Monetizing Your Data - Andrew Roman Wells - ebook

Monetizing Your Data ebook

Andrew Roman Wells

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149,99 zł

Opis

Transforming data into revenue generating strategies and actions Organizations are swamped with data--collected from web traffic, point of sale systems, enterprise resource planning systems, and more, but what to do with it? Monetizing Your Data provides a framework and path for business managers to convert ever-increasing volumes of data into revenue generating actions through three disciplines: decision architecture, data science, and guided analytics. There are large gaps between understanding a business problem and knowing which data is relevant to the problem and how to leverage that data to drive significant financial performance. Using a proven methodology developed in the field through delivering meaningful solutions to Fortune 500 companies, this book gives you the analytical tools, methods, and techniques to transform data you already have into information into insights that drive winning decisions. Beginning with an explanation of the analytical cycle, this book guides you through the process of developing value generating strategies that can translate into big returns. The companion website, www.monetizingyourdata.com, provides templates, checklists, and examples to help you apply the methodology in your environment, and the expert author team provides authoritative guidance every step of the way. This book shows you how to use your data to: * Monetize your data to drive revenue and cut costs * Connect your data to decisions that drive action and deliver value * Develop analytic tools to guide managers up and down the ladder to better decisions Turning data into action is key; data can be a valuable competitive advantage, but only if you understand how to organize it, structure it, and uncover the actionable information hidden within it through decision architecture and guided analytics. From multinational corporations to single-owner small businesses, companies of every size and structure stand to benefit from these tools, methods, and techniques; Monetizing Your Data walks you through the translation and transformation to help you leverage your data into value creating strategies.

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Table of Contents

Cover

Title Page

Copyright

Dedication

Preface

Acknowledgments

About the Authors

Section I: Introduction

Chapter 1: Introduction

Decisions

Analytical Journey

Solving the Problem

The Survey Says…

How to Use This Book

Let's Start

Chapter 2: Analytical Cycle: Driving Quality Decisions

Analytical Cycle Overview

Hierarchy of Information User

Next Steps

Chapter 3: Decision Architecture Methodology: Closing the Gap

Methodology Overview

Discovery

Decision Analysis

Monetization Strategy

Agile Analytics

Enablement

Summary

Section II: Decision Analysis

Chapter 4: Decision Analysis: Architecting Decisions

Category Tree

Question Analysis

Key Decisions

Data Needs

Action Levers

Success Metrics

Category Tree Revisited

Summary

Section III: Monetization Strategy

Chapter 5: Monetization Strategy: Making Data Pay

Business Levers

Monetization Strategy Framework

Decision Analysis and Agile Analytics

Competitive and Market Information

Summary

Chapter 6: Monetization Guiding Principles: Making It Solid

Quality Data

Be Specific

Be Holistic

Actionable

Decision Matrix

Grounded in Data Science

Monetary Value

Confidence Factor

Measurable

Motivation

Organizational Culture

Drives Innovation

Chapter 7: Product Profitability Monetization Strategy: A Case Study

Background

Business Levers

Discovery

Decide

Data Science

Monetization Framework Requirements

Decision Matrix

Section IV: Agile Analytics

Chapter 8: Decision Theory: Making It Rational

Decision Matrix

Probability

Prospect Theory

Choice Architecture

Cognitive Bias

Chapter 9: Data Science: Making It Smart

Metrics

Thresholds

Trends and Forecasting

Correlation Analysis

Segmentation

Cluster Analysis

Velocity

Predictive and Explanatory Models

Machine Learning

Chapter 10: Data Development: Making It Organized

Data Quality

Dirty Data, Now What?

Data Types

Data Organization

Data Transformation

Summary

Chapter 11: Guided Analytics: Making It Relevant

So, What?

