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A Field Guide to Digital Transformation

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About

Features

  • Provides realistic, well-structured guidance to help students succeed, every step of the way
  • Offers complete coverage of risks, challenges, and pitfalls
  • Includes a from the ground up tutorial on the fundamental concepts underlying digital transformation
  • Co-authored by best-selling IT author Thomas Erl

Description

  • Copyright 2022
  • Pages: 288
  • Edition: 1st
  • eBook
  • ISBN-10: 0-13-757195-X
  • ISBN-13: 978-0-13-757195-6

Your Complete Guide to Digital Transformation 

A Field Guide to Digital Transformation is the definitive book on digital transformation. Top-selling IT author Thomas Erl and long-time practitioner Roger Stoffers combine to provide comprehensive, yet easy-to-understand coverage of essential digital transformation concepts, practices, and technologies in the format of a plain-English tutorial written for any IT professionals, students, or decision-makers. 

With more than 160 diagrams, this guide provides a highly visual exploration of what digital transformation is, how it works, and the techniques and technologies required to successfully build modern-day digital transformation solutions.

Learn from the experts and:

  • Discover what digital transformation is, why it emerged and when to apply it
  • Identify the significant business benefits that successful digital transformations can deliver and how to turn your organization into a disruptive force
  • Prepare for and overcome the common challenges associated with digital transformation initiatives
  • Understand the data-driven nature of digital transformation solutions and how they use and continually accumulate data intelligence
  • Understand how digital transformation solutions can utilize AI technology for intelligent automated decision-making
  • Gain insight into customer-centricity and how its practices are applied as part of digital transformations
  • Explore key digital transformation automation technologies, such as Robotic Process Automation (RPA), Internet of Things (IoT), Blockchain. and Cloud Computing
  • Explore key digital transformation data science technologies, such as Artificial Intelligence (AI), Machine Learning, and Big Data Analysis and Analytics


The book concludes with a uniquely detailed and highly visual real-world business scenario that provides step-by-step insights into how a digital transformation solution works, how it utilizes data intelligence to improve customer relationship building, and how it collects new data intelligence in support of enhancing future business capabilities.

