1st Edition

It's All Analytics - Part II Designing an Integrated AI, Analytics, and Data Science Architecture for Your Organization

By Scott Burk, David Sweenor, Gary Miner Copyright 2022
    296 Pages 40 B/W Illustrations
    by Productivity Press

    296 Pages 40 B/W Illustrations
    by Productivity Press

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    Up to 70% and even more of corporate Analytics Efforts fail!!! Even after these corporations have made very large investments, in time, talent, and money, in developing what they thought were good data and analytics programs. Why? Because the executives and decision makers and the entire analytics team have not considered the most important aspect of making these analytics efforts successful. In this Book II of "It’s All Analytics!" series, we describe two primary things: 1) What this "most important aspect" consists of, and 2) How to get this "most important aspect" at the center of the analytics effort and thus make your analytics program successful.

    This Book II in the series is divided into three main parts:

    Part I, Organizational Design for Success, discusses ……. The need for a complete company / organizational Alignment of the entire company and its analytics team for making its analytics successful. This means attention to the culture – the company culture culture!!! To be successful, the CEO’s and Decision Makers of a company / organization must be fully cognizant of the cultural focus on ‘establishing a center of excellence in analytics’. Simply, "culture – company culture" is the most important aspect of a successful analytics program. The focus must be on innovation, as this is needed by the analytics team to develop successful algorithms that will lead to greater company efficiency and increased profits.

    Part II, Data Design for Success, discusses ….. Data is the cornerstone of success with analytics. You can have the best analytics algorithms and models available, but if you do not have good data, efforts will at best be mediocre if not a complete failure. This Part II also goes further into data with descriptions of things like Volatile Data Memory Storage and Non-Volatile Data Memory Storage, in addition to things like data structures and data formats, plus considering things like Cluster Computing, Data Swamps, Muddy Data, Data Marts, Enterprise Data Warehouse, Data Reservoirs, and Analytic Sandboxes, and additionally Data Virtualization, Curated Data, Purchased Data, Nascent & Future Data, Supplemental Data, Meaningful Data, GIS (Geographic Information Systems) & Geo Analytics Data, Graph Databases, and Time Series Databases. Part II also considers Data Governance including Data Integrity, Data Security, Data Consistency, Data Confidence, Data Leakage, Data Distribution, and Data Literacy.

    Part III, Analytics Technology Design for Success, discusses …. Analytics Maturity and aspects of this maturity, like Exploratory Data Analysis, Data Preparation, Feature Engineering, Building Models, Model Evaluation, Model Selection, and Model Deployment. Part III also goes into the nuts and bolts of modern predictive analytics, discussing such terms as AI = Artificial Intelligence, Machine Learning, Deep Learning, and the more traditional aspects of analytics that feed into modern analytics like Statistics, Forecasting, Optimization, and Simulation. Part III also goes into how to Communicate and Act upon Analytics, which includes building a successful Analytics Culture within your company / organization.

    All-in-all, if your company or organization needs to be successful using analytics, this book will give you the basics of what you need to know to make it happen.

    Part 1: Designing for Organizational Success

    Chapter 1: Some Say it Starts with Data, It Doesn’t

    Chapter 2: The Anatomy of a Business Decision

    Chapter 3: Trustworthy AI

    Part 2: Designing for Data Success

    Chapter 4: Data Design for Success

    Chapter 5: Data in Motion, Data Pipes, APIs, Microservices, Streaming, Events and More

    Chapter 6: Data Stores, Warehouses, Big Data, Lakes and Cloud Data

    Chapter 7: Data Virtualization

    Chapter 8: Data Governance and Data Management

    Chapter 9: Miscellanea – Curated, Purchased, Nascent and Future Data

    Part 3: Designing for Analytics Success

    Chapter 10: Technology to Create Analytics

    Chapter 11: Technology to Communicate and Act Upon Analytics

    Chapter 12: To Build, Buy, or Outsource Analytics Platform


    Scott Burk has been solving complex business and health care problems for twenty-five years through science, statistics, machine learning and business acumen. Scott started his career, well actually in analytics, as as an analytic chemist after graduating with a double major in biology and chemistry from Texas State University. He continued his education, going to school at night taking advanced courses in science and math at the University of Texas at Dallas (UTD). He then started programming at the toxicology lab where he was working and thus started taking computer science (CS) and business courses until he graduated with a Master’s in Business with a concentration in finance soon after from UTD.