Guided Analytics

Summary

Chapter 12: User Interface (UI): Making It Clear

Introduction to UI

The Visual Palette

Less Is More

With Just One Look

Gestalt Principles of Pattern Perception

Putting It All Together

Summary

Chapter 13: User Experience (UX): Making It Work

Performance Load

Go with the Flow

Modularity

Propositional Density

Simplicity on the Other Side of Complexity

Summary

Section V: Enablement

Chapter 14: Agile Approach: Getting Agile

Agile Development

Riding the Wave

Agile Analytics

Summary

Chapter 15: Enablement: Gaining Adoption

Testing

Adoption

Summary

Chapter 16: Analytical Organization: Getting Organized

Decision Architecture Team

Decision Architecture Roles

Subject Matter Experts

Analytical Organization Mindset

Section VI: Case Study

Case Study: Michael Andrews Bespoke

Discovery

Decision Analysis Phase

Monetization Strategy, Part I

Agile Analytics

Monetization Strategy, Part II

Guided Analytics

Closing

Bibliography

Index

End User License Agreement

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Guide

Cover

Table of Contents

Begin Reading

List of Illustrations

Chapter 1: Introduction

Figure 1.1 Data Science Maturity

Figure 1.2 Data Science Impact

Figure 1.3 Capabilities for Respondent with Higher Data-Driven Decisioning

Figure 1.4 Decision Architecture Methodology

Chapter 2: Analytical Cycle: Driving Quality Decisions

Figure 2.1 Analytical Cycle

Figure 2.2 Analytical Cycle in the Abstract

Figure 2.3 The Analytical Cycle in Action

Figure 2.4 Hierarchy of the Information User

Chapter 3: Decision Architecture Methodology: Closing the Gap

Figure 3.1 The Analytical Cycle

Figure 3.2 High-Level Decision Architecture Methodology

Figure 3.3 Decision Architecture Methodology

Figure 3.4 Discovery Phase

Figure 3.5 Decision Analysis Phase

Figure 3.6 The Analytical Cycle in Action

Figure 3.7 Monetization Strategy Phase

Figure 3.8 Agile Analytics Phase

Figure 3.9 Enablement Phase

Chapter 4: Decision Analysis: Architecting Decisions

Figure 4.1 Decision Architecture: Decision Analysis Phase

Figure 4.2 Edison Car Company Category Tree

Figure 4.3 Edison Car Company Category Tree: Diagnostics

Figure 4.4 Category Tree Mapped to Questions, Decisions, Metrics, and Actions

Figure 4.5 Category Tree Mapped to Dashboards

Chapter 5: Monetization Strategy: Making Data Pay

Figure 5.1 High-Level View of the Monetization Strategy Framework

Figure 5.2 How Business Levers Partially Align to a P&L

Figure 5.3 Decision Architecture Components with Impact on the Monetization Strategy

Figure 5.4 Monetization Strategy Framework in Detail

Chapter 6: Monetization Guiding Principles: Making It Solid

Figure 6.1 Monetization Guiding Principles

Chapter 7: Product Profitability Monetization Strategy: A Case Study

Figure 7.1 Edison Furniture Business Levers

Figure 7.2 Edison Furniture Company Category Tree

Chapter 9: Data Science: Making It Smart

Figure 9.1 Customer Attrition Rate (Annualized)

Figure 9.2 Correlation Examples

Figure 9.3 Multidimensional Segmentation Model

Figure 9.4 Cluster Analysis Example

Figure 9.5 Hierarchical Clustering

Figure 9.6 K-Means Clustering

Chapter 10: Data Development: Making It Organized

Figure 10.1 Analytical Data Structure

Chapter 11: Guided Analytics: Making It Relevant

Figure 11.1 Edison Motors Customer Complaints Dashboard

Figure 11.2 Complaints by Year

Figure 11.3 (a) Average Mileage by Year of Complaint; (b) Annual Mileage Rate of Change by Year of Complaint

