IIA is excited to bring in top minds in the Analytics world to be our 2019 Analytics Symposium speakers!



EA is squarely in the center of the global hotbed for data science talent. More of it exists in the Bay area than anywhere else in the world which is because there is more concentrated demand for it here than anywhere else. When a candidate or employee is being heavily recruited by behemoths like Google, well-funded disruptors like Airbnb and interesting new garage-based startups with limitless potential, a 37 year old enterprise like EA needs to have an exceptional strategy to compete and excel in finding and retaining the best data scientists…and we do. In this presentation, Zack Anderson will be sharing his keys to the soft skills and hard constructs that create an environment within which a data science culture can thrive. 

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ABOUT: Zachery is the Chief Analytics Officer and Senior Vice President at Electronic Arts (EA), the world’s largest Video Game Company. He is responsible for leading Consumer Insights, UX Research, Data Science, Studio Analytics, and Marketing Analytics for EA. His team uses in-game behavioral data, traditional consumer research, lab work, and online advertising data to provoke and inspire EA’s development and marketing teams to think and act “Player First.” Prior to joining EA in 2007, Zachery was head of consulting and modeling for J.D. Power and Associates’ PIN group, Corporate Economist for Nissan North America, and Economist for the private investment company Fremont Group.

Zachery’s work has been highlighted in the Harvard Business Review and the MIT Sloan Management Review. His work has won many awards including the INFORMS Marketing Science Practice Prize and while at Nissan he was recognized by the US Federal Reserve for the Best Industry Forecast. He is a member of the University of California Master of Science in Business Analytics Industry Advisory Board.


Chief Analytics Office & SVP

Electronic Arts (EA)

Zachery’s undergraduate degree in Political Science and Communications is from Southern Illinois University. His graduate work was at UCLA, in Economics and Political Science, where he studied game theory with Nobel Prize Winner Lloyd Shapley.



The biggest initial hurdle to success with Big Data isn’t technical - it’s management. Your data engineering project’s initial success is predicated on your management team correctly staffing and resourcing it. This runs opposite to how most data engineering teams are started and run. If you just choose the best technologies, things will just fall into place. They don’t and that’s a common pattern for failure.

But how do you correctly do something that’s so new? This could be your team’s first data engineering project. What should the team look like? What skills should the team have? What should you look for in Data Engineer (because you’ll probably have to hire a Software Engineer and train them)? What are some of the management pitfalls?

In this talk, we will cover the most common reasons why data engineering teams fail and how to correct them. This will include ways to get your management to understand that data engineering is really complex and time consuming. It is not data warehousing with new names. Management needs to understand that you can’t compare a data engineering team to the web development team, for example.

Jesse will share the stories of teams who haven’t set up their data engineering culture correctly and what happened. Then, Jesse will talk about the teams who’ve turned around their culture and how they did it. Finally, Jesse will share the skills that every data engineering team needs.

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Data Engineer, Creative Engineer & Managing Director

Big Data Institute

ABOUT: Jesse Anderson is a Data Engineer, Creative Engineer and Managing Director of Big Data Institute.

He works with companies ranging from startups to Fortune 100 companies on Big Data. This includes training on cutting edge technologies like Apache Kafka, Apache Hadoop and Apache Spark. He has taught over 30,000 people the skills to become data engineers.

He is widely regarded as an expert in the field and for his novel teaching practices. Jesse is published on O’Reilly and Pragmatic Programmers. He has been covered in prestigious publications such as The Wall Street Journal, CNN, BBC, NPR, Engadget, and Wired.



Winning in the marketplace requires not just great analytics and product leadership, but increasingly a great engineering organization.  DevOps principles and practices were pioneered by organizations such as Google, Amazon and Facebook, but are increasingly being used in almost every industry vertical, including organizations such as Capital One, Nordstrom, Starbucks, Disney, etc.

In this presentation, I’ll share some of my top learnings from my 20-year journey of studying high performing technology organizations, our State of DevOps five-year research that spanned over 30,000 respondents, and how product leaders can create a mutually supportive relationship with engineering.

