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Data Science Strategy For Dummies

Ulrika Jagare

ISBN: 9788126533367

364 pages

INR 699

Description

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.

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  
  • 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

 

Index