Leading in Analytics E-Book

Joseph A. Cazier

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: SAS Institute Inc
• Sprache: Englisch

Seitenzahl: 669

Veröffentlichungsjahr: 2023

Beschreibung

A step-by-step guide for business leaders who need to manage successful big data projects Leading in Analytics: The Critical Tasks for Executives to Master in the Age of Big Data takes you through the entire process of guiding an analytics initiative from inception to execution. You'll learn which aspects of the project to pay attention to, the right questions to ask, and how to keep the project team focused on its mission to produce relevant and valuable project. As an executive, you can't control every aspect of the process. But if you focus on high-impact factors that you can control, you can ensure an effective outcome. This book describes those factors and offers practical insight on how to get them right. Drawn from best-practice research in the field of analytics, the Manageable Tasks described in this book are specific to the goal of implementing big data tools at an enterprise level. A dream team of analytics and business experts have contributed their knowledge to show you how to choose the right business problem to address, put together the right team, gather the right data, select the right tools, and execute your strategic plan to produce an actionable result. Become an analytics-savvy executive with this valuable book. * Ensure the success of analytics initiatives, maximize ROI, and draw value from big data * Learn to define success and failure in analytics and big data projects * Set your organization up for analytics success by identifying problems that have big data solutions * Bring together the people, the tools, and the strategies that are right for the job By learning to pay attention to critical tasks in every analytics project, non-technical executives and strategic planners can guide their organizations to measurable results.

Leseprobe

Praise for Leading in Analytics

“Advanced analytics helps turn data into intelligent insights for better decision‐making. Reading the hype may convince you that you need it, but does not help you to realize it. To move from concepts to real‐world implementations, start by studying Leading in Analytics. Then build your team and have them study Leading in Analytics.”

—Karl Kempf, PhD, senior fellow and director of Decision Engineering, Intel Corporation

“In an era when analytics is creating more value for companies than ever before, Cazier creates a blueprint for a successful analytics career. A must‐read book for anybody working with analytics to drive company value.”

—Patrick Getzen, retired chief data and analytics officer, BCBS of NC

“Leading in Analytics is a must‐read for business executives who are ready to move toward successful analytic projects. This easy‐to‐follow guide is devoted to sharing practical information that will help you construct a road map to success for your analytic projects. These concepts are further illustrated through interesting real‐life examples from a long list of executives with an impressive track record. Increase the likelihood of success for your next project by following these same methods as described in Leading in Analytics!”

—Paige Valentine, senior director, SAS

“This knowledge is exactly what I have needed for a long time in regards to running Liferithms. The information has already added priceless value to my approach to running my companies and showed me how to prioritize analytics. It is easy to get lost in the possibilities of what can be done with data; now I have a clearer picture of what should be done and a head start on how to get it done.”

—Olu Ogunlela, founder and CEO, Liferithms

“As an executive with just enough knowledge of analytics to be dangerous, I wish I had this book 20 years ago. Seeing, and acting on, the big picture as outlined in Leading in Analytics would have taken us to levels of success we did not know were possible.”

—D. Terry Rawls, former university president, entrepreneur

“I have been coding analytics for over a decade and have now reached a point in my life when I want to shift from an analytics coder to an analytics leader, and I found this book a very practical guide for this purpose.”

—Olim Atabayev, data engineer, Allstate Insurance

“As a young analytics professional, this book was a very eye‐opening experience for me. In the past, my understanding of analytics was focused on low‐level concepts such as probability distribution, p‐values, and Python. However, this book has provided me with a fresh and insightful perspective on analytics, focusing on people, leadership, and impact.”

—Ting Jennings, data analyst at PwC

“Cazier takes the reader on a journey far beyond the theories of analytics. he breaks down every aspect of application of methods, techniques, behaviors, historical background, and even the subject of ethics through responsibility. Through his step‐by‐step approach he calls tasks, he sets the stage with Task 0, where he explains the sense of urgency in the professions who uses analytics, failure rates across industries, and the roots of failure, all while citing some of the most distinguished people in the field. This book is a useful guide for me personally, and will be for thousands of others for years to come.”

—Darren Long, USAF (ret.) and advisory consultant at MSS BTA.

“Through this book, you not only learn the end‐to‐end analytics process but can also discover how to optimize your efforts to bring about value‐driven changes.”

—Jabari Myles,senior data scientist, MetLife

“In a perpetual asymmetric battlefield of analytics, Professor Cazier delivers The Art of War for the digital generals of the tomorrow.”

—Sai Pranav Kollaparthi, MS‐ISM student, Arizona State University

“I am absolutely thrilled to witness the long‐awaited publication of Leading in Analytics, masterfully crafted by Professor Cazier. Taking the readers on a captivating journey through the intricacies of success, this remarkable guide serves as a treasure trove for those eager to harness the potential of data analytics and emerge as leaders in the field. With a wealth of practical knowledge and methodologies curated from the invaluable experiences of industry pioneers, this book illuminates the path to embrace a data‐driven future and equips readers with the essential tools to excel in the ever‐evolving realm of data analytics.”

—Keerthana Bandlamudi, MS‐ISM student, Arizona State University

Wiley and SAS Business Series

The Wiley and SAS Business Series presents books that help senior level managers with their critical management decisions.

