Applied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics

Applied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics

By: Nathaniel Lin (author)Hardback

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Bridge the gap between analytics and execution, and actually translate analytics into better business decision-making! Now that you've collected data and crunched numbers, Applied Business Analytics reveals how to fully apply the information and knowledge you've gleaned from quants and tech teams. Nathaniel Lin explains why "analytics value chains" often break due to organizational and cultural issues, and offers "in the trenches" guidance for overcoming these obstacles. You'll discover why a special breed of "analytics deciders" is indispensable for any organization that seeks to compete on to become one of those deciders...and how to identify, foster, support, empower, and reward others to join you. Lin draws on actual cases and examples from his own experience, augmenting them with hands-on examples and exercises to integrate analytics at all levels: from top-level business questions to low-level technical details. Along the way, you'll learn how to bring together analytics team members with widely diverse goals, knowledge, and backgrounds. Coverage includes: * How analytical and conventional decision making differ - and the challenging implications * How to determine who your analytics deciders are, and ought to be * Proven best practices for actually applying analytics to decision-making * How to optimize your use of analytics as an analyst, manager, executive, or C-level officer Applied Business Analytics will be invaluable to wide audiences of professionals, decision-makers, and consultants involved in analytics, including Chief Analytics Officers, Chief Data Officers, Chief Scientists, Chief Marketing Officers, Chief Risk Officers, Chief Strategy Officers, VPs of Analytics and/or Big Data, data scientists, business strategists, and line of business executives. It will also be exceptionally useful to students of analytics in any graduate, undergraduate, or certificate program, including candidates for INFORMS certification.

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About Author

Dr. Nathaniel Lin is a recognized leader in marketing and business analytics across various industries worldwide. He has over 20 years of frontline experience applying actionable advanced analytics strategies to the world's largest companies in technology, finance, automotive, telecommunications, retail, and across many other businesses, including IBM, Fidelity Investments, OgilvyOne, and Aspen Marketing Analytics. Nathaniel is currently the Chief Customer Insights Officer of Attract China. He is leading the efforts to develop leading edge Big Data Analytics technology and knowledge assets to deliver unparalleled values to Chinese travelers and U.S. clients. Nathaniel is widely recognized as an expert, teacher, author, and hands-on leader and senior executive in the application of data and advanced analytics in a wide variety of businesses. He is also the Founder and President of Analytics Consult, LLC ( He leverages his rich and unique expertise in business analytics to help companies optimize their customer, marketing, and sales strategies. Together with his team, Nathaniel serves as a trusted strategic advisor to senior management teams. He is frequently invited as the keynote speaker in analytics events and advised over 150 CEOs in the U.S. and aboard on analytics and Big Data issues. He was invited by WWW2010 as one of the four expert panelists (together with the heads of Google Analytics, eBay Analytics, and Web Analytics Association) on the Future of Predictive Analytics. As a recognized analytics expert, Nathaniel has partnered with Professor Tom Davenport to benchmark analytics competencies of major corporations across different industries. He also demonstrates his passion in cultivating future analytics leaders by teaching Strategic CRM and Advanced Business Analytics for MBA students at the Georgia Tech College of Management, Boston College Carroll School of Management, and Quant III Advanced Business Analytics at Bentley University. Nathaniel holds a PhD in Engineering from Birmingham University (UK) and an MBA from MIT Sloan School of Management.