Figure 11.4 Average Length of Ownership by Year of Complaint

Figure 11.5 Complaints by Vehicle Ownership Period

Figure 11.6 Complaints by Mileage by Car Owner Cohort

Figure 11.7 Monthly Complaints by Vehicle Component

Figure 11.8 Monthly Power Train Complaints by Sedan Model

Figure 11.9 Sedan Model Power Train Control Chart

Chapter 12: User Interface (UI): Making It Clear

Figure 12.1 Original Control Chart

Figure 12.2 Simplified Control Chart

Figure 12.3 Remove Reference Line Labels

Figure 12.4 Reposition Axis Labels and Modify Line Width

Figure 12.5 Remove Horizontal Gridlines

Figure 12.6 Remove Vertical Gridlines

Figure 12.7 Use Color Sparingly

Figure 12.8 Pre-attentive Processing

Figure 12.9 Proximity

Figure 12.10 Similarity

Figure 12.11 Closure

Figure 12.12 Complaints by Competitor

Figure 12.13 States with Greater-than-Expected Complaints

Figure 12.14 Menu Controls

Figure 12.15 Word Cloud

Figure 12.16 Rule of Thirds

Figure 12.17 Edison Motors Customer Complaints Dashboard

Chapter 14: Agile Approach: Getting Agile

Figure 14.1 Decision Architecture Methodology

Chapter 16: Analytical Organization: Getting Organized

Figure 16.1 Using UI to Communicate Efficiently

Case Study: Michael Andrews Bespoke

Figure CS.1 Annual Client Spend and Orders

Figure CS.2 Monthly Orders Relative to Estimated Capacity

Figure CS.3 Average Annual Spend by Client Type

Figure CS.4 Client Retention by Order Year

Figure CS.5 Client Retention by Year of First Order

Figure CS.6 Monetization Business Lever Candidates

Figure CS.7 Questions from Working Session

Figure CS.8 MAB Category Tree

Figure CS.9 Decisions Mapped to Monetization Business Levers

Figure CS.10 Actions Mapped to Monetization Business Levers

Figure CS.11 MAB Analytic Data Mart

Figure CS.12 MAB Metadata

Figure CS.13 Client Order Variety of Products Ordered Statistical Analysis

Figure CS.14 Client Segmentation Model

Figure CS.15 Segment by Order Scope

Figure CS.16 Segment by Tenure

Figure CS.17 Segment by Occupation

Figure CS.18 Client and Spend Distribution

Figure CS.19 Client Segmentation Table

Figure CS.20 Client Segmentation Chart

Figure CS.21 Recommended Marketing Strategies

Figure CS.22 Engagement Monetization Decision Strategy Matrix

Figure CS.23 Engagement Monetization Strategy

Figure CS.24 MAB Category Tree with Dashboards

Figure CS.25 MAB Performance Dashboard

Figure CS.26 Client Profile Dashboard

Figure CS.27 Client Segmentation Dashboard

Figure CS.28 Client Engagement Diagnostic

Figure CS.29 Client Engagement Action

Figure CS.30 Client Retention Dashboard

Figure CS.31 Client Retention Dashboard by Client

List of Tables

Chapter 2: Analytical Cycle: Driving Quality Decisions

Table 2.1 Competitor Pricing

Table 2.2 Order Volume Trend

Table 2.3 Order Pipeline to Forecast

Table 2.4 Total Opportunity by Outlet Type

Chapter 5: Monetization Strategy: Making Data Pay

Table 5.1 Decision Matrix

Chapter 6: Monetization Guiding Principles: Making It Solid

Table 6.1 Decision Matrix for a Customer Acquisition Monetization Strategy

Table 6.2 Decision Matrix for a Maintenance Analysis

Table 6.3 Confidence Factor Analysis

Table 6.4 Descriptive Measure Matrix

Chapter 7: Product Profitability Monetization Strategy: A Case Study

Table 7.1 Decision Matrix Based on a Sample of Product Configurations

Chapter 8: Decision Theory: Making It Rational

Table 8.1 Edison Credit Card Probability Matrix

Table 8.2 Probability Matrix with Ability to Achieve Metric

Table 8.3 A Propensity Model

Chapter 9: Data Science: Making It Smart

Table 9.1 Marketing Effectiveness Decision Matrix

Table 9.2 Decision Matrix with Conversion Rate Velocity Metric

Chapter 10: Data Development: Making It Organized

Table 10.1 Customer Record

Table 10.2 Transaction History

Chapter 15: Enablement: Gaining Adoption

Table 15.1 Issue Log

Table 15.2 Enhancement Log

Case Study: Michael Andrews Bespoke

Table CS.1 Scoring Rubric for the Engagement Score

Table CS.2 Engagement Monetization Strategy Decision Matrix

Table CS.3 Multiyear–Wardrobe Clients

Table CS.4 Retention Monetization Strategy Decision Matrix

Monetizing Your Data

A GUIDE TO TURNING DATA INTO PROFIT-DRIVING STRATEGIES AND SOLUTIONS

Andrew Wells and Kathy Chiang

 

