2 When I first read Nathaniel Lin s Applied Business Analytics, I thought, I wish I had written this. The points Nathaniel makes about analytics deciders hit a unique target managers who today don t realize what it takes to drive business using data. Having analytics deciders engaged at the intersections makes it possible for innovation to flow throughout the organization, like traffic cops in downtown Manhattan. This is one of those books that you keep on your desk and refer to as you build an analytics team. Here are all the things I loved about this book: the Advanced Analytics Layer (it has immediate applicability); the case studies (which are practical); and the hands-on approach with KNIME (the real-world experience). Theresa Kushner, Vice President Enterprise Information Management, VMware and former Sr. Director of Customer Intelligence, Cisco Nathaniel has done a terrific job of capturing the key components needed to build high-performing analytics to drive business growth. This is a must read for senior executives involved in analytics for decision-making. John F Carter, Senior Vice President, Analytics, Insight & Loyalty, Charles Schwab Making analytics mainstream: There are scores of books on the application of analytics to organizations, however, Applied Business Analytics is unique in two respects. First, it identifies why organizations fail to truly leverage analytics to achieve transformational outcomes. Second, it proposes a framework in which business processes are modified to accommodate analytics through a continuous dialogue, rather than isolated analytical experiments. Forward-looking business leaders who want to truly leverage Big Data and analytics to transform and lead their organizations would do well to take to heart the core message of this book. Prat Moghe, CEO of Cazena and former Senior Vice President of IBM Netezza Business analytics has developed into an area of intense focus for many organizations, yet its successful application remains more art than science. Nathaniel Lin s Applied Business Analytics represents a significant step forward in remedying that situation by enabling business leaders to create the teams and business processes required to succeed with analytics. Business analytics is by its nature a deeply technical topic, and Dr. Lin does not skimp on the technical details, including meaty yet approachable hands-on exercises. Applied Business Analytics keeps the technical discussion where it belongs: in clear service of wellarticulated and relevant business problems that provide the necessary context for analytics projects to create meaningful value. No mere how-to guide, Dr. Lin s book provides a complete picture of the analytical organization, as well as the conditions and processes required to develop and enable analytics deciders, the data-driven
3 change agents who propel organizations to move from gut-based decision making to rational, evidence based decision making. Packed with numerous real-world case studies and war stories, Nathaniel Lin s Applied Business Analytics is a solid starting point for any executive seeking to transform an organization with analytics. Adam Ferrari, CTO Crisply and Strategic Advisor Big Data and Vice President of Product Development, Oracle, Former CTO, Endeca Technologies Leaders who are establishing a data-driven culture will benefit from the lucid analytical process and methodologies that Nathaniel demonstrates in Applied Business Analytics. The chapter about Analytics Eco-system includes a fantastic comparison of different team structures that exist today. Jane Chen, Head of Analytics Center of Excellence, Analog Devices I am positively impressed by Nathaniel s book. This book has a unique position in the marketplace, standing out as an insightful perspective balancing an evangelist book and technical how-to cookbook. I appreciate Nathaniel s thought leadership and guidance. Applied Business Analytics is 70% business and technical context discussion and 30% of how to be effective in organizational framework. The most challenging part of analytics leaders in today s world is still the conflict or tension between business drivers and technical details. That is why analytics deciders are critical for maximizing analytics functional impact on businesses. Balancing technical details (how) with why analytics is critical to your success and what result you can (expect to) achieve by building analytics capabilities is challenging. Nathaniel s personal stories, case studies, and real-world success are helpful. I believe this book can be a great reference guide for emerging analytics leaders. G ary Cao, Vice President, Information Services & Analytics Strategy, Cardinal Health, Inc.