Titles in the Wiley and SAS Business Series include:

The Analytic Hospitality Executive: Implementing Data Analytics in Hotels and Casinos

by Kelly A. McGuire

Analytics: The Agile Way

by Phil Simon

The Analytics Lifecycle Toolkit: A Practical Guide for an Effective Analytics Capability

by Gregory S. Nelson

Anti‐Money Laundering Transaction Monitoring Systems Implementation: Finding Anomalies

by Derek Chau and Maarten van Dijck Nemcsik

Artificial Intelligence for Marketing: Practical Applications

by Jim Sterne

Business Analytics for Managers: Taking Business Intelligence Beyond Reporting (Second Edition)

by Gert H. N. Laursen and Jesper Thorlund

Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning

by Michael Gilliland, Len Tashman, and Udo Sglavo

The Cloud‐Based Demand‐Driven Supply Chain

by Vinit Sharma

Consumption‐Based Forecasting and Planning: Predicting Changing Demand Patterns in the New Digital Economy

by Charles W. Chase

Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS

by Bart Baesen, Daniel Roesch, and Harald Scheule

Demand‐Driven Inventory Optimization and Replenishment: Creating a More Efficient Supply Chain (Second Edition)

by Robert A. Davis

Economic Modeling in the Post Great Recession Era: Incomplete Data, Imperfect Markets

by John Silvia, Azhar Iqbal, and Sarah Watt House

Enhance Oil & Gas Exploration with Data‐Driven Geophysical and Petrophysical Models

by Keith Holdaway and Duncan Irving

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection

by Bart Baesens, Veronique Van Vlasselaer, and Wouter Verbeke

Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards (Second Edition)

by Naeem Siddiqi

JMP Connections: The Art of Utilizing Connections in Your Data

by John Wubbel

Leaders and Innovators: How Data‐Driven Organizations Are Winning with Analytics

by Tho H. Nguyen

On‐Camera Coach: Tools and Techniques for Business Professionals in a Video‐Driven World

by Karin Reed

Next Generation Demand Management: People, Process, Analytics, and Technology

by Charles W. Chase

A Practical Guide to Analytics for Governments: Using Big Data for Good

by Marie Lowman

Practitioner's Guide to Operationalizing Data Governance

by Mary Anne Hopper

Profit from Your Forecasting Software: A Best Practice Guide for Sales Forecasters

by Paul Goodwin

Project Finance for Business Development

by John E. Triantis

Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning

by Terisa Roberts and Stephen J. Tonna

Smart Cities, Smart Future: Showcasing Tomorrow

by Mike Barlow and Cornelia Levy‐Bencheton

Statistical Thinking: Improving Business Performance (Third Edition)

by Roger W. Hoerl and Ronald D. Snee

Strategies in Biomedical Data Science: Driving Force for Innovation

by Jay Etchings

Style and Statistics: The Art of Retail Analytics

by Brittany Bullard

Text as Data: Computational Methods of Understanding Written Expression Using SAS

by Barry deVille and Gurpreet Singh Bawa

Transforming Healthcare Analytics: The Quest for Healthy Intelligence

by Michael N. Lewis and Tho H. Nguyen

Visual Six Sigma: Making Data Analysis Lean (Second Edition)

by Ian Cox, Marie A. Gaudard, and Mia L. Stephens

Warranty Fraud Management: Reducing Fraud and Other Excess Costs in Warranty and Service Operations

by Matti Kurvinen, Ilkka Töyrylä, and D. N. Prabhakar Murthy

For more information on any of the above titles, please visit

www.wiley.com

.

Leading in Analytics

The Seven Critical Tasks for Executives to Master in the Age of Big Data

By Joseph A. Cazier, PhD, CAP

Copyright © 2024 by John Wiley and Sons, Inc. All rights reserved.

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

Published simultaneously in Canada.

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To my wife and children in the hope that better, and more responsible, analytics will help build a better world for us all.

Foreword

Another book on analytics. Do we need one? Well, there is no doubt that every organization in every industry today is on a journey to increase their knowledge, skills, and abilities to leverage data for higher value. Some are early on in their analytics journey, trying to achieve higher value by leveraging data with descriptive and diagnostic analytics to increase their hindsight of what has happened so that they can better make sense of current situations for decision‐making. Others have been on the journey for a while now and are leveraging data with predictive and prescriptive analytics to forecast what may happen and how they can best position their products and services for the future—and maybe even create the future, to some extent.

Where are you, your team, and your organization on this journey? Are you early on in the journey, perhaps serving as the only person in the data analytics and business intelligence unit of your company—the person hired to manage Big Data and find yourself on a daily basis helping the leaders of your organization understand the difference between their elbows and eigenvalues and explaining the critical, often time‐consuming, nature of data cleansing as you dredge the organization's data lakes and data puddles and data streams for helpful insights into customer behaviors and untapped market possibilities?

Or are you part of a slightly larger team debating the nuanced advancements of the latest software and tools that help manage data variety, velocity, veracity, and volume so that you can help the business achieve real value? Regardless of your current location on the journey, no doubt you are looking to get better and better and better. The companies you and I work for, and all other companies for that matter, share that common goal. We are each on a journey looking to help our organization mature its data analytics capability.