 

 

Copyright © 2017 by Andrew Wells and Kathy Chiang. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750–8400, fax (978) 646–8600, or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748–6011, fax (201) 748–6008, or online at http://www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

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Library of Congress Cataloging-in-Publication Data is Available:

ISBN 978-1-119-35624-0 (Hardcover)

ISBN 978-1-119-35626-4 (ePDF)

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Cover Design: Wiley

Cover Image: © SergeyNivens/iStockphoto

Kathy Williams Chiang:

To my parents, Si and Patty Jean Williams, who have believed in me longer than anyone else.

Andrew Roman Wells:

To my loving wife, Suzannah, who is a constant source of encouragement, love, and positive energy. And to my parents, Diana and Maitland, who instilled in me a love of numbers and a spirit of entrepreneurship.

Preface

The purpose of this book is to enable you to build monetization strategies enabled through analytical solutions that help managers and executives navigate through the sea of data to make quality decisions that drive revenue. However, this process is fraught with challenges. The first challenge is to distill the flood of information. We have a step-by-step process, Decision Architecture Methodology, that takes you from hypothesis to building an analytical solution. This process is guided by your monetization strategy, where you build decision matrixes to make economic tradeoffs for various actions. Through guided analytics, we show you how to build your analytical solution and leverage the disciplines of UI/UX to present your story with high impact and dashboard development to automate the analytical solution.

The real power of our method comes from tying together a set of disciplines, methods, tools, and skillsets into a structured process. The range of disciplines include Data Science, Decision Theory, Behavioral Economics, Decision Architecture, Data Development and Architecture, UI/UX Development, and Dashboard Development, disciplines rarely integrated into one seamless process. Our methodology brings these disciplines together in an easy-to-understand step-by-step approach to help organizations build solutions to monetize their data assets.

Some of the benefits you will receive from this book include:

Turning information assets into revenue-generating strategies

Providing a guided experience for the manager that helps reduce noise and cognitive bias

Making your organization more competitive through analytical solutions centered on monetization strategies linked to your organizational objectives

Turning your analytics into actionable tactics versus simply “reading the news”

Monetizing your data to drive revenue and reduce costs

This book is not about selling your internal data to other companies or consumers. Nor is it a deep dive into each of the various disciplines. Rather, we provide you with an overview of the various disciplines and the techniques we use most often to build these solutions.

For Andrew, one of your authors, the process of building monetization solutions started in 2003 when he was the Director of Business Intelligence at Capital One. The standard of that era was to provide analytics that were informational in nature. Whether the reporting was for marketing or operations, the information was automated with the gathering, grouping, and aggregating of data into a few key metrics displayed on a report. What Andrew did not know then, was that these reports lacked the intelligence and diagnostic framework to yield action. During this era, the solutions he developed were assigned an economic value to the analysis as a whole, but not to each individual action to drive quality decisions. Over the past decade, he has worked to refine the analytical solutions brought to his clients that have culminated in many of the methods and techniques prescribed in this book.

Kathy, your other author, over her many years in business planning and forecasting, was continually frustrated by the inability to trace business issues to their root cause. The high cost of IT infrastructure at the time constrained the delivery of analytic information through reporting systems that aggregated the data, losing the ability to explore the character and relationships of the underlying transactional data. She began her journey through the wonderful world of big data in 2009 when she signed on to help the Telecommunications Services of Trinidad and Tobago (TSTT) develop a strategic analytics system with the goal of integrating transactional data into business planning processes. Through this assignment, Kathy learned the power of data visualization tools, like Tableau, that connect managers and analysts directly to the data, and the importance of developing analytic data marts to prevent frustrating dead-ends.