I’ll discuss what great engineering performance looks like, and the technical, architectural and leadership practices that enable it — and what product leaders can do to help.


Author, Founder & Former CTO

Tripwire - IT Revolution

ABOUT: Gene Kim is a multiple award-winning CTO, researcher and author. He was founder and CTO of Tripwire for 13 years. He has written four books, including “The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win”, “The DevOps Handbook”, and the newly-released “Accelerate”. Since 2014, he has been the organizer of the DevOps Enterprise Summit, studying the technology transformations of large, complex organizations.



In 2019, Gartner predicted 80%+ of analytics insights won’t deliver outcomes through 2022—despite ongoing and sizable investments in technology and data. Executives are worried about having an AI strategy. Data scientists worry about getting their models to be as accurate as possible. IOT teams stay busy juggling telemetry, alerts, and APIs. Report developers do their best to visualize the data, and engineers try to glue it all together and ship it. However, if business value is dependent on specific users engaging successfully with a decision support application or data product, then teams must design these solutions around the people using them—not the data or technology. Human-centered design provides a process to help teams discover, define, and fall in love with customer problems and needs so that solutions encourage meaningful engagement and outcomes, and the business realizes value from its investment in analytics. In this mini-workshop, Brian will share some common causes of low engagement with data products, introduce the design process, and teach attendees to apply one design technique in a small group setting.



Designing For Analytics

ABOUT: Brian T. O'Neill is a product designer and founder of the consultancy, Designing for Analytics, which provides design and UX consulting for custom enterprise data products and apps. For over 20 years, he has worked with companies including DELL/EMC, Tripadvisor, Fidelity, NetApp, MITRE, JP Morgan Chase, ETrade and numerous SAAS startups. Today Brian focuses on helping clients create more useful, usable, profitable, and engaging decision support software and information products. In addition to consulting, Brian is also an international speaker and podcast guest, having appeared at multiple O'Reilly Strata conferences, Predictive Analytics World in Berlin, and on the IBM Analytics podcast, Making Data Simple. He also authored the Designing for Analytics Self-Assessment Guide for Non-Designers,  maintains an active mailing list, and hosts the podcast, Experiencing Data. Earlier in 2018, Brian joined the International Institute for Analytics' Expert Network as an advisor on design and UX. A musician by training, Brian is also a professional percussionist in Boston. He tours internationally and has performed at Carnegie Hall and The Kennedy Center. Follow him on Twitter (@rhythmspice) or join the mailing list at designingforanalytics.com.



Since the publication of the Manifesto for Agile Software Development in 2001, Agile methodologies have been adopted by a majority of tech companies and have unquestionably revolutionized the tech industry and its culture. Agile’s huge success is hardly a surprise: Agile development came as a breath of fresh air at a time when the tech industry was crippled by the many inefficiencies caused by its own success. Back then, the Agile mindset was a panacea for tech’s growing pains.

However, the tech industry is now facing a new revolution: big data, machine learning, and artificial intelligence. The methodologies that were so beneficial to the field of software development seem inappropriate for data science teams, because data science is part engineering, part research.

Jennifer Prendki demonstrates how, with a minimum amount of tweaking, data science managers can adapt Agile techniques and establish best practices to make their teams more efficient. Jennifer starts by discussing the Agile Manifesto in detail and reviewing the reasons for its major success in software engineering. She then outlines the different ways that organizations set up their data science initiatives and explains in which ways these teams differ or are similar to software engineering teams. Jennifer concludes by detailing how to adapt traditional Agile methodologies to create a powerful framework for data science managers and shares tips on how to allocate resources, improve best practices, and tweak the usage of planning and organization tools for the benefit of data teams.


VP of Machine Learning

Figure Eight

ABOUT: Jennifer Prendki is currently the VP of Machine Learning at Figure Eight, the essential human-in-the-loop AI platform for data science and machine learning teams. She has spent most of her career creating a data-driven culture wherever she went, succeeding in sometimes highly skeptical environments. She is particularly skilled at building and scaling high-performance Machine Learning teams, and is known for enjoying a good challenge. Trained as a particle physicist (she holds a PhD in Particle Physics from Sorbonne University), she likes to use her analytical mind not only when building complex models, but also as part of her leadership philosophy. She is pragmatic yet detail-oriented. Jennifer also takes great pleasure in addressing both technical and non-technical audiences at conferences and seminars, and is passionate about attracting more women to careers in STEM.