Whether you are a business analyst, data engineer, chief data officer, business intelligence manager, data analytics translator, statistician, Big Data dude, giga data gal, numbers nerd, or go by some other title, you are leading your organization in analytics and this book is for you. Dr. Joseph Cazier has given us all a gift with Leading in Analytics. This is not just another book about probability distributions, p‐value calculations, or R versus Python. This is a leadership book that doubles as a compass to navigate our journey in data management, science, and analytics.

As you dive into the content, you will immediately recognize Cazier is an optimist. He is focused as much on helping data scientists better understand the art of leadership as he is on helping business leaders better understand the science of data analytics. His work here has a 10‐to‐1 ratio equivalent to business‐leader‐to‐data‐scientist, which is representative of the work carried out in any organization. He knows that for every one data scientist in an organization there are (at least) 10 business leaders all of whom need to increase their individual and collective capacity to convert data into value.

The urgency of this need is easily found in any rudimentary search on the success rates of data analytics projects. Your search findings, as well as your own experience, and mine, too, will spotlight a need for improvement. Indeed, most data analytics projects fail or struggle to yield intended value, with sources citing a failure rate reaching almost 90%. Want to be part of the minority—the 10%—success rate with your analytics projects? Read this book and apply the recommendations from Cazier.

Our data analytics work is hard, and it is getting harder, which makes this book as informative as it is timely. Our project failure rates are clear. We can do better. Cazier helps us recognize critical points of failure and how to mitigate them so that we are on the minority side (the winning side) of the success/failure equation. The concepts he outlines are clear, logical, and immediately actionable to navigate the challenges and issues that the teams in my organization and all others I have worked alongside have been wrestling to resolve. They are the same challenges and issues you are wrestling to resolve, too.

Cazier balances content that is well researched with his own firsthand experience as a data scientist. And, it is not just his research and experience. More than three dozen experts materially contributed their time, wisdom, and the best practices that have helped them succeed to the contents of this book. These experts come from all levels and types in organizations who are engaged with furthering analytics success. From seasoned c‐suite executives to rising analytics professionals, you will hear perspectives and advice from the technical, analytics, business, and management sides of the organization along with accomplished consultants.

So, beyond Cazier's insights and experiences, you will learn from dozens of experts, each of whom helped to change my thinking and my approach to data analytics—in all the right ways; so much so, I built the Leading in Analytics academy on the foundation of their wisdom. I have seen firsthand the growth and positive reactions from leaders everywhere as they heard from many of these experts, in their own words, how best to lead in analytics. Now, it is all here, distilled in one place, so that you can reference it clearly and easily from anywhere, while diving deeper into the content with richer detail and tools to help you along the way.

I have known Joseph for years. We are colleagues, collaborators, business partners, and friends. He is passionate about leveraging data for good—helping people learn in the classroom and in the boardroom so that they, in turn, can help provide better products and services to their customers and for the benefit of the communities in which we live. As noted, this book is a gift. The task Cazier has carried out so diligently—researching and writing this book—has not been easy. As digital transformation continues to cascade across all aspects of our work (and lives), data becomes increasingly important; it is the new oil, and to mine it appropriately requires the science of great analytics and the art of great leadership. This is not an academic tome, but rather a compass to help us navigate the complexity of our journey.

Another book on analytics. Do we need one? Yes, this one!

Tim Rahschulte, PhDChief executive officerProfessional Development Academy

Acknowledgments

First, thanks to my wife and family for supporting me on this three‐year‐plus journey to research and write this book, with getting up before the sunrise and staying up well after sunset to research, write, and analyze the wisdom shared by our expert contributors. I could not have done it without you and your support, advice, and nurturing along the way. Mom, dad, sister, brothers, kids all also played an important role and I will always be grateful.

Second, thank you, Dr. Karl Kempf, for being the inspiration for this book and graciously sharing your knowledge and wisdom with me, making this journey possible. To learn more about Kempf's inspirational legacy, please visit the Afterword at the end of this book.

Third, thank you to my cousin, brother, and friend, Wayne Thorsen, for involving me in his internet start‐up 25 years ago and inspiring me to dig deeper into the technology sector and all it has to offer. It has been an exciting journey. Thank you, Wayne, for that first push that got this journey started.

Fourth, thank you to the more than three dozen experts who willingly and thoughtfully gave their time and wisdom to share their best practices, and especially to Dr. Terry Rawls and Dr. Tim Rahschulte, who were both there every step of the way as we interviewed, digested, and documented their wisdom, which makes this book, and the companion Leading in Analytics course offered by Rahschulte at PDA, so rich and valuable.

Fifth, thank you to the many mentors I have had along the way. There are too many to list all of them, but Ms. Artaburn, Ms. Bendixon, Dr. Mel Cambell, Uncle Andy Cazier, Uncle Ben Cazier, Mr. Jim Chesterfield, Mr. Noel Commeree, Dr. Doug Dean, Dr. Bill Dean, Dr. Steve Eskelson, Mr. Fletcher, Dr. Paul Godfrey, Jeff Mason, Ms. Meek, Mr. Jim McLean, Mr. Morash, Mr. Ogden, Dr. Benjamin Shao, Dr. Scott Smith, Dr. Robert St. Louis, Aunt Susan Thorsen, Mr. Wishkoski, Dr. Martin Wistensen, Dr. Warner Woodruff, and many others all played leading roles in guiding my education in important ways.