    Texas Instruments (TI) hired him as a financial systems analyst in Semiconductor Group, but due to TI’s needs and Scott’s love of computers, he soon after became a systems analyst for corporate TI. He worked there for three years and started itching to get back to school (even though, he continued to take courses at night (Operations Research and CS) through TI’s generous educational program). TI granted him an educational leave of absence and he went to Baylor University to teach in the business school and get a PhD in statistics. He joined Baylor as a non-tenure track professor teaching Quantitative Business Analysis (today = business analytics).

    After graduating, Scott went back to TI as a Decision Support Manager for the consumer arm of TI (today = consulting data scientist). Where he engaged in many functional areas – marketing and sales, finance, engineering, logistics, customer relations the call center and more. It was a dream job, but unfortunately, TI exited that business.

    Scott joined Scott and White, a large integrated healthcare delivery system in Texas as a consulting statistician. He moved into an executive role as Associate Executive Director, Information Systems leading Data Warehousing, Business Intelligence and Quality Organizations working with clinics, hospitals and the health plan. At the same time, he received a faculty appointment and taught informatics with Texas A&M University. He left, but later came back to Baylor, Scott and White (BSW) as Chief Statistician for BSW Healthplan.

    Scott continued his education, getting an advanced management certification from Southern Methodist University (SMU) and Master’s Degree (MS) in Data Mining (machine learning) from Central Connecticut State University. Scott is a firm believer in life-long learning.

    He also worked as Chief Statistician at Overstock, re-engineering the way they tested and evaluated marketing campaigns and other programs (analytics, statistics). He launched their ‘total customer value’ program. He was a Lead Pricing Scientist (analytics, optimization) for a B2B pricing optimization company (Zilliant) for a number of years. He thoroughly enjoyed working with a rich diverse, well-educated group that affected the way he looks at multidisciplinary methods of solving problems.

    He was a Risk Manager for eBay/Paypal identifying fraud and other risks on the platform and payment system. He has been working the last few years supporting software development, marketing and sales, specifically data infrastructure, data science and analytics platforms for Dell and now TIBCO. He supports his desire to learn and keep current by writing and teaching in the Masters of Data Science Program at City University of New York.

    David Sweenor is an analytics thought leader, international speaker, author, and has co-developed several patents. David has over 20 years of hands-on business analytics experience spanning product marketing, strategy, product development, and data warehousing. He specializes in artificial intelligence, machine learning, data science, business intelligence, the internet of things (IoT), and manufacturing analytics.

    In his current role as the Sr. Director of Product Marketing at Alteryx, David is responsible for GTM strategy for the data science and machine learning portfolio. Prior to joining Alteryx, David has served in a variety of roles—including an Analytics Center of Competency solutions consultant, competitive intelligence analyst, semiconductor yield characterization engineer, and various advanced analytics roles for SAS, IBM, TIBCO, Dell, and Quest. David holds a B.S. in Applied Physics from Rensselaer Polytechnic Institute in Troy, NY and an MBA from the University of Vermont.

    Follow David on Twitter @DavidSweenor and connect with him on LinkedIn https://www.linkedin.com/in/davidsweenor/.

    Dr. Gary Miner received his B.S. from Hamline University, St. Paul, Minnesota with biology, chemistry and education majors; M.S. in Zoology & Population Genetics from the University of Wyoming, and his Ph.D. in Biochemical Genetics from the University of Kansas as the recipient of a NASA Pre-Doctoral Fellowship. During the doctoral study years, he also studied mammalian genetics at The Jackson Laboratory, Bar Harbor, ME, under a College Training Program on an NIH award; and another College Training Program at the Bermuda Biological Station, St. George’s West, Bermuda in a Marine Developmental Embryology Course, on an NSF award; and a third College Training Program held at the University of California, San Diego at the Molecular Techniques in Developmental Biology Institute, again on an NSF award.

    Following that he studied as a Post-Doctoral student at the University of Minnesota in Behavioral Genetics, where, along with research in schizophrenia and Alzheimer’s Disease, he learned "how to write books" from assisting in editing two book manuscripts of his mentor, Irving Gottesman, Ph.D. (Dr. Gottesman returned the favor 41 years later by writing two tutorials for this PRACTICAL TEXT MINING book). After academic research and teaching positions, Dr. Miner did another two-year NIH-Post-Doctoral in Psychiatric Epidemiology and Biostatistics at the University of Iowa where he became thoroughly immersed in studying affective disorders and Alzheimer’s Disease. All together he spend over 30 years researching and writing papers and books on the genetics of Alzheimer’s Disease (Miner, G.D., Richter, R, Blass, J.P., Valentine, J.L, and Winters-Miner, Linda. FAMILIAL ALZHEIMER’S DISEASE: Molecular Genetics and Clinical Perspectives. Dekker: NYC, 1989; and Miner, G.D., Winters-Miner, Linda, Blass, J.P., Richter, R, and Valentine, J.L. CARING FOR ALZHEIMER’S PATIENTS: A Guide for Family & Healthcare Providers. Plenum Press Insight Books: NYC. 1989).