4 Applied Business Analytics
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6 Applied Business Analytics Integrating Business Process, Big Data, and Advanced Analytics Nathaniel Lin
7 Editor-in-Chief: Amy Neidlinger Executive Editor: Jeanne Glasser Levine Operations Specialist: Jodi Kemper Cover Designer: Chuti Prasertsith Managing Editor: Kristy Hart Project Editor: Deadline Driven Publishing Copy Editor: Nancy Sixsmith Proofreader: Deadline Driven Publishing Indexer: Angie Martin Compositor: Jake McFarland Manufacturing Buyer: Dan Uhrig 2015 by Nathaniel Lin Upper Saddle River, New Jersey For information about buying this title in bulk quantities, or for special sales opportunities (which may include electronic versions; custom cover designs; and content particular to your business, training goals, marketing focus, or branding interests), please contact our corporate sales department at or (800) For government sales inquiries, please contact For questions about sales outside the U.S., please contact Company and product names mentioned herein are the trademarks or registered trademarks of their respective owners. All rights reserved. No part of this book may be reproduced, in any form or by any means, without permission in writing from the publisher. Printed in the United States of America First Printing December ISBN-10: ISBN-13: Pearson Education LTD. Pearson Education Australia PTY, Limited. Pearson Education Singapore, Pte. Ltd. Pearson Education Asia, Ltd. Pearson Education Canada, Ltd. Pearson Educación de Mexico, S.A. de C.V. Pearson Education Japan Pearson Education Malaysia, Pte. Ltd. Library of Congress Control Number:
8 To my parents Tah-Fu and Paw-Chu Lin, who have spent years encouraging me to do something special. Even though this book is nothing special, it is a token of my appreciation for their love and nurturing since they brought me into the world 60 years ago. Last, to the one who really matters, I would use the same dedication from my PhD thesis: Jehovah, who stretches forth the heavens and lays the foundations of the earth and forms the spirit of man within him. Zechariah 12:1
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10 Contents 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 Raw Data, the New Oil Data Big and Small Is Not New Definition of Analytics Top 10 Business Questions for Analytics Financial Management Customer Management HR Management Internal Operations Vital Lessons Learned Use Analytics Reasons Why Analytics Are Not Used Linking Analytics to Business Business Analytics Value Chain Integrated Approach Hands-on Exercises Reasons for Using KNIME Workflows Conclusion Chapter 2 Know Your Ingredients Data Big and Small Garbage in, Garbage out Data or Big Data Definition of Big Data Data Types Company Data Individual Customer Data
11 X APPLIED BUSINESS ANALYTICS Sensor Data Syndicated Data Data Formats Structured, Poorly Structured, and Unstructured Data Conclusion Chapter 3 Data Management Integration, Data Quality, and Governance Data Integration Data Quality Data Security and Data Privacy Data Security Data Privacy Data Governance Data Preparation Data Manipulation Types of Data Categorize Numerical Variables Dummy Variables Missing Values Data Normalization Data Partitions Exploratory Data Analysis Multidimensional Cube Slicing Dicing Drilling Down/Up Pivoting Visualization of Data Patterns and Trends Popularity of BI Visualization Selecting a BI Visualization Tool Beyond BI Visualizations Conclusion Chapter 4 Handle the Tools: Analytics Methodology and Tools Getting Familiar with the Tools Master Chefs Who Can t Cook Types of Analytics
12 CONTENTS xi Descriptive and Diagnostic Tools: BI Visualization and Reporting Advanced Analytics Tools: Prediction, Optimization, and Knowledge Discovery A Unified View of BI Analysis, Advanced Analytics, and Visualization Two Ways of Knowledge Discovery Types of Advanced Analytics and Applications Analytics Modeling Tools by Functions Modeling Likelihood Modeling Groupings Supervised Learning Value Prediction Other Models Conclusion Chapter 5 Chapter 6 Analytics Decision-Making Process and the Analytics Deciders Time to Take Off the Mittens Overview of the Business Analytics Process (BAP) Analytics Rapid Prototyping Analytics Sandbox for Instant Business Insights Analytics IT Sandbox Database People and the Decision Blinders Risks of Crossing the Chasms The Medici Effect Analytics Deciders How to Find Analytics Deciders Becoming an Analytics Decider Conclusion Business Processes and Analytics (by Alejandro Simkievich) Overview of Process Families Enterprise Resource Planning Customer Relationship Management Product Lifecycle Management Shortcomings of Operational Systems
13 XII APPLIED BUSINESS ANALYTICS Embedding Advanced Analytics into Operational Systems Example 1: Forecast Example 2: Improving Salesforce Decisions Example 3: Engineers Get Instant Feedback on Their Design Choices Conclusion Chapter 7 Identifying Business Opportunities by Recognizing Patterns Patterns of Group Behavior Importance of Pattern Recognition in Business Group Patterns by Clustering and Decision Trees Three Ways of Grouping Recognize Purchase Patterns: Association Analysis Association Rules Business Case Patterns over Time: Time Series Predictions Time Series Models Conclusion Chapter 8 Knowing the Unknowable Unknowable Events Unknowable in Business Poor or Inadequate Data Data with Limited Views Business Case Predicting Individual Customer Behaviors in Real-Time Lever Settings and Causality in Business Start with a High Baseline Causality with Control Groups Conclusion Chapter 9 Demonstration of Business Analytics Workflows: Analytics Enterprise A Case for Illustration Top Questions for Analytics Applications