The challenges large non-digital-natives face in moving towards forward-leaning data science companies are certainly technological but perhaps more importantly cultural. As Nike executes their strategy to grow and innovate with ML and AI they have found some expected counterforces along with a few surprises. In this session, Matt Levinson, the Personalization Data Science Director at Nike, will walk through his experience in transforming Nike to become an enterprise that runs on analytics.   

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Personalization Data Science Director


ABOUT: Matt leads Content Science at Nike, optimizing content creation, understanding content facets and user tastes, and personalizing the front end content experience.  Prior to that, Matt co-led the Personalization Science team, which focused on personalization and optimization of front end product focused experiences, primarily through recommendations and search optimization. Before joining Nike, Matt spent a decade in academia, working as a research programmer in economic development and statistical genetics before getting his PhD is statistics and completing a postdoc in computational biology. Outside of work, Matt loves spending sunny Oregon weekends outside with his family and pursuing his dream of becoming a professional hobbyist.



AI, data science and machine learning are taking over—but just how much are they really taking over? While technology is replacing a lot of formerly human-only jobs, there are new opportunities and positions available (both technical and non-technical) for those willing to learn. We will discuss how AI and data are disrupting technology, the shortcomings of AI and the shifting roles for humans in a data-driven world. We’ll be looking at how the growing demand for data scientists and data-savvy managers are moving towards different specialized training tracks for both technical and non-technical roles.

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Founder and CEO

Data Incubator

ABOUT: Tianhui Michael Li is the founder and CEO of the Data Incubator. Michael has worked as a data scientist lead at Foursquare, a quant at D.E. Shaw and JPMorgan, and a rocket scientist at NASA. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup that lets him focus on what he really loves. He did his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar.



We hear about AI almost every day now. Opinions seem split between impending doom side and “superintelligence will save the human race.” Jana Eggers offers the real deal on AI, explaining what’s hype and what isn’t and what we can do about it. Being in and around the field of AI for more than 25 years, Jana has a perspective on what’s been accomplished lately and why; what we are still missing; and even how afraid to be for our and our kids’ futures.



Nara Logics

ABOUT: Jana Eggers is a math and computer nerd who took the business path for a career. Today, she’s CEO of Nara Logics, a neuroscience-inspired artificial intelligence company providing a platform for recommendations and decision support. Jana’s career has taken her from 3-person business beginnings to 50,000-person enterprises. She’s opened the European logistics software offices as part of American Airlines, dived into the internet in ’96 at Lycos, founded Intuit’s corporate Innovation Lab, helped define mass customization at Spreadshirt, and researched conducting polymers at Los Alamos National Laboratory. Her passions are working with teams to define and deliver products customers love, algorithms and their intelligence, and inspiring teams to do more than they thought possible.



Your enterprise data strategy has to begin with an assessment of your information economy: supply, demand, their relative competencies and capabilities, and how the two do – and don’t – work together effectively, to get information from the point of capture to the point of use, within your organization.

 In this workshop, we’ll outline a straightforward process for mapping your information economy, focusing on:

  • Systems of record, their theoretical and actual information contributions, and their accessibility

  • The owners and operators of those systems of record, and their needs and concerns

  • Demand-side constituents, their relative analytical sophistication and their information requirements

  • Intermediaries, and their role in brokering connections between supply, and demand.

Marc Demarest is the CEO and Principal of Noumenal, Inc., a private intellectual capital consulting firm based in the Pacific Northwest and the United Kingdom.


CEO & Principal


Widely known as the father of data marting, Marc publishes and speaks regularly on topics ranging from big data, knowledge management, analytical applications and decision support systems to IT technology futures and information ethics; selections are available at www.noumenal.com/marc. He holds BA degrees in Political Science and English Literature from Bucknell University in Lewisburg, PA., and an MA from the University of South Carolina. He is a MBA graduate of Stanford University’s AEA Executive Institute and the NCR Process Management Institute and is an inventor on more than dozen granted or in-process patents in the areas of data warehousing, social network analysis and e-discovery, and is writing a book on decision support systems.