    Over the years he held positions, including professor and chairman of a department, at various universities including The University of Kansas, The University of Minnesota, Northwest Nazarene University, Eastern Nazarene University, Southern Nazarene University, Oral Roberts University Medical School where he was Associate Professor of Pharmacology and Director of the Alzheimer Disease & Geriatric Disorders Research Laboratories, and even for a period of time in the 1990’s was a visiting Clinical Professor of Psychology for Geriatrics at the Fuller Graduate School of Psychology & Fuller Theological Seminary in Pasadena, CA.

    In 1985 he and his wife, Dr. Linda Winters-Miner [author of several tutorials in this book] founded The Familial Alzheimer’s Disease Research Foundation [aka "The Alzheimer’s Foundation] which became a leading force in organizing both local and international scientific meetings and thus bringing together all the leaders in the field of genetics of AD from several countries, which then lead to the writing of the first scientific book on the genetics of Alzheimer’s Disease; this book included papers by over 100 scientists coming out of the First International Symposium on the Genetics of Alzheimer’s Disease held in Tulsa, OK in October, 1987. During part of this time he was also an Affiliate Research Scientist with the Oklahoma Medical Research Foundation located in Oklahoma City with the University of Oklahoma School of Medicine.

    Dr. Miner was influential in bringing all of the world’s leading scientists working on Genetics of AD together at just the right time when various laboratories from Harvard to Duke University and University of California-San Diego, to the University of Heidelberg, in Germany, and universities in Belgium, France, England and Perth, Australia were beginning to find "genes" which they thought were related to Alzheimer’s Disease.

    During the 1990’s Dr. Miner was appointed to the Oklahoma Governor’s Task Force on Alzheimer’s Disease, and also Associate Editor for Alzheimer’s Disease for THE JOURNAL OF GERIATRIC PSYCHIATRY & NEUROLOGY, which he still serves on to this day. By 1995 most of these dominantly inherited genes for AD had been discovered, and the one that Dr. Miner had been working on since the mid-1980’s with the University of Washington in Seattle was the last of these initial 5 to be identified, this gene on Chromosome 1 of the human genome. At that time, having met the goal of finding out some of the genetics of AD, Dr. Miner decided to do something different, to find an area of the business world, and since he had been analyzing data for over 30 years, working for StatSoft, Inc. as a Senior Statistician and Data Mining Consultant seemed a perfect "semi-retirement" career. Interestingly (as his wife had predicted), he discovered that the "business world" was much more fun than the "academic world", and at a KDD-Data Mining meeting in 1999 in San Francisco, he decided that he would specialize in "data mining". Incidentally, he first met Bob Nisbet there who told him, "You just have to meet this bright young rising star John Elder!", and within minutes Bob found John introduced me to him, as he was also at this meeting.

    As Gary delved into this new "data mining" field, and looked at statistics text books in general, he saw the need for ‘practical statistical books’ and started writing chapters, and organizing various outlines for different books. Gary, Bob, and John kept running into each other at KDD meetings, and eventually at a breakfast meeting in Seattle in August of 2005 decided they needed to write a book on data mining, and right there re-organized Gary’s outline which eventually became the book Handbook of Statistical Analysis and Data Mining Applications, 2009, published by Elsevier. And then, in 2012, he was the lead author on a 2nd book from Elsevier/Academic Press, PRACTICAL TEXT MINING. And then a 3rd in this "series" in 2015: PRACTICAL PREDICTIVE ANALYTICS and DECISIONING SYSTEMS FOR MEDICINE. All thanks to Dr. Irving Gottesman, Gary’s "mentor in book writing", who planted the seed back in 1970 while Gary was doing a post-doctoral with him at the University of Minnesota.

    His latest book was released in 2018, the 2nd Edition of the 2009 book HANDBOOK OF STATISTICAL ANALYSIS and DATA MINING APPLICATIONS (https://www.amazon.com/Handbook-Statistical-Analysis-Mining-Applications/dp/0124166326/); and a 2019 book written more for the layperson and decision maker, titled: HEALTHCARE’S OUT SICK – PREDIDCTING A CURE – SOLUTIONS THAT WORK!!! Published by Routledge / Taylor and Francis Group – "A Productivity Press Book" (https://www.amazon.com/HEALTHCAREs-OUT-SICK-PREDICTING-INNOVATIONS/dp/1138581097).

    Dr. Miner is currently working on a 2nd and 3rd book in a series with Scott Burk, Ph.D., and also teaches courses periodically in "Predictive Analytics and Healthcare Analytics" for the University of California-Irvine.

