        2. Master Data
        3. Reference Data
        4. Data Quality
        5. Security
      5. Summary
      6. References
      7. Additional Resources
        1. Some Terms Related to This Chapter to Consider Exploring
        2. Data Quality Resource
    6. 9 Miscellanea – Curated, Purchased, Nascent, and Future Data
      1. Introduction
      2. Data Outside Your Organization
        1. Supplemental Data
        2. Meaningful Data
      3. Data for Free
        1. Publically Available Data
      4. Data Available from Commercial Entities and Universities
      5. Data for Sale
        1. Data Syndicators
        2. Data Brokers
        3. Data Exchange and Data Exchange Platforms
        4. Data Marketplaces
      6. Should You Monetize Your Data?
      7. Future Data
        1. Keep an Eye Out for Nascent Technologies and Trends in Applications of Analytics
        2. GIS and Geo Analytics
        3. Graph Databases
        4. Time Series Databases
        5. Today Is the Time to Start Collecting Data for the Future
        6. Data Strategy and Data Paradigms
      8. Summary
      9. References
      10. Additional Resources
        1. What Is DataOps?
  11. Section III Designing for Analytics Success
    1. 10 Technology to Create Analytics
      1. Introduction
      2. Analytics Maturity
      3. Architectural Considerations for the Data Scientist
        1. Data Discovery and Acquisition
        2. Exploratory Data Analysis
        3. Data Preparation
        4. Feature Engineering
        5. Model Build and Selection
        6. Model Evaluation and Testing
        7. Model Deployment
        8. Model Monitoring
        9. Legality and Ethical Use of Data
      4. Automation and ML
      5. The Real World is Different than University
      6. Do You Know How to Bake Bread?
      7. Analytical Capabilities and Architectural Considerations
        1. Data Management as a Prerequisite
          1. Starting with the Data
          2. Starting with the Analytics
        2. Data and Analytics Architecture
          1. Data Sources
          2. Data Management
        3. Analytics
          1. Model Building
          2. Reporting and Dashboards
          3. Data Science
          4. AI, ML, Deep Learning – Oh My!
          5. Model Training
          6. Model Inference
          7. Model Management
          8. Governance
        4. Streaming Analytics
        5. IoT and Edge Analytics
        6. Cloud Ecosystems and Frameworks
      8. A Few Example Architectures
        1. Uber
        2. Facebook
        3. An Evolution of CRISP-DM
      9. Feature Stores
      10. Technology
      11. Cost Considerations
        1. Other Open Source Considerations
      12. Technical Debt in Data Science and ML
        1. Model Dependencies
        2. Data Dependencies
        3. Feedback
        4. Anti-patterns or Poor Coding Habits
      13. Summary
      14. References
      15. Additional Resources
    2. 11 Technology to Communicate and Act Upon Analytics
      1. Introduction
      2. An Analytics Confluence
      3. Data Storytelling
      4. Building an Analytics Culture
      5. Model Ops
        1. How Is Analytics Different?
        2. Why Does an Organization Need Model Ops?
        3. Model Ops Capabilities
          1. Model Visibility
          2. Model Repository
          3. Model Performance Metrics
          4. Contextualized Collaboration Framework
          5. Governance
      6. Summary
      7. References
      8. Additional Resources
      9. Keywords
    3. 12 To Build, Buy, or Outsource Analytics Platform
      1. Introduction
      2. Analytics Infrastructure Components
      3. What Really Matters (In Your Business)?
      4. Build vs. Buy Considerations
        1. Strategy and Competitive Advantage
        2. Costs
        3. Scale and Complexity
        4. Commoditization, Flexibility, and Change
        5. Time
        6. In-House Expertise
        7. Risks
        8. Support Structure
        9. Operational Factors
        10. Intellectual Property
      5. Outsourcing
      6. Build vs. Buy vs. Outsource Guidelines
      7. Summary
      8. References
      9. Additional Resources
  12. Index

Product information

  • Title: It's All Analytics - Part II
  • Author(s): Scott Burk, David Sweenor, Gary Miner
  • Release date: September 2021
  • Publisher(s): Productivity Press
  • ISBN: 9781000433999