14 CONTENTS xiii Financial Management Human Resources Internal Operations Conclusion Chapter 10 Demonstration of Business Analytics Workflows Analytics CRM Questions About Customers Know the Customers Actionable Customer Insights Social and Mobile CRM Issues CRM Knowledge Management Conclusion Chapter 11 Analytics Competencies and Ecosystem Analytics Maturity Levels Analytics Organizational Structure The Centralized Model The Consulting Model The Decentralized Model The Center of Excellence Model Reporting Structures Roles and Responsibilities Analytics Roles Business Strategy and Leadership Roles Data and Quantitative Roles Analytics Ecosystem The In-House IT Function External Analytics Advisory and Consulting Resources Analytics Talent Management Conclusion Chapter 12 Conclusions and Now What? Analytics Is Not a Fad Acquire Rich and Effective Data Start with EDA and BI Analysis Gain Firsthand Analytics Experience Become an Analytics Decider and Recruit Others
15 XIV APPLIED BUSINESS ANALYTICS Empower Enterprise Business Processes with Analytics Recognize Patterns with Analytics Know the Unknowable Imbue Business Processes with Analytics Acquire Competencies and Establish Ecosystem Epilogue Appendix A KNIME Basics Data Preparation Types of Variable Values Dummy Variables Missing Values Data Partitions Exploratory Data Analysis (EDA) Multi-Dimensional Cube Slicing Dicing Drilling Down or Up Pivoting Index
16 Foreword This book is well-timed for the current business analytics zeitgeist the spirit of the times in a variety of ways. Analytics and big data have become accepted tools for business strategy and competition. In surveys, virtually every manager agrees that they are important resources for business. If you are not a believer in data and analytics as guides to decision making, it is becoming embarrassing or politically incorrect to say so. This is all good news, at least from my perspective. The only problem with this broad-scale consensus is that many managers and organizations still lack the skills and understanding to make analytics work for them. Lin s book is directed at just this issue. As he says, there have been a variety of books that attempt to get managers excited about analytics and textbooks that delve into the details of analytics without much sense of the business context. This book is a bridge between those extremes. It s for managers who already believe that analytics are important, but who don t yet know enough to practice them personally. This transition is important and, indeed, necessary. In sophisticated companies, it is becoming difficult to draw the line anymore between analytics and the way work is done. Business is analytics, analytics is business. This book is designed to enable regular business people to create and consume analytics, even advanced ones. The detailed modeling and analysis of data may continue to be left up to professional quants, but the framing, interpreting, and communicating of the analytical work will be a highly collaborative effort among deciders, as Lin puts it, and quantitative experts. Another way in which the book fits with the contemporary analytical mood is its focus on business and analytical processes. Lin correctly points out that analytics are increasingly being embedded within key business processes. In that sense, they are becoming
17 XVI APPLIED BUSINESS ANALYTICS somewhat invisible; as I write today, in fact, Gartner named advanced, pervasive, and invisible analytics as one of the Top 10 Strategic Technology Trends for This invisibility makes the importance of management understanding even greater; if your business is heavily dependent on analytics in the underlying technology and process infrastructure, someone needs to constantly ask intelligent questions about underlying assumptions, methods, and outcomes. To continue the process orientation, many of the topics and insights in this book are structured in terms of an analytics process that ranges from data preparation to data analysis and interpretation. The idea of a process fits the current analytics environment well. For too long, analytics have been pursued as a slow, one-off, painstaking process that would be difficult to institutionalize. Today, however, many companies are beginning to create analytical factories that emphasize speed, scale, and repeatability. The means to do this is to view analytics as a process with clear steps, handoffs, and modules. That is the focus in the book, and it s buttressed by examples using the open source analytics tool KNIME, which is itself structured in terms of processes. If we adopt Lin s model of thinking about analytics in process terms, our analytics will have higher productivity and impact. A final linkage between this book and the current set of analytics themes is the focus on both big data and small. Most current books are on traditional, small data analytics or big data, but few cross that chasm to address both. Lin moves comfortably between analytics on traditional data (for example, marketing propensity models) and such big data types as social media and text from online product reviews. It s clear to me that before long, we won t use the term big data, but we will certainly want to deal with unstructured, large-volume data. In any case, you ll be well-prepared for a world that integrates and analyzes all types of data if you read this book. Lin helpfully supplies a list of likely data elements that an organization might employ for typical analyses in different business areas.