    "I started to count the assertions in this book with which I wholeheartedly agree, and there were so many I ran out of patience. Analytics are at the core of data science and AI and establishing an integrated support organization for them makes perfect sense. So do the people and process-related steps the authors recommend. Buy and read this book and do what it says to do. You have nothing to lose but your organization’s bad decisions!"

    Thomas H. Davenport
    Distinguished Professor, Babson College
    Visiting Professor, Oxford Saïd Business School
    Research Fellow, MIT Initiative on the Digital Economy
    Author of Competing on Analytics and The AI Advantage


    "It’s All Analytics – Part I provides a rich context on AI, data science, analytics, Part II takes it to the next level and shows how to transform smart solutions to tremendous value for the business, and more importantly, how to do it in the right way.

    As authors pointed out, a large percentage of corporate analytics efforts failed. This is not due to short of analytics talent, or lack of technology investment. The success requires a lot more – people and process, senior leadership commitment, re-organization, culture of innovation, to name a few.

    I am thrilled that Scott, David, and Gary continue to share their years of experience in Part II, and truly believe that it could not only help business executives save hundreds of millions of dollars of investment, but also achieve billions of dollars of value to the business and shareholders.

    It’s All Analytics – Part II continues to be a fun read with many practical examples and thought exercises and explains complex concepts in simple language. It’s an incredible piece of work. If any business has the ambition to be smarter and stronger in the digital era, this is a must read for all its executives."

    Xingchu Liu, Ph.D.
    Senior Vice President, Enterprise Data & Analytics

    "I found the secret sauce in Scott Burk's new book It's All Analytics.  I have found that every business stakeholder with whom I have engaged intrinsically understands the decisions that they need to make; they make them every day or every week.  They also intrinsically understand the value of those decisions and - with a little help - can quickly attribute value to improving the effectiveness of those decisions.  

    Chapter 2 ‘The Anatomy of a Business Decision’ does a marvelous job of articulating the importance of not only identifying and understanding the value of those key decisions, but also highlights how those key decisions provide a natural linkage between the business and data science teams. Powerful concept in its simplicity."

    Bill Schmarzo
    Author, Professor, Data Monetization Consultant, and
    Dean of Big Data


    "Analytics, whether it be in the form of data science, business intelligence, machine learning, or any other new and exciting application that's under development, is transforming the world at a speed unprecedented in human history. Most businesses know this, and understand that embracing this change is not just a competitive advantage, but a requirement to remain competitive. However, many businesses don't know how to do this, and make the mistake of thinking that if they hire some data scientists and buy some data tools that this will solve their problem. This is a recipe for failure, and potentially quite expensive failure. Data scientists and data tools are components of implementing change in how a business works, and without an understanding of these changes and a plan for how they will be implemented, the data scientists and data tools will do no good.

    While there are many books and resources on both the concepts and the tools for data science, there aren't many books on how to make them work within a business. This book by Burk, Sweenor, and Miner fills this critical gap. Here, they walk through what organizations need to do, yesterday, to transition into an analytics first business, in terms of organizational, data, and analytics technology design. If you're in a business that wants to start this transition, or is struggling to make it happen, then you need to read this book and follow what it says. It's no exaggeration to say the future of your business depends on it."

    Dylan Zwick, Ph.D.
    Former Director of Data Science


    "There are a great many books that have been written about Analytics that focus on the technical side of the equation. We are now seeing more books that are focusing on the organizational and process side of the Analytics environment. And we are seeing more and more books written about how Analytics Teams should be organized and managed. All of these areas of focus are warranted and are helping practitioners, students, academics, researchers and others who are interested in the field to gain a more holistic view of how the Analytics field is rapidly evolving, growing and changing.

    This new book is a unique combination of data, process, technology, organizational approaches and more. If you are seeking an overview of what you need to know about data and analytics, this book is a great place to start. I speak with a number of people who want to find a way to join the field, either as students about to leave university and begin their careers, or people who want to make a change in their career paths or those that have left the workforce and are now ready to rejoin the day to day working world. For all of those people with all those varied viewpoints and perspectives, this book is a good place to start your journey."

    John Thompson
    Global Head, Advanced Analytics and AI
    CSL Behring


    "This book on the Its All Analytics - Part 2 is particularly appropriate at this time.  We stand at the juncture of a new revolution in business – Rise of the Business Ecosystem driven by analytics.  The biggest challenge we face getting there is not technical but organizational.  This book defines the pathway to get there."

    Robert Nisbet, Ph.D.
    Consulting Data Miner
    Goleta (Santa Barbara), California