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Table of Contents

About This Book     xxvii
PART I: DIGITAL TRANSFORMATION FUNDAMENTALS
Chapter 1: Understanding Digital Transformation     3
(What is Digital Transformation?)     3
Business, Technology, Data and People     5
    Digital Transformation and Business     6
    Digital Transformation and Technology     7
    Digital Transformation and Data     9
    Digital Transformation and People     10
    Digital Transformation and Organizations and Solutions     11
Chapter 2: Common Business Drivers     13
(What Led to Digital Transformation?)     13
Losing Touch with Customer Communities     14
Inability to Grow in Stale Marketplaces     16
Inability to Adapt to Rapidly Changing Marketplaces     16
Cold Customer Relationships     19
Inefficient Operations     19
Inefficient Decision-Making     21
Chapter 3: Common Technology Drivers     23
(What Enables Digital Transformation?)     23
Enhanced and Diverse Data Collection     25
Contemporary Data Science     27
Sophisticated Automation Technology     29
Autonomous Decision-Making     29
Centralized, Scalable, Resilient IT Resources     31
Immutable Data Storage     33
Ubiquitous Multiexperience Access     34
Chapter 4: Common Benefits and Goals     37
(Why Undergo a Digital Transformation?)     37
Enhanced Business Alignment     39
Enhanced Automation and Productivity     42
Enhanced Data Intelligence and Decision-Making     44
Improved Customer Experience and Customer Confidence     44
Improved Organizational Agility     48
Improved Ability to Attain Market Growth     50
Chapter 5: Common Risks and Challenges     53
(What Are the Pitfalls?)     53
Poor Data Quality and Data Bias     55
Increased Quantity of Vulnerable Digital Data     55
Resistance to Digital Culture     58
Risk of Over-Automation     59
Difficult to Govern     61
Chapter 6: Realizing Customer-Centricity     63
What Is a Product?     64
What Is a Customer?     65
Product-Centric vs. Customer-Centric Relationships     67
Transaction-Value vs. Relationship-Value Actions     69
Customer-Facing vs. Customer-Oriented Actions     71
Relationship Value and Warmth     71
    Warmth in Communication     71
    Warmth in Proactive Accommodation     74
    Warmth in Customer Rewards     76
    Warmth in Exceeding Customer Expectations     76
Single vs. Multi vs. Omni-Channel Customer Interactions     77
Customer Journeys     81
Customer Data Intelligence     84
Chapter 7: Data Intelligence Basics     89
Data Origins (Where Does the Data Come From?)     90
    Corporate Data     92
    Third-Party Data     92
    Creating New Corporate Data Intelligence     92
Common Data Sources (Who Produces the Data?)     93
    Operations Data     95
    Customer Data     95
    Social Media Data     95
    Public Sector Data     96
    Private Sector Data     97
Data Collection Methods (How Is the Data Collected?)     97
    Manual Data Entry     98
    Automated Data Entry or Collection     98
    Telemetry Data Capture     98
    Digitization     99
    Data Ingress     101
Data Utilization Types (How Is the Data Used?)     101
    Analysis and Reporting     101
    Automated Decision-Making     102
    Solution Input     103
    Bot-Driven Automation     103
    Model Training and Retraining     103
    Historical Record Keeping     104
Chapter 8: Intelligent Decision-Making     105
Manual Decision-Making     107
    Computer-Assisted Manual Decision-Making     107
Conditional Automated Decision-Making     108
Intelligent Manual Decision-Making     109
Intelligent Automated Decision-Making     112
    Direct-Driven Automated Decision-Making     113
    Periodic Automated Decision-Making     114
    Realtime Automated Decision-Making     115
Intelligent Manual vs. Intelligent Automated Decision-Making     115
PART II: DIGITAL TRANSFORMATION IN PRACTICE
Chapter 9: Understanding Digital Transformation Solutions     121
Distributed Solution Design Basics     122
Data Ingress Basics     127
    File Pull     127
    File Push     128
    API Pull     129
    API Push     129
    Data Streaming     130
Common Digital Transformation Technologies     132
Chapter 10: An Introduction to Digital Transformation Automation Technologies     135
Cloud Computing     137
    Cloud Computing in Practice     138
    Common Risks and Challenges     143
Blockchain     144
    Blockchain in Practice     145
        Partial Business Data Capture     147
        Full Business Data Capture     148
        Log Data Access Capture     150
        Partial Business Data Store     151
        Ledger Export     152
    Common Risks and Challenges     153
Internet of Things (IoT)     154
    IoT Devices     154
    IoT in Practice     160
    Common Risks and Challenges     163
Robotic Process Automation (RPA)     164
    RPA in Practice     165
    Common Risks and Challenges     168
Chapter 11: An Introduction to Digital Transformation Data Science Technologies     171
Big Data Analysis and Analytics     172
    The Five V's of Big Data     175
    Big Data in Practice     177
    Common Risks and Challenges     178
Machine Learning     179
    Model Training     180
    Machine Learning in Practice     180
    Common Risks and Challenges     184
Artificial Intelligence (AI)     186
    Neural Networks     186
    Automated Decision-Making     187
    AI in Practice     189
    Common Risks and Challenges     189
Chapter 12: Inside a Customer-Centric Solution     193
Scenario Background     195
    Business Challenges     195
    The Original Customer Journey     196
    Business Objectives     201
Terminology Recap     201
    Key Terms from Chapter 6: Realizing Customer-Centricity     202
    Key Terms from Chapter 7: Data Intelligence Basics     202
    Key Terms from Chapter 8: Intelligent Decision-Making     203
    Key Terms from Chapter 9: Understanding Digital Transformation Solutions     203
    Key Terms from Chapter 10: An Introduction to Digital Transformation Automation Technologies     204
    Key Terms from Chapter 11: An Introduction to Digital Transformation Data Science Technologies     204
The Enhanced Customer Journey     204
    Supporting Data Sources     205
    Step-by-Step Business Process     206
Future Decision-Making     241
About the Authors     243
Index     245

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