18 FOREWORD xvii This book is not only up-to-the-moment, but extremely comprehensive. There is scarcely an area of business analytics that is not discussed. You may want to read it quickly when you acquire it, and then use it as a reference book as you encounter a variety of analytics situations over the next several years. The ideas are both classical and contemporary, and they won t go out of fashion or relevance for a long time. Thomas H. Davenport, Distinguished Professor, Babson College Author of Competing on Analytics and Big Work
19 Acknowledgments I would like to thank the following people: My wife, Lu, for her confidence and encouragement. Alejandro Simkievich, a good friend and fellow Sloanie, who wrote Chapter 6. Any errors or omissions are solely mine. I want to thank also FT Times, and Victor for reading the draft and providing valuable suggestions.
20 About the 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 (www.analyticsconsult.com). 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.
21 XX APPLIED BUSINESS 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.
22 Preface I have to admit that this book turned out to be much harder to write than I had envisioned. Having spent decades applying analytics and building and leading analytics teams, I thought it would be simply a summary of what I had learned and done before. Unf ortunately, I find myself constantly struggling to find the Goldilocks approach am I covering too much technical detail or too little? The latter, in my opinion, is worse because the lack of details might prevent business readers from doing the hands-on analytics exercises. However, the former with too many technical details would likely discourage business readers from reading the book altogether! After several rounds of revisions, I hope I have managed to strike a balance to ensure the original objective of writing an analytics hands-on book for business readers has been met. However, in the event you find the content either too technical or not sufficiently technical, I beg for your indulgence. My advice is that you simply skip over the offending sections and go onto the next section. I am sure there are enough examples and business cases in this book that any serious business reader aspiring to be analytical would find relevant and helpful. Why Another Book on Analytics? Despite the many titles on analytics, a quick survey reveals that most broadly fall into two categories. One group focuses on promoting xxi
23 xxii APPLIED BUSINESS ANALYTICS how businesses benefit from analytics. These are usually written by evangelists promoting the virtues of analytics. At the opposite end are those books written by quants for other analytics quants. These contain too much technical detail for business readers. Few books are written to bridge the gap. There is a need for a book with hands-on applications of business analytics that solve real business problems that business readers can understand, practice, and use. I often hear the laments of business leaders: I am sold after reading books promoting the power of analytics, but none can show me how it is done and how to actually apply it in my business without requiring me to go back to school and getting an advanced degrees in analytics and statistics! If you share the same sentiments, then this book is for you. How This Book Is Organized Before getting your hands dirty with hands-on exercises, this book devotes the first four chapters to the basics, that is, the data, analytics tools (from simple to leading edge), and their relationships to the various business processes. The book continues with how to embed analytics and integrate the findings in existing or new strategic processes. With this in mind, the chapters are organized as follows: Chapter 1, Introduction, is an introduction to analytics. It shows how analytics as the refining of data will transform business the way refining of oil transformed the twentieth century. It also addresses definitions, potential areas of applications, some vital lessons learned, how analytics are positioned in the business context, and how analytics are integrated into the business process. Chapter 2, Know Your Ingredients Data Big and Small, discusses everything you need to know about the basic ingredients of successful business analytics. It defines the differences between Big Data and the various types of data that may be