Finally, thank you to the many reviewers and helpers in writing and reviewing this book, including Olim Atabayev, Keerthana Bandlamudi, Dr. Carrie Beam, Shaun Doheney, LeAnne Hill, Ting Jennings, Sai Kollaparthi, Preston MacDonald, Jabari Myles, Rocco Pagano, Dr. Tim Rahschulte, Dr. Terry Rawls, Richard Rogers, Sam Volstad, Dr. Wendy Winn, Paige Wright, and others. Included in this list are several former students who were willing to listen to their professor, and fix his mistakes, one last time.

To all of you here, and dozens of others not mentioned by name, you are here in the knowledge and help you shared along the way. THANK YOU ALL!!!

Introduction: The Last Analytics Mile

THE LAST MILE TO ANALYTICS SUCCESS

Analytics became widely known and accepted as a competitive imperative in 2006 when Thomas Davenport published his landmark article, “Competing on Analytics,” which soon became one of the top‐10 must‐read articles in the history of Harvard Business Review.1 Analytics had always been helpful, but as long as your competitors were not using it, you had a chance to survive without it, too.

Now that analytics has become affordable and practical to do at scale, everyone is doing it, and so must you if you wish to survive in the new age of Big Data, and the intense competitive pressure brought on by those who know how to “compete on analytics” well. Ahmer Inam, chief data and AI officer at Relanto, painted a stark picture of the competitive landscape when he said businesses in today's world must “do analytics or die.”2

Most businesses know the importance of using analytics, prompting them to invest more than $100 billion by 2018,3 which shows what was collectively spent by organizations to take advantage of the power of analytics. Unfortunately, close to $90 billion of those funds missed the mark by failing to generate the expected return on investment (ROI). That is right: nearly 90% of all analytics projects failed to generate “significant financial benefit,” according to a report that MIT released in 2020.4 No matter how good we analytics professionals are at building models, if they are not adopted and integrated into the organization in a way that creates value, they will be perceived to be failures.

In some ways this failure rate is to be expected, because all young disciplines fail in the beginning as they learn to walk. Analytics is, in fact, still a very young discipline, and one that is changing more rapidly than nearly any other, so it is understandable that the failure rate is so high. Understandable, yes, but it is still unacceptable, and is a tragic waste of resources that could be used much more effectively if analysts and business leaders were able to more effectively work together to manage and avoid the preventable, the manageable, causes of analytics failure.

Yes, some projects will always fail, just like some planes still crash and some bridges will still collapse. However, these rates are much less than they were in the beginning. The failure rates in other professions have been dramatically reduced as they have matured and developed a set of professional best practices that taught them and their sponsors how to work together to succeed. This can be true of analytics as well.

Indeed, this must be true for analytics. We must make it true. Analytics is not just about making more money, or even about firm survival in a competitive landscape, as important as those things are. It is also about doing things better, doing them more efficiently, sustainably, and intelligently. It is about, or can be about, with the right moral and ethical practices, building a better society that uses analytics to compete, but also uses it to make our world a better place to live and work.

We know that this concept can be true because a handful of companies such as Capital One, Amazon, and Intel have shown that it can be done well. Even so, in that same MIT report showing only an 11% average success rate of adding significant value, they also reported that some firms had achieved as much as a 73% success rate. That is nearly a sevenfold increase in success and something to which all of us can aspire. Even more, I believe the value created by these successes is far greater than the cost of all of the failures put together, as shown in Figure 0.1.

The firms that have succeeded in achieving these astonishing levels of success with analytics had a few things in common. The most important of which is that they achieved a high level of organizational learning for, with, and on behalf of AI and analytics. They learned from analytics how to change because of it, and they were willing, even eager, to change. Not just among the analytics professionals, but across the entire organization, along with their partners who were able to learn, adapt, and grow to apply and support analytics and did so at an industrial scale across the organization.5

What is the secret to this level of organizational learning about analytics? Certainly investment in people, tools, and technologies. Certainly commitment to do it. Certainly competitive pressure pushing for it. But none of these reasons are enough on their own. It also takes many more analytics supporters and enablers than analytics doers, maybe on the order of 10 to 1, for the organization to learn, grow, and succeed at this much higher level.

To be the type of organization that truly takes advantage of the potential of analytics, one that succeeds much more often than not, the organizational leadership as a whole, not just the analytics professionals, need to know their role in adopting and supporting analytics. They need to develop the skills to work together with the analytics team, and vice versa, to understand the business value of analytics and the critical nontechnical role they have to play in analytics success. They need to become an adaptable learning organization that uses, and is continuously and skillfully driven by, analytics to compete, grow, and improve in their efforts.

Figure 0.1 Many Failures, Some Astonishing Successes

Nontechnical employees need something more than data literacy, but less than coding, to succeed in analytics. They must become fluent in the best practices of analytics, at least the ones that interact with their role and function in and around the organization. Yes, analytics professionals need to also learn to better interact with the business, as has been identified many times in many places, but it will never be enough. Analytics professionals are not capable of doing it on their own. They must be guided and supported by at least an order‐of‐magnitude times as many skillful analytics supporters and enablers to succeed.

