Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY
September 12–13, 2018: Keynotes & Sessions
New York, NY
September 11-13 2018
New York, NY
Make data work for business.
The 2018 Strata Business Summit will give you a thorough understanding of how some of the world’s leading companies build successful data strategies. You’ll discover game-changing technologies and their business applications—and how to move your enterprise forward to bridge the gap. You'll also receive a hand-picked lineup of executive briefings on key issues such as: predictive analytics and machine learning, Cloud strategy, governance security and privacy, IoT, and artificial intelligence, and more.
In just 3 days, you’ll have the intel you need to build strategies and data-driven business models that deliver customer insight, drive efficiency and innovation in products and services, modernize architecture, reduce costs, and lower risk.
Featured Speakers
Gold and Silver pass holders have access to Strata Business Summit on Tues–Thurs. Platinum and Bronze pass holders have access to Strata Business Summit on Wed–Thurs.
| Tuesday Sep 11: Tutorials (Gold & Silver passes) |
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| Wednesday Sep 12: Keynotes & Sessions (Platinum, Gold, Silver & Bronze passes) |
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| 9:00am | Location: 3E Strata Data Conference Keynotes |
| 10:50am Morning break |
| Thursday Sep 13: Keynotes & Sessions (Platinum, Gold, Silver & Bronze passes) |
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| 9:00am | Location: 3E Strata Data Conference Keynotes |
| 10:50am Morning break |
9:00am–5:00pm Tuesday, 09/11/2018
Location: 1A 04/05
Average rating:
(5.00, 1 rating)
Acquiring machine learning (ML) technology is relatively straightforward, but ML must be applied to be useful. In this one-day boot camp that is equal parts hackathon, presentation, and group participation, Jerry Overton, Ashim Bose, and Samir Sehovic teach you how to apply advanced analytics in ways that reshape the enterprise and improve outcomes.
Read more.
9:00am–12:30pm Tuesday, 09/11/2018
Location: 1A 12/14
Level: Non-technical
Secondary topics:
Machine Learning in the enterpriseAverage rating:
(4.29, 7 ratings)
The honeymoon era of data science is ending, and accountability is coming. Successful data science leaders deliver measurable impact on an increasing share of an enterprise’s KPIs. Joshua Poduska and Patrick Harrison detail how leading organizations have taken a holistic approach to people, process, and technology to build a sustainable competitive advantage
Read more.
9:00am–5:00pm Tuesday, 09/11/2018
Location: 1A 08
Alistair Croll (Solve For Interesting),
Robert Passarella (Alpha Features),
Amro Alkhatib (National Health Insurance Company-Daman),
Mridul Mishra (Fidelity Investments),
Patrick Angeles (Cloudera),
James Psota (Panjiva ),
Andreas Kohlmaier (Munich Re),
Paul Lashmet (Arcadia Data),
Nick Curcuru (Mastercard),
Robin Way (Corios),
Theresa Johnson (Airbnb),
Jane Tran (Unqork),
Swatee Singh (American Express)
From analyzing risk and detecting fraud to predicting payments and improving customer experience, take a deep dive into the ways data technologies are transforming the financial industry.
Read more.
9:00am–5:00pm Tuesday, 09/11/2018
Location: 1E 10
Paco Nathan (derwen.ai),
Katharina Warzel (EveryMundo),
Mike Berger (Mount Sinai Health System),
Sam Helmich (Deere & Company),
Stephanie Fischer (datanizing GmbH),
Maryam Jahanshahi (TapRecruit),
Greg Quist (SmartCover Systems),
Ann Nguyen (Whole Whale),
Steve Otto (Navistar),
Jennifer Lim (Cerner),
S Anand (Gramener),
Ian Brooks (Cloudera)
Hear practical insights from household brands and global companies: the challenges they tackled, approaches they took, and the benefits—and drawbacks—of their solutions.
Read more.
