Table of Contents
Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
1. The Business Transformation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Why Businesses Are Transforming 1
Factor 1: The Speed of Change 2
Factor 2: The Convergence of Multiple Technologies 2
Factor 3: The Importance of Data 2
Factor 4: Changing Consumer Behavior and Customer Centricity 4
Industries Heavily Impacted by Digital Transformation 5
The Consequences for Your Business 7
There’s No Analytics Transformation Without Augmented Analytics 8
A Data-Driven Culture 9
The “People Problem” and the Limits of Upskilling 9
Conclusion 10
2. The Analytics Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Finding Your Analytics Purpose 12
Competition and Customer Expectations 12
Operational Efficiency 12
Availability and User Friendliness 12
Innovation 13
Regulatory Compliance 13
How to Start Your Analytics Journey 13
Industry Examples 14
Ecommerce 14
Healthcare 14
v
Manufacturing 14
Financial Services 14
Government 15
Commercial Insurance 15
The Concept of Analytical Maturity 16
Determine Your Current—and Future—Data Maturity 20
Stage 1: Data Reactive 20
Stage 2: Data Active 23
Stage 3: Data Progressive 31
Stage 4: Data Fluent 36
Conclusion 40
3. Understanding Augmented Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Definition 41
The Five I’s of Augmented Analytics 43
Overcoming the Limitations of Traditional Analytics Approaches 44
Augmented Workflows 45
The Benefits of Augmented Analytics 46
AA Gives Nonexpert Users a Better Experience 48
Automated Integration Provides More Complete Insights 49
AA Gives Faster, More Efficient Insights 49
Standardization Reduces Human Errors and Bias for Better Insights 50
AA Tools Are Easier to Scale Up 51
AA Reaches Further Afield to Generate Unexpected Insights 51
Overcoming Bias 51
Key Enablers of Augmented Analytics 55
Automation and AI 56
Artificial Intelligence: The Five Archetypes 57
The Limitations of Augmented Analytics 64
The Challenges of Augmented Analytics 66
Conclusion 67
4. Preparing People and the Organization for Augmented Analytics. . . . . . . . . . . . . . . . . . 69
Tailoring Augmented Analytics for Different Organizational Roles 70
Analytics Leader 71
Analytics Translator 73
Analytics User 74
Analytics Professional 78
Analytics Transformation Manager 82
Summary of Key Roles 84
vi | Table of Contents
The Center of Excellence 86
Creating a Center of Excellence 86
Approaches to Organizing a CoE 89
Driving Transformational Change with the Influence Model 94
Fostering Understanding and Conviction 95
Reinforcing with Formal Mechanisms 95
Developing Talent and Skills 96
Role Modeling 96
Cultivating a Data-Literate Culture 97
Cultivating Analytics Awareness 98
Storytelling with Data 99
Embracing Data-Driven Management 100
Leading in the Age of AI 100
The Enablement Program 101
Training Formats for Analytics Leaders 101
Training Formats for Analytics Translators 105
Data Literacy Training 107
Technical Training 107
Conclusion 108
5. Augmented Workflows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
Types of Workflow Augmentation 110
Fixed-Rule, High-Confidence Augmentation 110
Idea and Insight Enrichment 111
Conversational Augmentation 111
Contextual Augmentation 112
Collaborative Augmentation 112
The Analytics Use-Case Approach: Finding Workflows to Augment 112
Phase 1. Idea: The Initial Spark 114
Phase 2. Concept: Structuring the Idea 115
Phase 3. Proof of Concept: Testing the Waters 118
Phase 4. Prototyping: Shaping the Concept 121
Phase 5. Pilot: The Test Run 123
Phase 6. Product: Full Deployment 124
Making the Make-or-Buy Decision 124
Decision Scenarios 127
Overarching Success Factors 129
Balancing Automation and Integration 130
The Use-Case Library 133
Technical Requirements for Implementing AA 139
Table of Contents | vii
Infrastructure Setup Challenges 141
IT System Integration Challenges 149
Governance Challenges 151
Conclusion 157
6. Augmented Frames. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
Business Objects and Frame Units 159
Understanding Frames 162
Key Features of Frames 164
Frame Types 165
Frame Engines 167
Frame Engine Types 169
Attribute Aggregation 170
Engine Interfaces 172
Result Objects 179
Implementation Challenges 186
Frame Agent 187
Dissolving Frames 188
Identifying Types 188
Translating Frame Units 188
Enriching Frames 188
Orchestrating Calls 189
Standardizing Results 189
Central Repository 189
Monitoring and Performance Analysis 189
User Access and Security 190
User Interface 190
Frame Dissolver 193
Frame Adapter 195
Dealing with Group Variables 196
Dealing with Bottom-up Business Object Structures 198
Dealing with Unconnected Business Objects 199
Frame Creator 200
Case Study: AP/TP Frame Engine 201
Infrastructure and Technology 207
An Iterative Approach to Introducing Augmented Frames 211
Iteration 1: Free Frames and Frame Engines 211
Iteration 2: A Frame Agent and Frame Adapter 212
Iteration 3: The Frame Dissolver, ID Frames, and Indexed Frames 213
Iteration 4: Static Frames 213
viii | Table of Contents
Iteration 5: Dynamic Frames 214
Iteration 6: The Frame Creator 215
Iteration Wrap-up 215
Conclusion 216
7. Applied Examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
The Underwriting Process 219
Types of Augmented Workflows in Underwriting 220
The Workflows in Detail 221
Example 1: Location Workflow 224
Situation and Problem Statement 224
Solution Overview 224
Solution Breakdown 226
Example Summary 228
Example 2: Benchmarking Workflow 229
Situation and Problem Statement 229
Solution Overview 229
Solution Breakdown 231
Example Summary 234
Example 3: Proposal Workflow 235
Situation and Problem Statement 235
Solution Overview 236
Solution Breakdown 236
Example Summary 238
Example 4: Improved Forecasting in Agile Projects 238
Situation and Problem Statement 238
Solution Overview 239
Solution Breakdown 240
Example Summary 245
Example 5: Quick Sales Intelligence 246
Situation and Problem Statement 247
Solution Overview 248
Solution Breakdown 249
Example Summary 256
Conclusion 257
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
Table of Contents | ix