Data Science Strategy For Dummies

Ulrika Jagare

ISBN: 9788126533367

364 pages

INR 699


Data Science Strategy For Dummies begins by explaining what exactly data science is and why it's important. While Using non-technical language, it covers mindsets, organizational players, processes and common roadblocks, all the while keeping a razor focus on business value and the nurturing of a top quality data science team.



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  
  • Capture
  • Maintain  
  • Process  
  • Analyze  
  • Communicate
  • Actuate
  • Sorting Out the Concept of a Data-driven Organization
  • Approaching data-driven
  • Being data obsessed
  • Sorting Out the Concept of Machine Learning
  • Defining and Scoping a Data Science Strategy  
  • Objectives
  • Approach  
  • Choices  
  • Data  
  • Legal  
  • Ethics  
  • Competence
  • Infrastructure  
  • Governance and security
  • Commercial/business models
  • Measurements


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  
  • Believing that all data is needed  
  • Thinking that investing in a data lake will solve all your problems
  • Focusing on AI when analytics is enough
  • Believing in the 1-tool approach  
  • Investing only in certain areas
  • Leveraging the infrastructure for reporting rather than exploration
  • Underestimating the need for skilled data scientists
  • `Navigating the Complexity


Chapter 3: Dealing with Difficult Challenges

  • Getting Data from There to Here  
  • Handling dependencies on data owned by others
  • Managing data transfer and computation across-country borders
  • Managing Data Consistency Across the Data Science Environment  
  • Securing Explain ability 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
  • Using digital engagement tools
  • Applying social media analytics to identify stakeholder sentiment
  • Capturing reference data in change projects
  • Using data to select people for change roles
  • Automating change metrics
  • 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
  • Explaining traditional data versus big data
  • Knowing the value of data
  • Exploring Current Trends in Data
  • Data monetization
  • Responsible AI
  • Cloud-based data architectures
  • Computation and intelligence in the edge
  • Digital twins
  • Blockchain
  • Conversational platforms
  • Elaborating on Some Future Scenarios
  • Standardization for data science productivity
  • From data monetization scenarios to a data economy
  • An explosion of human/machine hybrid systems
  • Quantum computing will solve the unsolvable problems


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
  • Securing management buy-in and assigning a chief data officer (CDO)
  • Identifying the key business value aligned with the business maturity
  • Developing a Data Strategy
  • Caring for your data
  • Democratizing the data
  • Driving data standardization
  • Structuring the 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
  • Adopting different leadership approaches
  • Approaching data science leadership
  • Finding the right data science leader or manager
  • Defining the Prerequisites for a Successful Team
  • Developing a team structure
  • Establishing an infrastructure
  • Ensuring data availability
  • Insisting on interesting projects
  • Promoting continuous learning
  • Encouraging research studies
  • Building the Team
  • Developing smart hiring processes
  • Letting your teams evolve organically  
  • Connecting the Team to the Business Purpose


Chapter 11: Approaching a Data Science Organizational Setup

  • Finding the Right Organizational Design
  • Designing the data science function
  • Evaluating the benefits of a center of excellence for data science
  • Identifying success factors for a data science center of excellence  
  • Applying a Common Data Science Function
  • Selecting a location
  • Approaching ways of working
  • Managing expectations
  • Selecting an execution approach


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
  • Data scientist
  • Data engineer
  • Machine learning engineer
  • Data architect
  • Business analyst
  • Software engineer
  • Domain expert
  • Seeing What Makes a Great Data Scientist
  • Structuring a Data Science Team
  • Hiring and evaluating the data science talent you need
  • Retaining Competence in Data Science
  • Understanding what makes a data scientist leave


Part 4: Investing in the Right Infrastructure

Chapter 14: Developing a Data Architecture

  • Defining What Makes Up a Data Architecture
  • Describing traditional architectural approaches
  • Elements of a data architecture
  • Exploring the Characteristics of a Modern Data Architecture
  • Explaining Data Architecture Layers
  • Listing the Essential Technologies for a Modern Data Architecture
  • NoSQL databases
  • Real-time streaming platforms
  • Docker and containers
  • Container repositories  
  • Container orchestration  
  • Microservices
  • Function as a service
  • Creating a Modern Data Architecture


Chapter 15: Focusing Data Governance on the Right Aspects

  • Sorting Out Data Governance
  • Data governance for defense or offense
  • Objectives for data governance
  • Explaining Why Data Governance is Needed
  • Data governance saves money
  • Bad data governance is dangerous
  • Good data governance provides clarity
  • 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
  • Working with many models
  • Making the case for efficient model management
  • Implementing Model Management
  • Pinpointing implementation challenges
  • Managing model risk
  • Measuring the risk level
  • Identifying suitable control mechanisms


Chapter 17: Exploring the Importance of Open Source

  • Exploring the Role of Open Source
  • Understanding the importance of open source in smaller companies
  • Understanding the trend
  • Describing the Context of Data Science Programming Languages
  • Unfolding Open Source Frameworks for AI/ML Models
  • TensorFlow
  • Theano
  • Torch
  • Caffe and Caffe2
  • The Microsoft Cognitive Toolkit (previously known as Microsoft CNTK)
  • Keras
  • Scikit-learn
  • Spark MLlib
  • Azure ML Studio
  • Amazon Machine Learning
  • Choosing Open Source or Not?


Chapter 18: Realizing the Infrastructure

  • Approaching Infrastructure Realization
  • Listing Key Infrastructure Considerations for AI and ML Support
  • Location
  • Capacity
  • Data center setup
  • End-to-end management
  • Network infrastructure
  • Security and ethics
  • Advisory and supporting services
  • Ecosystem fit
  • 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
  • Approaching data monetization is about treating data as an asset
  • Data monetization in a data economy
  • 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
  • Recognizing data science categories for practical implementation
  • Applying data-science-driven internal business insights
  • Using Data for Commercial Opportunities
  • Defining a data product
  • Distinguishing between categories of data products
  • Balancing Strategic Objectives


Chapter 21: Engaging Differently with Your Customers

  • Understanding Your Customers
  • Step 1: Engage your customers
  • Step 2: Identify what drives your customers
  • Step 3: Apply analytics and machine learning to customer actions
  • Step 4: Predict and prepare for the next step
  • Step 5: Imagine your customer's future
  • Keeping Your Customers Happy
  • Serving Customers More Efficiently
  • Predicting demand
  • Automating tasks
  • Making company applications predictive


Chapter 22: Introducing Data-driven Business Models

  • Defining Business Models
  • Exploring Data-driven Business Models
  • Creating data-centric businesses
  • Investigating different types of data-driven business models
  • Using a Framework for Data-driven Business Models
  • Creating a data-driven business model using a framework
  • Key resources
  • Key activities
  • Offering/value proposition
  • Customer segment
  • Revenue model
  • Cost structure
  • Putting it all together


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
  • Self-service analytics environments as a delivery model
  • Applications, websites, and product/service interfaces as delivery models
  • Existing products and services
  • Downloadable files
  • APIs
  • Cloud services
  • Online market places
  • Downloadable licenses
  • Online services
  • Onsite services


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




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