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• Cover
• Foreword
• Introduction
• About This Book
• Foolish Assumptions
• How This Book Is Organized
• Icons Used In This Book
• Beyond The Book
• Where To Go From Here
• Part 1: Optimizing Your Data Science Investment
• Chapter 1: Framing Data Science Strategy
• Establishing the Data Science Narrative
• Sorting Out the Concept of a Data-driven Organization
• Sorting Out the Concept of Machine Learning
• Defining and Scoping a Data Science Strategy
• Chapter 2: Considering the Inherent Complexity in Data Science
• Diagnosing Complexity in Data Science
• Recognizing Complexity as a Potential
• Enrolling in Data Science Pitfalls 101
• `Navigating the Complexity
• Chapter 3: Dealing with Difficult Challenges
• Getting Data from There to Here
• Managing Data Consistency Across the Data Science Environment
• Securing Explainability in AI
• Dealing with the Difference between Machine Learning and Traditional Software Programming
• Managing the Rapid AI Technology Evolution and Lack of Standardization
• Chapter 4: Managing Change in Data Science
• Understanding Change Management in Data Science
• Approaching Change in Data Science
• Recognizing what to avoid when driving change in data science
• Using Data Science Techniques to Drive Successful Change
• Getting Started
• Part 2: Making Strategic Choices for Your Data
• Chapter 5: Understanding the Past, Present, and Future of Data
• Sorting Out the Basics of Data
• Exploring Current Trends in Data
• Elaborating on Some Future Scenarios
• Chapter 6: Knowing Your Data
• Selecting Your Data
• Describing Data
• Exploring Data
• Assessing Data Quality
• Improving Data Quality
• Chapter 7: Considering the Ethical Aspects of Data Science
• Explaining AI Ethics
• Addressing trustworthy artificial intelligence
• Introducing Ethics by Design
• Chapter 8: Becoming Data-driven
• Understanding Why Data-Driven Is a Must
• Transitioning to a Data-Driven Model
• Developing a Data Strategy
• Establishing a Data-Driven Culture and Mindset
• Chapter 9: Evolving from Data-driven to Machine-driven
• Digitizing the Data
• Applying a Data-driven Approach
• Automating Workflows
• Introducing AI/ML capabilities
• Part 3: Building a Successful Data Science Organization
• Chapter 10: Building Successful Data Science Teams
• Starting with the Data Science Team Leader
• Defining the Prerequisites for a Successful Team
• Building the Team
• Connecting the Team to the Business Purpose
• Chapter 11: Approaching a Data Science Organizational Setup
• Finding the Right Organizational Design
• Applying a Common Data Science Function
• Chapter 12: Positioning the Role of the Chief Data Officer (CDO)
• Scoping the Role of the Chief Data Officer (CDO)
• Explaining Why a Chief Data Officer Is Needed
• Establishing the CDO Role
• The Future of the CDO Role
• Chapter 13: Acquiring Resources and Competencies
• Identifying the Roles in a Data Science Team
• Seeing What Makes a Great Data Scientist
• Structuring a Data Science Team
• Retaining Competence in Data Science
• Part 4: Investing in the Right Infrastructure
• Chapter 14: Developing a Data Architecture
• Defining What Makes Up a Data Architecture
• Exploring the Characteristics of a Modern Data Architecture
• Explaining Data Architecture Layers
• Listing the Essential Technologies for a Modern Data Architecture
• Creating a Modern Data Architecture
• Chapter 15: Focusing Data Governance on the Right Aspects
• Sorting Out Data Governance
• Explaining Why Data Governance is Needed
• Establishing Data Stewardship to Enforce Data Governance Rules
• Implementing a Structured Approach to Data Governance
• Chapter 16: Managing Models During Development and Production
• Unfolding the Fundamentals of Model Management
• Implementing Model Management
• Chapter 17: Exploring the Importance of Open Source
• Exploring the Role of Open Source
• Describing the Context of Data Science Programming Languages
• Unfolding Open Source Frameworks for AI/ML Models
• Choosing Open Source or Not?
• Chapter 18: Realizing the Infrastructure
• Approaching Infrastructure Realization
• Listing Key Infrastructure Considerations for AI and ML Support
• Automating Workflows in Your Data Infrastructure
• Enabling an Efficient Workspace for Data Engineers and Data Scientists
• Part 5: Data as a Business
• Chapter 19: Investing in Data as a Business
• Exploring How to Monetize Data
• Looking to the Future of the Data Economy
• Chapter 20: Using Data for Insights or Commercial Opportunities
• Focusing Your Data Science Investment
• Determining the Drivers for Internal Business Insights
• Using Data for Commercial Opportunities
• Balancing Strategic Objectives
• Chapter 21: Engaging Differently with Your Customers
• Understanding Your Customers
• Keeping Your Customers Happy
• Serving Customers More Efficiently
• Chapter 22: Introducing Data-driven Business Models
• Defining Business Models
• Exploring Data-driven Business Models
• Using a Framework for Data-driven Business Models
• Chapter 23: Handling New Delivery Models
• Defining Delivery Models for Data Products and Services
• Understanding and Adapting to New Delivery Models
• Introducing New Ways to Deliver Data Products
• Part 6: The Part of Tens
• Chapter 24: Ten Reasons to Develop a Data Science Strategy
• Expanding Your View on Data Science
• Aligning the Company View
• Creating a Solid Base for Execution
• Realizing Priorities Early
• Putting the Objective into Perspective
• Creating an Excellent Base for Communication
• Understanding Why Choices Matter
• Identifying the Risks Early
• Thoroughly Considering Your Data Need
• Understanding the Change Impact
• Chapter 25: Ten Mistakes to Avoid When Investing in Data Science
• Don’t Tolerate Top Management’s Ignorance of Data Science
• Don’t Believe That AI Is Magic
• Don’t Approach Data Science as a Race to the Death between Man and Machine
• Don’t Underestimate the Potential of AI
• Don’t Underestimate the Needed Data Science Skill Set
• Don’t Think That a Dashboard Is the End Objective
• Don’t Forget about the Ethical Aspects of AI
• Don’t Forget to Consider the Legal Rights to the Data
• Don’t Ignore the Scale of Change Needed
• Don’t Forget the Measurements Needed to Prove Value
• Index
• About the Author
• Connect with Dummies
• End User License Agreement