This organizational learning should not be confined to the analytics team, though that is part of it, too. Indeed, it cannot be confined to the analytics team if we want analytics to move from the lab into production. There are three core groups of people in the organization who must work together skillfully for analytics success. This is what Dr. Rudi Pleines, head of business transformation at ABB Robotics, calls the minimal viable team needed for analytics to succeed.

This minimal team includes (1) an executive champion to sponsor the project, (2) a business process owner who is able to integrate a tool and the related analytics into the processes of an organization to ensure they are used and the generated value is maximized, and (3) the technical analytics person or team to do the analysis. Notice the technical and analytics teams are necessary, but they cannot succeed on their own. Even working together, it still takes skillful collaboration aimed at overcoming common causes of failure to succeed.

Most of the causes of analytics failure are manageable, wrote Dr. Karl Kempf,6 head of analytics at Intel, whose team is responsible for documented savings exceeding $55 billion from analytics. Manageable means preventable, if you are smart and skilled enough to manage the causes of failure correctly. The analyst has direct control over only one of the five manageable tasks Kempf identified as necessary for analytics success.

This means that the entire organization, including many more non‐analytics professionals than analytics professionals, need to learn how to engage with and support analytics effectively for long‐term analytics growth and success. This community effort is how analytics becomes a profession: by growing beyond a few innovative pioneers into a standard, repeatable, organization‐wide process that can consistently add value.

This is the last mile of analytics: learning to work together to get more projects into production successfully. We, as analytics professionals, cannot walk that last mile alone. It takes all of us, working together, skillfully, to dramatically increase the success rate of analytics and provide value to the organizations we work for and with.

This book is about how you, whoever you are and whatever your function, can more effectively lead, guide, support, and integrate with analytics to build the kind of mature analytics organization that succeeds, not just on a few projects, but on most of them. It is a collection of best practices addressing each of the manageable tasks in analytics, the preventable causes of failure that destroy projects, and how you can use them to compete on analytics as Thomas Davenport advised in his 2006 Harvard Business Review article. It is about how you can help analytics cross that last mile to analytics success and maturity as a profession into a practice of success.

EXPERT CONTRIBUTORS

The knowledge and experience of more than three dozen highly successful experts is condensed and shared as best practices in this book, along with their many stories, illustrating what analytics can do and how to use it. I am grateful they agreed to share some of their time and wisdom in an effort to help build analytics as a practice and profession and increase the analytics success rate.

These experts were all handpicked for this book because they have something unique to offer through their wisdom, insights, and experience at all levels of the analytics process. Some are practicing data scientists, some analytics executives or educators, and still others come from the business side. Some are young and fresh in their careers and others have decades of experience. Some work in deep complex analytics, others more with business intelligence and/or visualizations. Some have backgrounds in technology firms, others in retail, logistics, engineering, or manufacturing. The depth and breadth of their collective knowledge comes together here with the one goal of helping you learn how to lead your analytics efforts successfully. These are the geniuses who made this book possible. See Table 0.1 for a list and description of these contributors.

Table 0.1 List of Expert Contributors

Experts

Background

Hina Arora,

PhD, is a clinical associate professor who teaches analytics at Arizona State University. Previously, Arora was a senior data scientist lead and analytics manager at Microsoft and a software engineer at IBM.

Josh Belliveau

is a principal solution engineer and data science product specialist for Tableau at Salesforce. In this role, Belliveau helps enterprises across a variety of industries find increased business value through their effective use of the analytics ecosystem. Belliveau previously worked for a variety of other technology companies, including Sight Machine, Lavastorm, and Appalachian State University as an analytics program manager.

Anthony Berghammer

is a research data scientist at RTI International. Berghammer previously worked as a senior decision data analyst at USAA and Data Scientist Ernst & Young.

Antonio Rafael Braga

, PhD, is an accomplished data scientist and professor at the Universidade Federal do Ceará in Brazil. Previously, Braga was a doctoral fellow in the Center for Analytics Research and Education at Appalachian State University.

Joseph Byrum,

PhD, is CTO at Consilience AI and is a leader known for delivering results through knowledge‐based solutions. Byrum has demonstrated expertise across traditional domain boundaries to bring new capabilities to Fortune 500‐level companies. Byrum is a frequent thought leadership contributor to a number of publications from

Forbes

,

Fortune

,

Fast Company

, and

TechCrunch

, to

Analytics Magazine

and

ISE Magazine

. Byrum received honors from the Aspen Business & Society Long Term Strategy Group and earned the INFORMS Edelman Prize, DAS Practice Award, ANA Genius Award, and Drexel Lebow Analytics Top 50 MSU CANRAA Outstanding Alumnus Award.

Joseph Cazier,

PhD and CAP, is a clinical professor and associate director of the Center for AI and Data Analytics at Arizona State University. Previously, Cazier served as the executive director of the Center for Analytics Research and Education and as associate dean at Appalachian State University. Cazier also served as chief analytics officer for HiveTracks and as a faculty fellow for the UNC System, leading projects in innovation and analytics.

