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Field Guide to Digital Transformation, A, 1st edition

Published by Addison-Wesley Professional (December 6, 2021) © 2022

  • Thomas Erl
  • Roger Stoffers

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A Field Guide to Digital Transformation is a complete tutorial on digital transformation concepts, tools, technologies and practices, organized into a proven industry framework for adoption. Best-selling IT author Thomas Erl and Roger Stoffers offer end-to-end coverage of the full project lifecycle, walking readers through planning, defining, designing, building, and governing digital transformation solutions. The authors highlight common risks, pitfalls, and adoption considerations, sharing practical insights into the organizational, cultural, technological, and operational impacts associated with digital transformation initiatives.

Uniquely detailed and practical, this guide reflects the authors' deep experience driving value from digital transformation using technologies available right now.

  • 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
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

Thomas Erl is a best-selling IT author who has authored and co-authored 15 books published by Prentice Hall and Pearson Education and dedicated to topics focused on contemporary information technology and practices. These titles were delivered for the Pearson Digital Enterprise Series from Thomas Erl (formerly the Prentice Hall Service Technology Series from Thomas Erl) for which Thomas also acts as series editor.

As founder and president of Arcitura Education (www.arcitura.com), Thomas also leads the development of curricula for internationally recognized, vendor-neutral training and accreditation programs. Arcitura's portfolio currently consists of over 100 courses, over 90 Pearson VUE exams, and over 40 certification tracks, covering topics such as Digital Transformation, Robotic Process Automation (RPA), DevOps, Blockchain, IoT, Containerization, Machine Learning, Artificial Intelligence (AI), Cybersecurity, Service-Oriented Architecture (SOA), Cloud Computing and Big Data Analytics.


Roger Stoffers is a TOGAF-certified Enterprise Architect passionate about Digital Transformation and Integration. Roger has worked for (and with) organizations in Telecommunication, Government and Finance industries, building 25 years of experience in multinational organizations. He has served as principal and lead Enterprise Domain Architect for digital business transformation initiatives, with strong focus for customer relationships and sustainable organizational agility in distributed environments.


Presently Roger works as Enterprise Architect for de Volksbank, fostering Customer Centricity and the organization's Sustainability & Social Responsibility goals with Data-Driven Customer Engagement and Decisioning. Independently, Roger is a worldwide consulting architect for digital transformation and integration, and a senior trainer for (enterprise) architecture topics and contemporary vendor-neutral technology and next-generation IT topics. He is a speaker at conferences about Digital Transformation, SOA and Microservices and Enterprise Architecture. He is a contributor for the Service-Oriented Architecture: Analysis and Design for Service and Microservices book from the Pearson Digital Enterprise Series from Thomas Erl.

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