Over the course of the past several years, both Kathy and Andrew have worked together to build a variety of solutions that help companies monetize their data. This includes solutions ranging from large Fortune 500 companies to businesses that have under $100 million in revenue. When we first started tackling this problem, one of the key challenges we noticed was the siloed approach to the development and distribution of analytic information. The analyst was using a spreadsheet to do most of their analytical work. The data scientist was working on bigger analytical problems using advanced statistical methods. The IT team was worried about distributing enterprise reports to be consumed by hundreds or thousands of users. Small analytical projects that often lead to the biggest returns for the organization would fall into the gaps between the silos, unable to compete for organizational attention.

As we were building our solutions, we noticed several gaps in the current methods and tools, which led us to develop our own methodology building from the best practices in these various disciplines. One gap that is being closed by new tools is the easier access to data for managers. Where in the past, if a manager wanted to build an analytical solution, they were often limited to analysis in MS Excel or standing up an IT project, which could be lengthy and time consuming, today, data visualization and analysis tools such as Tableau, QlikView, and Power BI give the average business user direct access to a greater volume and scope of data with less drain on IT resources.

This move toward self-service analytics is a big trend that will continue for the next several years. Much of the IT role will transition to enterprise scale analytics and building data environments for analysis. This new paradigm will allow for faster innovation as analysts become empowered with new technology and easier access to data.

As the tools have gotten better and business users have direct access to more information than ever before, they are encountering the need to be aware of and deal with data quality issues masked by the cleansed reporting solutions they accessed in the past. Users must now learn data cleaning techniques and the importance of maintaining data standards and data quality.

One benefit that has come with the increased capabilities of these tools is better User Interface (UI) and User Design (UX) functionality. The usability of an analytical solution is often dictated by the ability to understand and interface with the data. We see prettier dashboards now, but not necessarily geared toward usability or guiding someone through a story. As more analysts and managers begin creating their own reporting solutions, they often build an informational solution that helps them “read the news” versus building a diagnostic to help them manage to a decision that drives action.

Another gap we noticed centers around Data Science and Decision Theory, which are not well deployed in analytical solutions. We began integrating these disciplines into our practice several years ago and they are now integral components. These techniques include: choice architecture, understanding cognitive bias, decision trees, cluster analysis, segmentation, thresholds, and correlations.

Few solutions provide monetization strategies allowing the manager to weigh the economic value tradeoffs of various actions. In adding this method to our solutions, we noticed a considerable uptick in quantifiable value we delivered to our clients and an increase in usage of these analytical solutions.

Closing these gaps and putting it all together was a process of trial and error. Some things worked in some situations and not others while some things we tried did not work at all. After several iterations, we believe our methodology is ready for broader consumption. It is truly unique in that it brings together a varied set of disciplines and best practices to help organizations build analytical solutions to monetize their data. We humbly share our experience, tools, methods, and techniques with you.

Acknowledgments

We owe a large measure of gratitude to everyone who has helped contribute to the development of this book and to those who have helped us along our life's journey.

Thank you, Michael Andrews, for welcoming us into your store, walking us through the business of Michael Andrews Bespoke, and serving as an outstanding case study. The way you strive for excellence and provide white-glove customer service is an inspiration to all of us.

Thank you to Amanda Hand, Lloyd Lay, and Jeff Forman for your assistance in developing and editing several of the chapters and conducting the survey. Your guidance and counsel was invaluable.

Thank you to Jason Reiling, Doug McClure, Alex Clarke, Dev Koushik, Alex Durham, and countless others who participated in the interview and survey process. We appreciate the time and energy that you gave to help us understand the current environment and issues that you are encountering.

Bill Franks and Justin Honaman, thank you for your advice and wisdom in the book-writing process and opening up your networks to provide us with an insider's perspective on what it takes to write a great book. In addition, many thanks to the team at Wiley for taking a leap of faith in us.