24 PREFACE xxiii useful to business analytics. It also describes data formats and the increasing importance of poorly or unstructured data to business insights and how to handle them. Chapter 3, Data Management Integration, Data Quality, and Governance, provides everything you need to manage and analyze data, from processing diverse data, to the importance of governance on data security and privacy, to good data versus bad data, to quality and latency, and to where to go beyond internal data sources. Chapter 4, Handle the Tools: Analytics Methodology and Tools, discusses the most commonly used analytics models and tools, and provides hands-on exercises for some of the most commonly used analytics modeling techniques so you can see and feel how they work within real business cases. Its coverage spans basic models such as regressions, clustering, decision trees to the more sophisticated Big Data analytics such as text mining, and sentiment analysis on real data including online reviews on Amazon Fine Food and TripAdvisor hotels. A value prediction methodology using Ensemble Regression Tree model is also presented. Chapter 5, Analytics Decision-Making Process and the Analytics Deciders, elucidates the differences between the conventional and the analytics decisioning process. It starts by exploring how conventional decision processes often falls short. It also describes the analytics decisioning process, known in this book as the BAP (Business Analytics Process), and how the right analytics deciders are needed to work in the intersection between silos to ensure the BAP s success. It ends with notes on how to become, identify, recruit, and retain analytics deciders. Chapter 6, Business Processes and Analytics, discusses the business processes that can benefit from analytics. It also covers
25 xxiv APPLIED BUSINESS ANALYTICS today s ERP (Enterprise Resource Planning) that is fueled by analytics. It includes CRM (Customer Relationship Management), SCM (Supply Chain Management), Financial Management, PLM (Product Cycle Management), and Human Capital Management. Chapter 7, Identifying Business Opportunities by Recognizing Patterns, explains why the current way of dividing customers into segments just won t cut it. It tells you how to leverage advanced analytics tools to detect patterns of behaviors, events, trends, topics, preferences, and other critical factors that can impact business. Finally, it discusses pattern detection and how the group behavior of customers may help marketers plan more effective engagements using insights from the Market Basket Analysis and customer segment persona discovered using analytics. Chapter 8, Knowing the Unknowable, discusses those topics and questions that are often deemed unknowable or unique (UU). These may involve little or limited data, predicting individual behavior in real time, and determining causality and lever settings for complex situations. This chapter provides real-life business cases and actual analytics workflows to illustrate how enlightened businesses can know and leverage analytics in these apparent unknowable events, thus, gaining a competitive edge over the competition. A novel methodology is also described to predict customer wallets and how to use that to form effective strategies. Chapter 9, Demonstration of Business Analytics Workflows Analytics Enterprise, uses the proper ingredients and tools to answer the top business questions except CRM (Customer Relationship Management), which is discussed in Chapter 10. You are encouraged to download and modify the workflows so that you can try to run them and adapt them to your own needs.
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COPYRIGHT 2012 VERTICURL WHITEPAPER: TOP MISTAKES TO AVOID WHEN BUILDING A DEMAND CENTER For many B2B organizations, building a demand center is a no-brainer. Learn how to ensure you re successful by avoiding
2.0 Big Data and Data Analytics (Volume 18, Number 3) By Heather A. Smith James D. McKeen Sponsored by: Introduction At a time when organizations are just beginning to do the hard work of standardizing
Get Better Business Results From the Four Stages of Your Customer Lifecycle Stage 1 Acquisition A white paper from Identify Unique Needs and Opportunities at Each Lifecycle Stage It s a given that having
Big Data Putting data to productive use Fast Forward What is big data, and why should you care? Get familiar with big data terminology, technologies, and techniques. Getting started with big data to realize
ElegantJ BI White Paper Key Performance Indicators (KPI) A Critical Component of Enterprise Business Intelligence (BI) Integrated Business Intelligence and Reporting for Performance Management, Operational
BRIDGING THE DIGITAL DISCONNECT: HOW OUTSOURCING DIGITAL MARKETING CREATES A UNIFIED MARKETING STRATEGY In today s fragmented digital landscape, marketing executives must bridge the gap between instant,
Benchmark Report Content Marketing in 2014: Sponsored By: 2014 Demand Metric Research Corporation in Partnership with Ascend2. All Rights Reserved. TABLE OF CONTENTS 3 Executive Summary 10 Content Campaign
CRM Expert Advisor White Paper 3 Keys to Preparing for CRM Success: Avoid the Pitfalls and Follow Best Practices Ten years ago, when CRM was nascent in the market, companies believed the technology alone