Joshua Cazier

is the global head of solution and demo engineering at Qualtrics and is one of the founding team members and the fourth active tenured employee. As an analytics executive, Joshua Cazier leads teams and companies to make better decisions with data. Joshua Cazier is also the author's smarter, and better looking, little brother.

Libor Cech

is the former CEO of Chemoprojekt and current board member of Bochemie. Over a long career, Libor Cech has led many analytics and automation projects at companies such as Global Process Automation, Hi‐Grade International Engineering, and GE Infrastructure.

Thomas Cech

is a senior data scientist at National Council for Community and Education Partnerships (NCCEP) and focuses on improving educational outcomes and graduation rates. Thomas is also Libor's son.

Dan Cohen‐Vogel,

PhD, is the principal at DataWorks Partners. Previously, Cohen‐Vogel served as vice president for data and analytics for the University of North Carolina System and assistant vice chancellor for the State University System of Florida.

Bill Disch,

PhD, is an analytics educator for DataRobot. Disch served as chief analytics officer for a variety of firms and teaches statistics at Central Connecticut State University.

Shaun Doheney,

PMP and CAP, is a research scientist in Operations Research and analytics manager at Amazon Web Services (AWS). Previously, Doheney was the chief analytics officer for an Inc. 5000 small business. Doheney leverages experiences from a career as a Marine Corps officer and operations researcher to actively give back to the larger analytics communities.

Grant Fleming

is a senior data scientist at Elder Research. Fleming is also the lead author of

Responsible Data Science

(Wiley, 2021), a guide for data science practitioners and managers on the ethical implications of developing and deploying AI models, and has delivered several talks on the subject.

Bill Franks​​

is the director of the Center for Data Science and Analytics within the School of Data Science and Analytics at Kennesaw State University. In this role, Franks helps companies and governmental agencies pair with faculty and student resources to further research in the area of analytics and data science. Franks is also chief analytics officer for ​the International Institute for Analytics (IIA) and serves on the advisory boards of ActiveGraf, Aspirent, DataPrime, DataSeers, Kavi Global, and Quaeris.

Patrick Getzen

recently retired as the founding chief data and analytics officer for Blue Cross Blue Shield of North Carolina, and served in a variety of roles, including chief actuary. Getzen currently serves on several advisory boards, helping companies with business and analytic strategies as well as organizational management.

Sherrill Hayes

, PhD, is the director of the School of Data Science and Analytics and professor of conflict management at Kennesaw State University. Hayes has more than 20 years of experience in program development and evaluation, working with families, organizations, court systems, and higher education.

LeAnne Hill

is a manager at the accounting and advisory firm FORVIS LLP in the enterprise risk and quantitative advisory practice area. Hill previously led efforts in fraud analytics at American Credit Acceptance and LPL Financial and as a senior associate at Grant Thorton LLP.

David Houser

is the chief revenue officer for ReverseLogix and was previously the EVP and head of the Global Account Program at Koerber AG. From earlier days at Oracle using current BI analytics tools to Houser's current position using BI everywhere tools, Houser has become an industry expert and daily user of advanced analytics to promote more efficient business solutions within the supply chain and now in reverse supply chains.

Ahmer Inam

is currently chief data and artificial intelligence officer at Relanto and has previously served as the head of analytics for Centific, Cambia Health Solutions, Nike, PwC, and Sonic Automotive. Inam's breadth and depth of analytics experience includes contributing to the XPRIZE, World Economic Forum, the Forbes Technology Council, and the International Institute for Analytics.

Piyanka Jain

is the founder and CEO of the data analytics firm Aryng, and the author of the best‐selling data analytics book,

Behind Every Good Decision

. Jain is a well‐known keynote speaker and analytics advisor to several firms and was previously head of analytics at PayPal and analytics manager at Adobe.

Cecil John

is an enterprise architect with 25+ years of experience. John works with the largest organizations in the world to migrate and build secure regulatory‐compliant Azure cloud infrastructures and platforms.

Karl Kempf,

PhD, is the head of analytics at Intel where Kempf's team recently won the Franz Edelman Prize for Impactful Analytics with documented savings for Intel of over $55 billion under Kempf's leadership. Kempf studied math and artificial intelligence before it was cool and used these skills to help Christopher Reeve fly in the 1980s Superman movies, among other notable adventures noted in this book's Afterword. Kempf is also the originator of the five manageable tasks that inspired this book.

Stephen Kimel

is a senior data scientist at Red Hat and former data scientist for NetApp.

Alexandra Koszegi

is a technical sales engineer at D‐Wave Systems Inc. and helps organizations harness the power of quantum computing solutions.

Diego Lopez‐Yse

leads digital projects for Moody's in Latin America. Lopez‐Yse held similar roles at Bayer, PwC, and SAP and is a recognized speaker, writer, and thought leader for data science in Latin America.

Preston MacDonald

is a marketing data scientist at Red Hat and was formerly a senior data analyst for the Wounded Warrior Project.

Lindsay Marshall

is a data scientist and director of data and analytics at Gilbane Building Company. Marshall held similar data science roles at Cisco, SAS, MetLife, and ECRS.

