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Guidance and results for the data-rich, yet information-poor.

BIG DATA Rhetoric and Reality

Fiction, Friction, Fundamentals & Facts

Classroom: One Day
Online: Two Half-Day Live Sessions
$995 USD
INFORMS Professional Development Units: 9


Big Data is a big deal in business today. But if you were to ask a hundred people what Big Data is – and more importantly, to state its business value – you’d probably get a hundred different answers. Dan Ariely, Professor at Duke University’s Center for Advanced Hindsight, summed it up well in his famous quote, “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”

Big Data is too important and too interesting to be so elusive. This seminar remedies that. It walks participants through the most powerful concepts of Big Data and defines its business value, while simplifying and explaining the technology behind it. The purpose is to distinguish rhetoric from reality, cut through the market buzz surrounding Big Data and boil it down to its essential concepts and applications.

The seminar documents real-world usage and ROI of Big Data. It delineates the successes and the failures of Big Data, and the reasons underlying both. It turns odd-sounding technical terms into fundamental understanding. It characterizes what a data scientist is, and what s/he does all day. It peels away the complexities and rhetoric surrounding Big Data, boiling it down to its essence, presented in a style that all can understand.

This seminar is a non-biased, coherent, and often entertaining integration of facts and figures, explanations and real-world usage of Big Data, translating its technology into business value, and its business value into strategic competitive advantage. It is taught by a 30-year veteran of analytics, with the reason and measured judgment that can only come from that experience. Her perspective is both passionate and impartial, a rare find in the Big Data-crazed marketplace.


    • Line of business executive and functional managers struggling to understand the reality, the business value, the challenges, and the rewards of Big Data
    • IT executives seeking business rationalization for Big Data initiatives
    • Analytic professionals trying to understand the differences in “regular data” and Big Data
    • Data analysts, statisticians, engineers, and computer scientists who aspire to become data scientists
    • The curious who are tired of being bombarded by the Big Data market buzz and frustrated at not understanding it sufficiently to make reasoned decisions about its use


    • A delineation of what’s real and what’s not – rhetoric vs. reality – of Big Data
    • Real world case studies – successful and unsuccessful ones
    • Comprehensive understanding of business challenges and strategic rewards of Big Data initiatives
    • Working understanding of Hadoop, Map Reduce, Python, Pig, Hive (and other technical “things”)
    • Firm grasp of current reality and likely future of Big Data, advanced analytics, and predictive modeling


Big Data is a big deal in business today. It cost big money to develop. It is purported to have big business value. But the message is muddled and often incomplete. Is Big Data just another fad, or is there sound business value behind it? What is the difference between Big Data and “regular data”, and why should you care? What are the critical skills necessary to bring such initiatives to reality? Is Big Data a passing fad or is it here to stay? This seminar answers these fundamental questions, as well as illuminates Big Data’s strategic potential.

Executive interviews recently conducted by the author of this course revealed the following key takeaways –

          • 100% of executives have heard the term “Big Data”.
              • 69% think that the term is mostly buzz, but that there is also business value to be had from it.
              • The remaining 31% feel that the technology industry has not sufficiently built the business case for investing in Big Data.
          • Major obstacles in Big Data adoption are –
                    • High cost of development – 100% of respondents
                    • Lack of a compelling business case – 82% of respondents
                    • Confusion around the varying technologies and associated risks – 68% of respondents

Even companies that are early adopters of Big Data and have successfully shown value with a project or two are challenged by issues of integrating it into organizational processes and culture, and the problem of how to scale initial successes into enterprise-wide strategic business advantage.

This seminar gives a high-level, yet comprehensive overview of Big Data and associated analytics, and methodically addresses each of these issues from a strategic business perspective. It was created by a seasoned analytics consultant who also has a strong background and direct experience in technology. She has managed multiple “proofs of concept” to ascertain both the business value and the most robust Big Data technologies. These years of experience are molded into an entertaining and vastly informative day of distinguishing reality from rhetoric about Big Data.


The developer of this seminar is a highly seasoned practitioner and strategic consultant of all things data, including advanced analytics. Sandra Hendren has been immersed in the evolution from “small data” and “analyses and reporting” in the ’80s to “big data” and advanced analytics today. Her experience has been hands-on (she still writes code and develops predictive models and machine learning algorithms), functional management (in charge of multiple analytic development teams), and at the executive level (most recently as Chief Data and Analytics Strategist for UnitedHealth Group, a Fortune 14 company). Trained as a statistician, she is now known as a Strategic Data Scientist, and is forthright in explaining the differences.

Ms. Hendren is known as an energetic speaker, bridging the gap between business and technology in her lucid explanations of their relationship. She has held multiple adjunct faculty positions, sometimes teaching technology courses, but as often teaching management courses. Most recently she was Senior Lecturer of Strategic Management for Harvard University.