Prior to founding Noumenal, Marc was Chairman and CEO of DecisionPoint Applications, Inc., the industry’s first provider of packaged enterprise-scale data warehousing software for financial, human resources, manufacturing and distribution analysis. As CEO, he brought the company to market, oversaw the acquisition of the company’s first 100 customers and raised $42M in three funding rounds from a variety of venture and institutional investors including British Telecom, IBM, J&W Seligman and Battery Ventures.

Marc has also held executive positions with The Sales Consultancy, Inc., a high-technology sales and marketing consulting firm based in Dallas, Texas and the United Kingdom; Atlas Telecommunications, a global supplier of value-added network services, where he was GM of the company’s AFAX service bureau and AFNET global TCP/IP VAN; and Sequent Computer Systems, Inc., where he was head of Corporate Architecture and Chief Knowledge Officer.



In 1986, the mathematician and philosopher Gian-Carlo Rota wrote, “I wonder whether or when artificial intelligence will ever crash the barrier of meaning.” Here, the phrase “barrier of meaning” refers to a belief about humans versus machines: humans are able to “actually understand” the situations they encounter, whereas AI systems (at least current ones) do not possess such understanding. The internal representations learned by (or programmed into) AI systems do not capture the rich “meanings” that humans bring to bear in perception, language, and reasoning. In this talk, Melanie will assess the state of the art of artificial intelligence in several domains, and describe some of their current limitations and vulnerabilities, which can be accounted for by a lack of true understanding of the domains they work in. She will explore the following questions: (1) To be reliable in human domains, what do AI systems actually need to “understand”? (2) Which domains require human-like understanding? And (3) What does such understanding entail?

ABOUT: Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Member of the Science Board at the Santa Fe Institute. She attended Brown University, where she majored in mathematics and did research in astronomy, and the University of Michigan, where she received a Ph.D. in computer science. Her dissertation, in collaboration with her advisor Douglas Hofstadter, was the development of Copycat, a computer program that makes analogies.


Professor of Computer Science

Portland State University

Melanie has held faculty or professional positions at the University of Michigan, the Santa Fe Institute, Los Alamos National Laboratory, the OGI School of Science and Engineering, and Portland State University. She is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Melanie’s book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award and was named by Amazon.com as one of the ten best science books of 2009. Melanie’s latest book, Artificial Intelligence: A Guide for Thinking Humans, will be published by Farrar, Straus, and Giroux in 2019.

Melanie originated the Santa Fe Institute's Complexity Explorer platform, which offers online courses and other educational resources related to the field of complex systems.Her online course “Introduction to Complexity” has been taken by over 25,000 students, and is one of Course Central’s “top fifty online courses of all time”.



Companies collect all kinds of data and use advanced tools and techniques to find insights, but they often fail in the last mile: communicating insights effectively to drive change. Brent Dykes discusses the power that stories wield over statistics and explores the art and science of data storytelling—an essential skill in today’s data economy.

Brent sets the stage by explaining why you need to communicate effectively with data. He then dives into the psychology of storytelling and details how decisions are often influenced by emotion—not logic or reason. By combining data (logic) with story (emotion), Brent demonstrates how data professionals can communicate more effectively their insights to their audiences and shares a powerful framework for understanding how data storytelling can leverage data, narrative, and visuals to influence change. Along the way, Brent covers the structure of a data story, the data storytelling arc, how to bring characters or heroes into your data story by focusing on the people behind the numbers, and the visual aspects of a data story. You’ll also get five tips for creating better visual stories—identify the right data, choose the right visualizations, calibrate visuals to your message, remove unnecessary noise, and focus attention on what’s important.

Physician and early pioneer of antiseptic procedures Ignaz Semmelweis discovered a life-saving insight but failed to tell its story. He missed an opportunity to save countless lives because he simply couldn’t communicate his insight effectively. Don’t let it happen to you. Join Brent to learn how to become a data storyteller and ensure your insights drive action and positive change.