We would like to thank many of our clients, including: The Coca-Cola Company, The Home Depot, RGA, Grady Hospital, AT&T, TSTT, Genuine Parts Company, Carters, Cox, Turner, SITA, and Macys. We would like to give special thanks to the team at IHG for their support: Quentin, Alex, Tae, Ryan, Jia, Michelle, Ivy, Lisa, Joe, and many others.

Kathy would like to say a few words:

None of us achieve anything of import alone. In the immortal words of John Donne, “No man is an island.” And so, in writing this book, I, too, stand on the shoulders of those who went before me, those who mentored me and encouraged me to do my best, to strive for more, to find my own way in the world. It is impossible to name everyone whom I have traveled with but I remember each and every one in my thoughts. I would like to mention a few who have been particularly helpful in my journey. I would like to thank my mentors, AJ Robison, Kinny Roper, John Hartman, Robert Peon, Carl Wilson, Trevor Deane, Linda McQuade, and Stuart Kramer, who believed in me, saw my potential, and invested in my development. I would like to thank my loving husband, Fuling Chiang, who has stood by me from the beginning and makes my coffee every morning. And finally, I would like to thank my children, Sean and Christine, who lovingly accepted their fate with a working mom without complaining.

In addition, Andrew would like to thank the following people:

Thank you to my fellow members of Young Presidents Organization for igniting a spark that gave me the idea and confidence to write a book and the invaluable friendship and advice I received from so many of you. Thank you to Aaron Edelheit and JP James for being an inspiration that anything is possible.

Thank you to the entire Aspirent team for your expertise and hard work every day to deliver outstanding solutions to our clients. In addition, thank you for your help in writing this book and creating our monetization website and collateral.

Thank you to my family, Diana, Jen, Rick, April, Ada, Ayden, Adley, and Wanda. And finally, and most importantly, thank you to Suzannah for supporting me during the many nights and weekends that it took to write this book. I appreciate your loving patience and understanding.

About the Authors

Andrew Roman Wells is the CEO of Aspirent, a management consulting firm focused on analytics. He has extensive experience building analytical solutions for a wide range of companies, from Fortune 500s to small nonprofits. Andrew focuses on helping organizations utilize their data to make impactful decisions that drive revenue through monetization strategies. He has been building analytical solutions for over 25 years and is excited to share these practical methods, tools, and techniques with a wider audience.

In addition to his role as an executive, Andrew is a hands-on consultant, which he has been since his early days building reporting solutions as a consultant at Ernst & Young. He refined his craft in Silicon Valley, working for two successful startups focused on customer analytics and the use of predictive methods to drive performance. Andrew has also held executive roles in industry as Director of Business Intelligence at Capital One where he helped drive several patented analytical innovations. From consulting, to startup companies, to being in industry, Andrew has had a wide variety of experience in driving growth through analytics. He has built solutions for a wide variety of industries and companies, including The Coca-Cola Company, IHG, The Home Depot, Capital One, Wells Fargo, HP, Time Warner, Merrill Lynch, Applied Materials, and many others.

Andrew lives in Atlanta with his wife, Suzannah, and he enjoys photography, running, and international travel. He is a co-owner at Michael Andrews Bespoke. Andrew earned a Bachelor's degree in Business Administration with a focus on Finance and Management Information Systems from the University of Georgia.

Kathy Williams Chiang is an established Business Analytics practitioner with expertise in guided analytics, analytic data mart development, and business planning. Prior to her current position as VP, Business Insights, at Wunderman Data Management, Ms. Chiang consulted with Aspirent on numerous analytic projects for several multinational clients, including IHG and Coca Cola, among others. She has also worked for multinational corporations, including Telecommunications Systems of Trinidad and Tobago, Acuity Brands Lighting, BellSouth International, and Portman Overseas.

Ms. Chiang is experienced in designing and developing analytic tools and management dashboards that inform and drive action. She is highly skilled in data exploration, analysis, visualization, and presentation and has developed solutions in telecom, hospitality, and consumer products industries covering customer experience, marketing campaigns, revenue management, and web analytics.