Frustrated at the muddled content and too often biased representation of Big Data, coupled with the total lack of vendor-neutral education of same, Ms. Hendren joined The Modeling Agency specifically to add this seminar to the curriculum. She feels strongly that all managers need a working knowledge of the latest trends in technology to be effective. Likewise, she feels that information technology professionals should seek a better understanding of the business value of the technology they are responsible for. Thus, this course was developed to strike that balance, and to be equally useful for both business and technology professionals, as well as analytic professionals attempting to make the move from “regular data” to Big Data.


What is Big Data?
                          • The Official Definition
                          • The Unofficial Definition
                          • Some Executives’ Definitions
                          • The “Real” Definition
                          • A Strategic Definition
                          • My Working Definition
What is the Business Value of Big Data?
                              • Two High Value Use Cases
                              • The ROI of Analytics
                              • Analytic Stages and ROI
                              • The Relationship of Big Data and High ROI Analytics
                              • Top Three Sources of High ROI
How is Big Data Analytics Different from “Regular Analytics”?
                                • A Short History of Analytics
                                • Three Types of Analytics
                                    • Descriptive Analytics
                                    • Predictive Analytics
                                    • Discovery
                                  • Big Data Analytic Methods, the Same but Different
                                      • Statistics
                                      • Data Mining
                                      • Machine Learning
                                    • Comparison and Cautions of Big Data Analytics vs. Regular Analytics
What are the Risks of Big Data?
                                  • Big Data Data Issues
                                  • The Truth about Social Media Data
                                  • Big Data People Issues
                                  • Big Data Technology Issues
                                  • The Top 5 Risks of Big Data
                                  • A Big Big Data Failure
What are Big Data Technologies? A Layman’s View
                                      • Data and Analytics Technology – Old Rules
                                      • Data and Analytics Technology – New Rules
                                      • Newcomers: Who Are They and What Do They Do?
                                          • Hadoop and Map/Reduce
                                          • Open Source Code – Python, R, Pig, Hive, and More
                                        • Hadoop Realities
                                        • Licensed Software Realities
                                        • Total Cost of Ownership of Big Data Realities
                                        • How to Decide: The Data Part
                                        • How to Decide: The Analytics Part
What are the Skills Needed for Big Data?
                                          • Data Science Professionals
                                              • Data Architect
                                              • Data Engineer
                                              • Data Scientist
                                              • Subject Matter Expert
                                            • What Does a Data Scientist Do All Day?
                                                • Data Scientist Fundamental Skills
                                                • Characteristics of Data Scientists
How do You Organize Big Data in Your Company?
                                                    • Historic Data and Analytics Organization
                                                    • Big Data Organizational Paradox
                                                    • 5 Types of Organizational Structures
The Future of Big Data and Advanced Analytics
                                                      • From Rhetoric to Reality
                                                      • Market Facts and Figures – Reality
                                                      • Biggest Driver of Business Innovation
                                                          • Continually Improving Productivity and Profitability
                                                          • Predicting Problems Before They Happen Becomes the New Norm
                                                          • Changing Ever More Business Models
                                                        • What’s Next in Big Data?
Picking Through the Rhetoric to Define Your Organization’s Big Data Reality
                                                            • A High Level Big Data Plan
                                                                • My Top Rhetorics (and Associated Realities) Summarized

The Modeling Agency, LLC, is registered with INFORMS (the INstitute For Operations Research and the Management Sciences) as a Recognized Analytics Continuing Educational Provider for the CAP® (Certified Analytics Professional) program. The CAP® credential provides analytics professionals with a means to distinguish themselves and demonstrate to employers, colleagues, and the public that they are knowledgeable analytics professionals. Courses provided by The Modeling Agency, LLC, are automatically accepted by INFORMS when claimed by credential holders as evidence of continuing education. For more information about the CAP® program, including requirements, eligibility, benefits, preparation, and exam dates, please visit the INFORMS website at www.informs.org/certification.


April 19, 2017
June 27 & 28, 2017
12:00p – 5p US EDT | 9:00a – 2p US PDT
17:00 – 22:00 UTC / GMT
August 2, 2017

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Attendee Comments

“Big Data and Analytics are not merely about scalability and stats, but the ability to understand the context of the problems, to ask the right questions, and to establish the performance metrics that are relevant and crucial to answer these questions. It requires a service design mentality, and that’s what this class delivers”

Ting-Shuo Yo
Data Scientist
ASUSTeK Computer inc.

“The instructor is also a consultant. She fully understands real world scenarios, and a project approach to effective execution.”

Jean M. Cotis
Application Development Manager
UPS Freight

“I look forward to applying what we learned during this seminar to identify and prioritize new projects. We will be more organized, efficient and effective by applying the concepts delivered in this class.”

Sean Schmitt
US Customs and Border Patrol

“No fluff at all. The instructor consistently provided specific, practical and understandable advice about complex ideas and processes. She explains things very clearly, backs up her answers with real-world exampls and is patient with questions.”

Partner Conference Production

“As someone without a formal degree, big data and analytics can be daunting. This course is approached in a very consumable way.  It is practical and applicable, which is exactly what is needed in order to support organizational objectives.”

Carmen Schwesinger
Government of Alberta