Polly Mitchell‐Guthrie

is the VP of industry outreach and thought leadership at Kinaxis, a supply chain management and analytics software company. Previously, Mitchell‐Guthrie was director of analytical consulting services at the University of North Carolina Health Care System and also worked in various roles at SAS. Mitchell‐Guthrie has an MBA from the Kenan‐Flagler Business School of the University of North Carolina at Chapel Hill and also received a BA in political science. Mitchell‐Guthrie is a member of the Foresight Advisory Board for the International Institute of Forecasters, has been very active in INFORMS (the leading professional society for operations research and analytics), and cofounded the third chapter of Women in Machine Learning and Data Science (there are now more than 100 chapters worldwide).

Jabari Myles

is a senior data scientist at MetLife. Previously, Myles held similar roles at Red Hat and SAS. A few years ago, Myles started giving back by teaching analytics concepts to young college students. After finding tremendous enjoyment from teaching, Myles is shifting careers to earn a PhD to teach, research, and mentor full time.

Olu Ogunlela

is the cofounder and CEO of Liferithms, a tech start‐up using lifestyle data analytics to help people reach their productivity, wellness, relationship, and life goals. Liferithms is advancing humanity's state of wholeness by improving an individual's chances of achieving one goal without compromising their ability to attain all others. The company is accomplishing this objective by aggregating activity and biometric data from users' wearable and connected devices alongside its weekly activity wholeness index, known as Life Score, while users also work with a team of dedicated coaches.

Albert Owusu

is a graduate of the Federal Chief Information Officers' Competencies Program. Owusu has more than 20 years of experience in engineering and information technology with more than 12 years of experience in developing solutions for enterprise management systems and enterprise database management, including extensive experience in IT high availability architecture solutions using Oracle RAC, Data Guard, Streams, and GoldenGate. Owusu has excellent analytical, research, communications, and leadership skills and is customer‐focused and highly motivated.

Chris Pitts

is lead information security engineer with experience leading security teams for TIAA and Duke Energy. Pitts is a certified information systems security professional and certified blockchain expert.

Ruediger (Rudi) Pleines,

PhD, is head of business transformation at ABB Robotics, a leading global technology company with more than 100,000 employees that operates in more than 100 countries. Previously, Pleines worked for more than 15 years at the global management consulting company Kearney, where he helped clients make data‐informed decisions to improve their businesses.

Tim Rahschulte,

PhD, is cofounder and CEO of the Professional Development Academy, former chief learning officer for Evanta, a Gartner Company, and former analytics leader for the state of Oregon.

D. Terry Rawls

, EdD, is a retired university president, administrator, and entrepreneur who practiced data‐driven cultural change everywhere he served. Today, Rawls focuses on bringing new ideas to institutions and organizations worldwide.

Skylar Ritchie

is a data scientist with experience at Boeing, Prestige Financial, and Natural Partners.

Max Rünzel

is cofounder and CEO of HiveTracks, Inc., which is on a mission to build a platform for community‐based and verified sourcing of environmental data. Using analytics, HiveTracks Inc. unlocks the value of a vast, untapped reservoir of environmental data by building a digital, scalable solution that incentivizes local biodiversity monitoring for beekeepers and their bees to improve planetary health.

Steve Stone

has led the transformation of Lowes into a data‐driven firm with rapidly accelerating growth as their CIO. Stone then moved to similar roles with Microstrategy and L Brands as a c‐suite executive focused on delivering bottom‐line results.

Wayne Thompson,

PhD, is the retired chief data science officer for SAS who pioneered many analytics products and projects there. After a short retirement, Thompson jumped back into the action as executive director at JP Morgan Chase, building and managing a team focused on data verification services.

Johann Vaz

has decades of experience as a CIO/CTO for a variety of multimillion‐dollar organizations in the technology, pharmaceutical, and finance industries. Vaz also shares wisdom and experience in the university classroom helping a new generation learn how to effectively use technology.

Sam Volstad

is a data analytics service manager at the national accounting and advisory firm GHJ, with prior analytics experience at Grant Thorton.

Beverly Wright,

PhD and CAP, is head of data science at Burtch Works. Wright brings more than 30 years of experience leading and delivering data science and analytics solutions through corporate, consulting, nonprofit, and academic experiences.

Figure 0.2 Sample Logos From Experts' Organizations

Figure 0.2 provides a graphic representing a few of the organizations these experts have worked for. We thank them for allowing their wisdom to be shared.

NOTES

1

https://www.tomdavenport.com/published-articles/

2

Private interview for this book.

3

M. Colas, I. Finck, J. Buvat, R. Nambiar, and R. R. Singh, ”Cracking the Data Conundrum: How Successful Companies Make Big Data Operational,” Hg. v. Capgemini Consulting (2014).

https://www.tandfonline.com/doi/full/10.1080/10580530.2021.1894515

4

Sam Ransbotham, Shervin Khodabandeh, David Kiron, François Candelon, Michael Chu, and Burt LaFountain, “Expanding AI's Impact with Organizational Learning,”

MIT Sloan Management Review

(October 2020): 1.

5

See

Task 6

: Analytics Maturity for more information about industrial scale analytics.

6

INFORMS,

Analytics Body of Knowledge

(ABOK) (Wiley, 2018), Chapter 2.