Ms. Chiang, a native of New Orleans, holds a Bachelor of Science in Chemistry, summa cum laude, with University honors (4.0), from Louisiana State University, as well as an MBA from Tulane University and is a member of Phi Beta Kappa and Mensa. Among the first wave of Americans to enter China following normalization of relations, Ms. Chiang lived in northeast China under challenging conditions for two years, teaching English, learning Mandarin Chinese, and traveling extensively throughout China. Over her career, she has worked in the United States, Caribbean, UK, Latin America, and China.

SECTION I

INTRODUCTION

CHAPTER 1Introduction

The explosion of information is accelerating. This can be seen in our everyday use of emails, online searches, text messages, blog posts, and postings on Facebook and YouTube. The amount of data being created and captured is staggering. It is flooding corporate walls and is only getting worse as the next big explosion is already upon us, the Internet of Things, when our machines talk to each other. At this point, the rate of information growth may go exponential. In his article for Industry Tap, David Russell Schilling explained the theory behind futurist Buckminster Fuller's “Knowledge Doubling Curve.”

…until 1900 human knowledge doubled approximately every century. By the end of World War II knowledge was doubling every 25 years. Today…human knowledge is doubling every 13 months. According to IBM, the buildout of the “internet of things” will lead to the doubling of knowledge every 12 hours.

According to Gartner, as many as 25 billion things will be connected by 2020. As we try to make sense of this information, of what Tom Davenport calls the “analytics of things,” we will need methods and tools to assimilate and distill the information into actionable insights that drive revenue. Having these troves of information is of little value if they are not utilized to give our companies a competitive edge. How are companies approaching the problem of monetizing this information today?

One approach that gets inconsistent results, for instance, is simple data mining. Corralling huge data sets allows companies to run dozens of statistical tests to identify submerged patterns, but that provides little benefit if managers can't effectively use the correlations to enhance business performance. A pure data-mining approach often leads to an endless search for what the data really say.

This is a quote from the Harvard Business Review article, “Making Advanced Analytics Work for You,” by Dominic Barton and David Court. This idea is further reinforced by Jason Reiling, Group Director of Trade Capability at The Coca-Cola Company, who commented, “If we don't link the business use of the data with the hypothesis and overall objective, we find situations where the data is guiding the analysis, versus the business guiding the data.” This sums up one of the biggest challenges that exist in analytics today: organizations are throwing data at the problem hoping to find a solution versus understanding the business problem and aligning the right data and methods to it.

What begins to matter more at this point is not necessarily the amount of data, but the ability to codify and distill this information into meaningful insights. Companies are struggling with this issue due to lack of integrated methods, tools, techniques, and resources. If they are able to solve this challenge, they will have a clear competitive advantage. However, this only solves part of the problem; even with the most relevant information, companies are mired in poor decision making.

Decisions

The ultimate goal of collecting and synthesizing this information is to provide insights to executives and managers to make better decisions. Decisions are at the heart of your business and the most powerful tool most managers have for achieving results. The quality of the decisions will directly impact the success of your organization. It is no longer acceptable to equip organizational leaders, managers, and analysts with one-off training courses and conferences, expecting them to make quality decisions based on limited knowledge and gut feel. They have more information coming at them than ever before. Distilling the flood of information into actionable decisions that your organization can monetize is the new challenge.

Unfortunately, simply distilling the information is not enough. There are various ways we undermine our ability to make quality decisions, from decision fatigue to cognitive bias. One way to improve decision making is by using best practices and the collective wisdom of the organization. However, this practice is not widely implemented. In a study by Erik Larson of over 500 managers and executives, they found that only 2 percent apply these best practices when making decisions. Furthermore, even fewer companies have solutions in place to improve decision making.

When executives are not applying best practices or data to make a decision, more often than not they are relying on their intuition or “gut.” This type of decision making is riddled with flaws and often brings in cognitive biases that influence choice. A cognitive bias is a deviation from the norm in judgment based on one's preferences and beliefs. For example, confirmation bias is the tendency to look for information that confirms our existing opinions and thoughts. These biases distort our judgment and lead to errors in choice.

