Achieve greater success by increasing the agility of analytics lifecycle management Agile by Design offers the insight you need to improve analytic lifecycle management while integrating the right analytics projects into different frameworks within your business. You will explore, in-depth, what analytics projects are and why they are set apart from traditional development initiatives. Beyond merely defining analytics projects, Agile by Design equips you with the information you need to apply agile methodologies in a way that tailors your approach to individual initiatives-and the needs of your projects and team. Lifecycle management is a complex subject area, and with the increasingly important integration of analytics into multiple facets of business models, understanding how to use agile tools while managing a product lifecycle is essential to maintaining a competitive edge in today's professional world.
* Gain an understanding of the principles, processes, and practices associated with effective analytic lifecycle management * Discover techniques that will enable you to successfully initiate, plan, and execute analytic development projects with an eye for the opportunity to engage agile methodologies * Understand agile development frameworks * Identify which agile methodologies are best for different frameworks-and how to apply them throughout the analytic development lifecycle With analytics becoming increasingly important in today's business world, you need to understand and apply agile methodologies in order to meet rising standards of efficiency and effectiveness. Agile by Design is the perfect reference for project managers, CFOs, IT managers, and marketing managers who want to cultivate a relevant, forward-thinking lifecycle management style.
RACHEL ALT-SIMMONS, MBB, PMI-ACP, PMP, CSM, is a principal consultant within the Financial Services Practice at SAS Institute and an adjunct faculty member at Boston University. She is a performance-excellence business intelligence (BI) and analytics practitioner with deep experience in the insurance and financial services industries.
Introduction xiii About the Author xix Chapter 1 Adjusting to a Customer-Centric Landscape 1 It s a Whole New World 1 From Customer-Aware to Customer-Centric 3 Being Customer-Centric, Operationally Efficient, and Analytically Aware 6 Our Example in Motion 9 Enabling Innovation 10 Chapter 2 The Analytic Lifecycle 13 What Are Analytics, Anyway? 13 Analytics in Your Organization 15 Case Study Example 17 Beyond IT: The Business Analytic Value Chain 18 Analytic Delivery Lifecycle 19 Stage One Perform Business Discovery 20 Stage Two Perform Data Discovery 21 Stage Three Prepare Data 22 Stage Four Model Data 23 Stage Five Score and Deploy 24 Stage Six Evaluate and Improve 25 Getting Started 25 Summary 26 Chapter 3 Getting Your Analytic Project off the Ground 27 A Day in the Life 29 Visioning 30 Facilitating Your Visioning Session 32 Think Like a Customer 33 Summary 36 Chapter 4 Project Justification and Prioritization 37 Organizational Value of Analytics 37 Analytic Demand Management Strategy 38 Results 40 Project Prioritization Criteria 42 Value-Based Prioritization 43 Financial-Based Prioritization 45 Knowledge Acquisition Spikes 46 Summary 47 Chapter 5 Analytics the Agile Way 49 Getting Started 49 Understanding Waterfall 51 The Heart of Agile 53 The Agile Manifesto/Declaration of Interdependence 54 Selecting the Right Methodology 57 Scrum 58 eXtreme Programming (XP) 59 Summary 61 Chapter 6 Analytic Planning Hierarchies 63 Analytic Project Example 63 Inputs into Planning Cycles 66 Release Planning 69 Analytic Release Plan 70 Release Train 71 Summary 73 Chapter 7 Our Analytic Scrum Framework 75 Getting Started 75 The Scrum Framework 77 Sprint Planning 78 Sprint Execution 80 Daily Standup 81 How Do We Know When We re Done? 82 Sprint Review 83 Sprint Retrospective 85 Summary 85 Chapter 8 Analytic Scrum Roles and Responsibilities 87 Product Owner Description 89 Product Owner: A Day in the Life 91 ScrumMaster Description 92 ScrumMaster: A Day in the Life 94 Analytic Development Team Description 95 Additional Roles 97 Summary 98 Chapter 9 Gathering Analytic User Stories 101 Overview 101 User Stories 103 The Card 104 Analytic User Story Examples 105 Technical User Stories 106 The Conversation 107 The Confirmation 107 Tools and Techniques 108 INVEST in Good Stories 109 Epics 111 Summary 112 Chapter 10 Facilitating Your Story Workshop 113 Stakeholder Analysis 113 Managing Stakeholder Influence 116 Agile versus Traditional Stakeholder Management 118 The Story Workshop 118 Workshop Preparation 119 Facilitating Your Workshop 121 Must-Answer Questions 123 Post-Workshop 124 Summary 126 Chapter 11 Collecting Knowledge Through Spikes 127 With Data, Well Begun Is Half Done 127 The Data Spike 129 Data Gathering 131 Visualization and Iterations 135 Defining Your Target Variable 136 Summary 138 Chapter 12 Shaping the Analytic Product Backlog 141 Creating Your Analytic Product Backlog 141 Going DEEP 145 Product Backlog Grooming 146 Defining Ready 146 Managing Flow 147 Release Flow Management 148 Sprint Flow Management 148 Summary 149 Chapter 13 The Analytic Sprint: Planning and Execution 151 Committing the Team 151 The Players 153 Sprint Planning 154 Velocity 155 Task Definition 156 The Team s Definition of Done 158 Organizing Work 159 Sprint Zero 160 Sprint Execution 161 Summary 163 Chapter 14 The Analytic Sprint: Review and Retrospective 165 Sprint Review 165 Roles and Responsibilities 168 Sprint Retrospective 168 Sprint Planning (Again) 171 Layering in Complexity 173 Summary 175 Chapter 15 Building in Quality and Simplicity 177 Quality Planning 177 Simple Design 181 Coding Standards 183 Refactoring 184 Collective Code Ownership 185 Technical Debt 186 Testing 187 Verification and Validation 188 Summary 189 Chapter 16 Collaboration and Communication 191 The Team Space 191 Things to Put in the Information Radiator 194 Analytic Velocity 195 Improving Velocity 196 The Kanban or Task Board 197 Considering an Agile Project Management Tool 198 Summary 200 Chapter 17 Business Implementation Planning 203 Are We Done Yet? 203 What s Next? 205 Analytic Release Planning 206 Section 1: What Did We Do, and Why? 206 Section 2: Supporting Information 208 Section 3: Model Highlights 208 Section 4: Conclusions and Recommendations 208 Section 5: Appendix 209 Model Review 209 Levers to Pull 210 Persona-Based Design 211 Segmentation Case Study 213 Summary 214 Chapter 18 Building Agility into Test-and-Learn Strategies 215 What Is Test-and-Learn? 215 Layering in Complexity 218 Incorporating Test-and-Learn into Your Model Deployment Strategy 219 Creating a Culture of Experimentation 221 Failing Fast and Frequently 222 Who Owns Testing? 222 Getting Started 223 Summary 225 Chapter 19 Operationalizing Your Model Deployment Strategy 227 Finding the Right Model 227 Simplicity over Complexity 231 How Deep Do We Go? 231 What Is an Operational Model Process? 232 Getting Your Data in Order 234 Automate Model-Ready Data Processes 235 So Who Owns It? 236 What If I Can t Automate This Process Right Now? 236 Determine Model Scoring Frequency 237 Model Performance Monitoring 239 Analytics the Success to Plan For 241 Summary 243 Chapter 20 Analytic Ever After 245 Beginning Your Journey 245 Supporting the Analytic Team 246 The Importance of Agile Analytic Leadership 248 Finding a Pilot Project 249 Scaling Up 249 The End of the Beginning 251 Sources 253 Index 255