Director of Data Strategy


ABOUT: Brent Dykes is the director of data strategy at Domo. Brent has over 15 years of enterprise analytics experience at Omniture, Adobe, and Domo. He is a regular Forbes contributor on data-related topics and has published two books on digital analytics, including Web Analytics Action Hero. In 2016, Brent received the Most Influential Industry Contributor Award from the Digital Analytics Association (DAA). He is a popular speaker at conferences such as Strata, RISE, Shop.org, Adtech, Pubcon, and Adobe Summit. Brent holds an MBA from Brigham Young University and a BBA in marketing from Simon Fraser University.



In a traditional manufacturing culture, it may be expected that long established processes would be a hindrance to innovation – however at John Deere we’ve found that these have actually given us an edge to better embracing analytics. By building on the strengths of our company's DNA we've accelerated our data-driven mandate to find sustainable success. In this talk, we will share the stepwise approach we took to build up our analytics culture on the uniqueness of what has made this company so successful for so long instead of trying to break it apart and start from scratch.


Analytics Leader

John Deere

ABOUT: Liz Conzo leads John Deere’s Precision Ag Analytics Team – a diverse team of data scientists, engineers, and agronomists developing solutions and insights in precision agriculture. Conzo has been working in or leading analytics teams at John Deere for 14 years in both manufacturing and sales & marketing functions. She helped to develop and inject predictive modeling into Deere’s sales and operations planning process and led the establishment of processes and models to support inventory optimization. She’s had the opportunity to apply her passion for analytics across functions and across the world with John Deere.


Director of Analytics

John Deere Financial

ABOUT: Kira H. Barclay is Director of Analytics at John Deere Financial World Headquarters in Johnston, Iowa. Barclay oversees customer and channel analytics at John Deere Financial. She leads a team of analytics professionals whose mission is to drive sales opportunities and deliver distinctive and sustainable competitive advantages for John Deere. In addition, she leads the development and execution of the John Deere Financial data and analytics strategy and develops processes and tactics to support strategic priorities. Barclay’s career with John Deere Financial began as an intern in Risk Analytics and throughout her tenure, she’s held positions of increasing responsibility in Risk Analytics and Market Pricing. She worked for the John Deere Construction and Forestry Division as Manager, Discounts and Incentives and most recently served as Director, Advanced Analytics Strategy. Barclay attended Iowa State University where she received a Bachelor of Science degree in Mathematics and Sociology and followed that with a Master’s degree in Statistics. She has also earned her Master of Business Administration degree in Strategic Management from Indiana University.



Humans are fully trained to live in a 3D world yet the technology that can recreate this experience is not yet well integrated within decision making practices in the corporate world. With analytics being at the core of modern enterprise decision making, in this talk, we'll take a look at the current abilities and limitations of mixed reality to enable analytics efforts. From providing a new source of data that can be automated through an algorithm to drive human activity, over to being a better visualization tool to tell the "story" to your business colleagues to gain their partnership - we'll review a few use cases to inspire your thinking.

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Founding Partner

Object Theory

ABOUT: Michael Hoffman is co-founder of Object Theory, an XR agency with a focus on strategy, UX, and product development. Object Theory is one of Microsoft's longest standing partners in their Microsoft Mixed Reality Partner (MMRP) program, since prior to the release of HoloLens.

Prior to forming Object Theory, Michael led the creation of showcase experiences for Microsoft HoloLens as a Principal Engineering Lead in on the HoloLens team. His work includes some of the remarkable applications created for Autodesk, Trimble, and JPL/NASA Curiosity Mars rover mission planning.

Michael’s history also includes positions at Google and Nike Digital Sport, as well as leading high profile platform solutions in publishing, bioscience, mass storage, and telecommunications.