TASK 0Analytics Leadership

KNOWLEDGE BEGINS IN FAILURE

The first time we try to do something new, we often fail. Through those failures, we learn what not to do, and through that pain of failure, we feel the desire to do better. Eventually we, or others we know, do something right and succeed. With enough successes, general principles emerge and best practices are developed. When that knowledge is practiced and shared, it grows into a profession with a set of best practices, enabling us to learn to succeed much more often than we fail.

Analytics is a young profession with a very high failure rate. Statistics often cite a rate in the range of 80% to 90% for failure to deliver meaningful value or achieve an expected ROI. Note, this is not necessarily a failure to generate a great insight or to build a model that could be incredibly useful. True, it is sometimes that. However, if that incredibly useful model is not successfully deployed and used to create value for the organization that funded it, it will be regarded as a failure. Indeed, it is a failure, despite all the efforts and insights generated, if it has failed its core mission, the reason for investment: to create value.

It does not need to be this way. As with any profession, the more we learn from our failures and share the wisdom of our successes, the faster the analytics profession grows and matures as it puts the lessons learned into a body of knowledge that can guide us to many more successes than failures.

From building bridges to flying airplanes, most of the professions we know went through a similar arc of high failure when they were new to the development of a set of general principles and best practices that guide a mature profession to a high success rate. Analytics, as a profession, is also on that path. By learning from the pains of failure and applying lessons of success, we too can succeed in analytics.

But we cannot do it alone. Just as every other profession, from engineering to aviation, works with others to guide, scope, and support their projects to realize value, analytics must grow this way as well. Most of the critical causes of failure, and the best practices needed for success, are outside the direct control of the analytics professionals. It takes all of us—analytics professionals and supporters—learning what it takes for analytics to succeed and skillfully working together for that success at scale.

In particular, it takes a minimum of three different groups intently and skillfully working together to achieve analytics success in a business context. This is what Dr. Rudi Pleines, head of business transformation at ABB Robotics, calls the minimal viable team, which we introduced in the Preface. Sure, sometimes more engagement is needed and is generally helpful from others, but without the full support and knowledge to manage and implement analytics of at least this group, projects will not generate the expected value for a business.

The groups, as identified by Dr. Pleines, include these roles:

Executive champion.

Executives have the power to execute on projects or kill them. That makes their role crucial in driving change. Executives must use their power and position to drive adoption of the tools in a lead‐by‐example manner by demonstrating they use these tools and insisting others also make decisions with them. Their primary role is to push the organization over initial barriers and resistance toward new approaches by providing resources, removing roadblocks, communicating the importance of adoption, and then demanding adoption. This strong leadership is necessary to overcome the initial resistance phase that causes many projects to fail.

Business process owner.

Business process owners control how the business operates, carrying out the critical day‐to‐day tasks the business needs to run effectively, and usually supervising many staff members who need to become analytics adopters. Their main role is to help integrate tools and analytics into the processes of an organization so they become part of the daily business and are not introduced as stand‐alone tools with the hope people will use it. They guide the technical team to meaningful data cleaning, sense check and interpret results, developing useful training materials using their unique vocabulary. By doing so, they give analytics a clear purpose and help the entire organization to change and improve. Without their skillful support, most projects will fail.

Technical team.

This is the analytics and IT teams, often working together, who discover, build, and support the actual project. They work with executives to identify high‐value project areas and then hand‐in‐hand with the business process owners to ensure that tools work error‐free, are easy to use, and of the right scope to support the processes in the most effective ways. Hard‐to‐use projects and those with technical errors are like poison to adoption efforts. This makes it critical to work closely with these groups to design an efficient error‐free solution, provide technical support, and provide realistic views on project feasibility, impact, needs, costs, and risks.

Members from each of these groups, and sometimes from others, need to learn to work together toward a common purpose and avoid the most common causes of failure to increase the odds of analytics success. If you are missing participation from any one of these three groups, here is what happens:

Missing technical team.

Project will be desired but remain only a dream without the technical and analytics team to build it.

Missing business process owner.

Risk developing a costly and technically advanced visually appealing stand‐alone system that does not integrate into existing processes and will never get adopted in a way that adds real value.

Missing executive champion.

Risk of failing because of limited funding or stalled implementation due to insufficient implementation authority.

This book contains a collection of best practices, based on failures, including my own, as well as successes from some of the smartest people I know. It contains a set of best practices focused on preventing the seven most common manageable causes of failure, those that can be avoided or prevented with skillful leadership inside and outside the analytics team who are focused on the task of best managing the primary causes of failure. No, it does not contain all of the best practices—no book can, and many are yet to be developed. But I am convinced what this book does contain is valuable and useful and will help our profession grow.

This book will show the executive champions how they can succeed in analytics through leadership by bringing the Pleines's minimal viable team together skillfully and managing failure. It will illustrate the role of business process owners in collaborating with and supporting technical analytics teams and guiding them to what will work. It will show the technical analytics teams how to more effectively work with these groups so more of their projects make it out of the analytics lab and into successful production.

There is no math in this book, no coding or deep technical discussions, just practical advice, stories, and best practices from those who know how to succeed to show how we can all work together toward success. Success means that the potential value from the insights and models built in the analytics process is realized in the firm. It means that what is built is put into practice and that it has the desired measurable impact. It means that value is created and the business earns the ROI they expected. It means that action was taken, goals were met, and the world is better because of your analytics efforts, not just that insights are generated.