Data Science Strategy For Dummies

Name: Data Science Strategy For Dummies
Brand: Wiley
Price: 22.99 EUR
Availability: OnlineOnly

Ulrika Jägare(Autor*in)

Wiley (Verlag)

1. Auflage

Erschienen am 10. Juni 2019

352 Seiten

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978-1-119-56626-7 (ISBN)

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Beschreibung

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Inhalt

Intro
Title Page
Copyright Page
Table of Contents
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 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
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
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