Think “wine,” and you’re likely to think about the pleasure of the vineyards and the lifestyle and the beverage itself. There’s another, more BI-leaning angle to wine, however, that's gaining traction and attention both within the industry and among the broader VC/tech community as well. In this interactive presentation, Cathy Huyghe, co-founder and CEO of Enolytics, describes the roller coaster ride of bringing analytics to an industry that’s long been grounded in the soil and traditional handwork rather than machine learning or cutting-edge technology. Join us and take the pulse of that adventure, complete with a tasting component and demonstration.




ABOUT: Cathy Huyghe is the co-founder of Enolytics, which provides big data services to the wine industry. She is also the author of the award-winning book, Hungry for Wine: Seeing the World through the Lens of a Wine Glass, which has received positive reviews from publications ranging from the Wall Street Journal to NPR to the Village Voice. 

Huyghe currently writes for Forbes.com about the business and politics of the wine industry, and for Inc.com about entrepreneurship, women and hospitality. She was a finalist in 2016 for the IWSC Wine Communicator of the Year award.

Huyghe’s writing has appeared in print and online for both general-interest and wine industry publications including the Harvard Business Review network, The Atlantic, Decanter, DailyBeast, Boston Globe, Washington Post, Wine Enthusiast, GlobalPost, Grist.org, and Daily Candy. She has also been featured on the BBC, WNYC, WGBH, and Nevada Public Radio.



The growing complexity of analytics leads to “black box” solutions that few people in the organization understand completely. We often hear about the difficulty of interpretability – explaining how an analytic model works – and that we need interpretability to deploy analytics. But we use many black boxes without understanding them… if they are reliable. It’s when the black box becomes unreliable that we lose trust.

Mistrust is really created by the lack of reliability, and the lack of reliability is the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibility – our ability to get the same results given the same information – extends our view to include the environment used to construct, operate, and support a model.

In this workshop we’ll review practices to follow to make systems more transparent and reliable, and examine some of the tradeoffs one must make between flexibility and reliability. We need to consider our practices so that we improve trust and increase the chances that our analytic solutions will succeed.

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Global Head of Architecture


ABOUT: Mark R. Madsen is an advisor to companies on data strategy and technology, focused on analytics, machine learning, and the data management and infrastructure behind them. He works in the technology & innovation office at Teradata as the global head of architecture. His experience includes building and managing analytics in roles ranging from analyst to CTO.



Executive Director


ABOUT: Kathy Koontz is the Executive Director of IIA’s Analytics Leadership Consortium. She works with analytics executives from high-performing, data-driven enterprises to further improve their capabilities. With more than 20 years of experience in the analytics space, Kathy has a clear understanding of the analytics challenges organizations face and how to overcome them. She has experience developing the strategy, implementing the technology and leveraging the power of data and analytics to create sustainable competitive advantage in organizations ranging from Fortune 100 to nonprofits. Before joining IIA, Kathy was the Practice Director-Customer Journey at Teradata, where she worked with clients across industries to use data, analytics and interactions to create enduring customer relationships. Previously, she served as Associate Vice President of Customer Analytics for Nationwide Mutual Insurance.


ABOUT: Bill Franks is IIA’s Chief Analytics Officer, where he provides perspective on trends in the analytics and big data space and helps clients understand how IIA can support their efforts and improve analytics performance. His focus is on translating complex analytics into terms that business users can understand and working with organizations to implement their analytics effectively. His work has spanned many industries for companies ranging from Fortune 100 companies to small non-profits.


Chief Analytics Officer


Franks is the author of the book Taming The Big Data Tidal Wave (John Wiley & Sons, Inc., April, 2012). In the book, he applies his two decades of experience working with clients on large-scale analytics initiatives to outline what it takes to succeed in today’s world of big data and analytics. Franks’ second book The Analytics Revolution (John Wiley & Sons, Inc., September, 2014) lays out how to move beyond using analytics to find important insights in data (both big and small) and into operationalizing those insights at scale to truly impact a business. He is an active speaker who has presented at dozens of events in recent years. His blog, Analytics Matters, addresses the transformation required to make analytics a core component of business decisions.

Franks earned a Bachelor’s degree in Applied Statistics from Virginia Tech and a Master’s degree in Applied Statistics from North Carolina State University. More information is available at www.bill-franks.com.