Another culprit of poor decisions is the hidden influences that can affect our decisions, such as mood. For example, let's take a decision about staffing between two field managers in two different locations. Whom to hire, when to hire someone, when to let someone go are all decisions they make based on little data and not much coaching. The decisions between two managers can vary to a large degree based on years and type of experience, mood on that particular day, and other factors that may be occurring in their life at that moment. These two individuals are likely to make different decisions on staffing even when presented with identical circumstances. This type of discrepancy in decision making is what the authors of “Noise: How to Overcome the High, Hidden Cost of Inconsistent Decision Making” call noise.

The problem is that humans are unreliable decision makers; their judgments are strongly influenced by irrelevant factors, such as their current mood, the time since their last meal, and the weather. We call the chance variability of judgments noise. It is an invisible tax on the bottom line of many companies.

The prevalence of noise has been demonstrated in several studies. Academic researchers have repeatedly confirmed that professionals often contradict their own prior judgments when given the same data on different occasions. For instance, when software developers were asked on two separate days to estimate the completion time for a given task, the hours they projected differed by 71%, on average. When pathologists made two assessments of the severity of biopsy results, the correlation between their ratings was only .61 (out of a perfect 1.0), indicating that they made inconsistent diagnoses quite frequently.

Along with noise, another impediment to decision making is decision fatigue. Decision fatigue is the deteriorating quality of your ability to make good decisions throughout the course of a day of making decisions. For example, scientists Shai Danziger, Jonathan Levav, and Liora Avnaim-Pesso studied 1,112 bench rulings in a parole court and analyzed the level of favorable rulings throughout the course of the day. The study found that the ruling started out around 65 percent favorable at the beginning of the day and by the end of the day was close to zero. Their internal resources for making quality decisions had been depleted through fatigue as the day wore on, resulting in less favorable rulings by the end of the day

Another challenge for decisions is company size. “Internal challenges of large organizations are big barriers to decision making” according to an executive who runs analytics for a Fortune 50 company. She commented that it can take 1.5 years to get an insight to market due to the level of effort associated with disseminating the information throughout a large matrixed environment. The number of hops in the decisioning process impedes speed to market along with the degradation of the original intent of the decision.

How do we solve for these factors that influence our ability to make a quality decision? One way is to automate all or part of the decision process. Later on in their article, “Noise,” the authors state:

It has long been known that predictions and decisions generated by simple statistical algorithms are often more accurate than those made by experts, even when the experts have access to more information than the formulas use. It is less well known that the key advantage of algorithms is that they are noise-free: Unlike humans, a formula will always return the same output for any given input. Superior consistency allows even simple and imperfect algorithms to achieve greater accuracy than human professionals.

Our approach to driving the quality of the decisions higher in your organization is to create embedded analytical solutions to help managers make data-driven decisions of monetary value that generate action for their organization. There is an abundance of evidence to support our approach. In a study performed by Andrew McAfee and Erik Brynjolfsson, they found that “companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.”

Analytical Journey

Companies are at various stages in their analytical journey, with different levels of capabilities to develop analytical solutions. Over the past 10 years, companies have invested in building teams and leveraging tools to drive insights for a competitive advantage. Those that have progressed furthest are reaping the rewards.

A study on the maturity of analytics inside companies performed by the Harvard Business Review Analytics Services team found that “more than half the respondents who described their organizations as best-in-class also say their organizations' annual revenue has grown by 10 percent or more over the last two years. In marked contrast, a third of the self-described laggards say their organizations have seen either flat or decreasing revenues.”

Study after study is finding similar results; companies that leverage data to drive the performance of their organization's decisions are winning at a faster rate than their competition. However, the technology behind most analytical applications is still nascent and lacks the functionality to deliver a complete solution. In an article by Harvard Business Review Analytics Services team, “Analytics That Work: Deploying Self-Service and Data Visualizations for Faster Decisions,” they found in a survey of over 827 business managers that there is a sense of frustration with the lack of tool capabilities.