Foreword xv Acknowledgments xviii About the Author xix Preface xxi Why Another Book on Analytics? xxi How This Book Is Organized xxii After Reading and Working Through This Book xxvi Chapter 1: Introduction 1 Raw Data, the New Oil 1 Data Big and Small Is Not New 2 Definition of Analytics 3 Top 10 Business Questions for Analytics 5 Financial Management 6 Customer Management 8 HR Management 11 Internal Operations 11 Vital Lessons Learned 12 Use Analytics 13 Reasons Why Analytics Are Not Used 13 Linking Analytics to Business 14 Business Analytics Value Chain 14 Integrated Approach 17 Hands-on Exercises 17 Reasons for Using KNIME Workflows 17 Conclusion 18 Chapter 2: Know Your Ingredients-Data Big and Small 21 Garbage in, Garbage out 21 Data or Big Data 22 Definition of Big Data 22 Data Types 23 Company Data 24 Individual Customer Data 31 Sensor Data 34 Syndicated Data 35 Data Formats 38 Structured, Poorly Structured, and Unstructured Data 39 Conclusion 42 Chapter 3: Data Management-Integration, Data Quality, and Governance 43 Data Integration 44 Data Quality 45 Data Security and Data Privacy 46 Data Security 47 Data Privacy 48 Data Governance 53 Data Preparation 56 Data Manipulation 58 Types of Data 58 Categorize Numerical Variables 59 Dummy Variables 60 Missing Values 60 Data Normalization 61 Data Partitions 62 Exploratory Data Analysis 64 Multidimensional Cube 65 Slicing 65 Dicing 65 Drilling Down/Up 66 Pivoting 66 Visualization of Data Patterns and Trends 66 Popularity of BI Visualization 66 Selecting a BI Visualization Tool 67 Beyond BI Visualizations 70 Conclusion 70 Chapter 4: Handle the Tools: Analytics Methodology and Tools 73 Getting Familiar with the Tools 73 Master Chefs Who Can't Cook 74 Types of Analytics 75 Descriptive and Diagnostic Tools: BI Visualization and Reporting 75 Advanced Analytics Tools: Prediction, Optimization, and Knowledge Discovery 77 A Unified View of BI Analysis, Advanced Analytics, and Visualization 77 Two Ways of Knowledge Discovery 79 Types of Advanced Analytics and Applications 81 Analytics Modeling Tools by Functions 81 Modeling Likelihood 82 Modeling Groupings 86 Supervised Learning 87 Value Prediction 97 Other Models 102 Conclusion 111 Chapter 5: Analytics Decision-Making Process and the Analytics Deciders 115 Time to Take Off the Mittens 115 Overview of the Business Analytics Process (BAP) 116 Analytics Rapid Prototyping 120 Analytics Sandbox for Instant Business Insights 122 Analytics IT Sandbox Database 125 People and the Decision Blinders 125 Risks of Crossing the Chasms 126 The Medici Effect 127 Analytics Deciders 129 How to Find Analytics Deciders 130 Becoming an Analytics Decider 132 Conclusion 139 Chapter 6: Business Processes and Analytics (by Alejandro Simkievich) 141 Overview of Process Families 142 Enterprise Resource Planning 143 Customer Relationship Management 145 Product Lifecycle Management 147 Shortcomings of Operational Systems 147 Embedding Advanced Analytics into Operational Systems 150 Example 1: Forecast 152 Example 2: Improving Salesforce Decisions 154 Example 3: Engineers Get Instant Feedback on Their Design Choices 155 Conclusion 155 Chapter 7: Identifying Business Opportunities by Recognizing Patterns 157 Patterns of Group Behavior 157 Importance of Pattern Recognition in Business 158 Group Patterns by Clustering and Decision Trees 161 Three Ways of Grouping 162 Recognize Purchase Patterns: Association Analysis 167 Association Rules 167 Business Case 169 Patterns over Time: Time Series Predictions 173 Time Series Models 174 Conclusion 179 Chapter 8: Knowing the Unknowable 181 Unknowable Events 181 Unknowable in Business 182 Poor or Inadequate Data 185 Data with Limited Views 185 Business Case 186 Predicting Individual Customer Behaviors in Real-Time 192 Lever Settings and Causality in Business 197 Start with a High Baseline 199 Causality with Control Groups 199 Conclusion 201 Chapter 9: Demonstration of Business Analytics Workflows: Analytics Enterprise 203 A Case for Illustration 204 Top Questions for Analytics Applications 209 Financial Management 210 Human Resources 212 Internal Operations 213 Conclusion 218 Chapter 10: Demonstration of Business Analytics Workflows-Analytics CRM 219 Questions About Customers 220 Know the Customers 220 Actionable Customer Insights 222 Social and Mobile CRM Issues 226 CRM Knowledge Management 227 Conclusion 228 Chapter 11: Analytics Competencies and Ecosystem 231 Analytics Maturity Levels 233 Analytics Organizational Structure 234 The Centralized Model 236 The Consulting Model 237 The Decentralized Model 238 The Center of Excellence Model 239 Reporting Structures 241 Roles and Responsibilities 242 Analytics Roles 242 Business Strategy and Leadership Roles 243 Data and Quantitative Roles 247 Analytics Ecosystem 250 The In-House IT Function 250 External Analytics Advisory and Consulting Resources 251 Analytics Talent Management 256 Conclusion 260 Chapter 12: Conclusions and Now What? 263 Analytics Is Not a Fad 263 Acquire Rich and Effective Data 264 Start with EDA and BI Analysis 265 Gain Firsthand Analytics Experience 265 Become an Analytics Decider and Recruit Others 266 Empower Enterprise Business Processes with Analytics 266 Recognize Patterns with Analytics 267 Know the Unknowable 268 Imbue Business Processes with Analytics 269 Acquire Competencies and Establish Ecosystem 270 Epilogue 271 Appendix A: KNIME Basics 273 Data Preparation 274 Types of Variable Values 274 Dummy Variables 275 Missing Values 275 Data Partitions 277 Exploratory Data Analysis (EDA) 279 Multi-Dimensional Cube 279 Slicing 281 Dicing 281 Drilling Down or Up 281 Pivoting 281 Index 285

Product Details

  • publication date: 19/12/2014
  • ISBN13: 9780133481501
  • Format: Hardback
  • Number Of Pages: 320
  • ID: 9780133481501
  • weight: 553
  • ISBN10: 0133481506

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