Data Science Strategy For Dummies

Data Science Strategy For Dummies

by Ulrika Jägare
Released July 2019
Publisher(s): For Dummies
ISBN: 9781119566250

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Book description

All the answers to your data science questions

Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the “what” and the “why” of data science and covering what it takes to lead and nurture a top-notch team of data scientists.

With this book, you’ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data.

  • Learn exactly what data science is and why it’s important
  • Adopt a data-driven mindset as the foundation to success
  • Understand the processes and common roadblocks behind data science
  • Keep your data science program focused on generating business value
  • Nurture a top-quality data science team

In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.

Table of contents

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

Product information

  • Title: Data Science Strategy For Dummies
  • Author(s): Ulrika Jägare
  • Release date: July 2019
  • Publisher(s): For Dummies
  • ISBN: 9781119566250