1:30pm–5:00pm Tuesday, 09/11/2018
Location: 1E 15/16
Level: Beginner
Secondary topics:
Machine Learning in the enterpriseAverage rating:
(2.67, 9 ratings)
Janet Forbes, Danielle Leighton, and Lindsay Brin lead a primer on crafting well-conceived data science projects that uncover valuable business insights. Using case studies and hands-on skills development, Janet, Danielle, and Lindsay walk you through essential techniques for effecting real business change.
Read more.
11:20am–12:00pm Wednesday, 09/12/2018
Location: 1E 10/11
Level: Non-technical
Secondary topics:
Data preparation, governance and privacy,
Machine Learning in the enterprise
JF Gagne (Element AI)
Average rating:
(3.50, 4 ratings)
JF Gagne explains why the CIO is going to need a broader mandate in the company to better align their AI training and outcomes with business goals and compliance. This mandate should include an AI governance team that is well staffed and deeply established in the company, in order to catch biases that can develop from faulty goals or flawed data.
Read more.
11:20am–12:00pm Wednesday, 09/12/2018
Location: 1E 12/13
Level: Intermediate
Secondary topics:
Machine Learning in the enterprise
Jennifer Prendki (Figure Eight)
Average rating:
(4.38, 8 ratings)
Agile methodologies have been widely successful for software engineering teams but 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.
Read more.
11:20am–12:00pm Wednesday, 09/12/2018
Location: 1E 14
Level: Intermediate
Secondary topics:
Data preparation, governance and privacy,
Ethics and Privacy
In May 2018, the General Data Protection Regulation (GDPR) went into effect for firms doing business in the EU, but many companies still aren't prepared for the strict regulation or fines for noncompliance (up to €20 million or 4% of global annual revenue). Mark Donsky and Steven Ross outline the capabilities your data environment needs to simplify compliance with GDPR and future regulations.
Read more.
1:15pm–1:55pm Wednesday, 09/12/2018
Location: 1E 12/13
Level: Advanced
Secondary topics:
Data preparation, governance and privacy,
Ethics and Privacy
Les McMonagle (BlueTalon)
Average rating:
(5.00, 2 ratings)
Privacy by design is a fundamentally important approach to achieving compliance with GDPR and other data privacy or data protection regulations. Les McMonagle outlines how organizations can save time and money while improving data security and regulatory compliance and dramatically reduce the risk of a data breach or expensive penalties for noncompliance.
Read more.
1:15pm–1:55pm Wednesday, 09/12/2018
Location: 1E 10/11
Level: Non-technical
Secondary topics:
Machine Learning in the enterprise,
Retail and e-commerce
Erin Coffman (Airbnb)
Average rating:
(5.00, 7 ratings)
Airbnb has open-sourced many high-leverage data tools, including Airflow, Superset, and the Knowledge Repo, but adoption of these tools across the company was relatively low. Erin Coffman offers an overview of Data University, launched to make data more accessible and utilized in decision making at Airbnb.
Read more.
1:15pm–1:55pm Wednesday, 09/12/2018
Location: 1E 14
Level: Non-technical
Secondary topics:
Machine Learning in the enterpriseAverage rating:
(3.40, 5 ratings)
Tony Baer and Florian Douetteau share the results of research cosponsored by Ovum and Dataiku that surveyed a specially selected sample of chief data officers and data scientists on how to map roles and processes to make success with AI in the business repeatable.
Read more.
2:05pm–2:45pm Wednesday, 09/12/2018
Location: 1E 10/11
Level: Beginner
Lawrence Cowan (Cicero Group)
Average rating:
(3.00, 3 ratings)
Firms are struggling to leverage their data. Lawrence Cowan outlines a methodology for assessing four critical areas that firms must consider when looking to make the analytical leap: data strategy, data culture, data analysis and implementation, and data management and architecture.
Read more.
2:05pm–2:45pm Wednesday, 09/12/2018
Location: 1E 12/13
Level: Beginner
Secondary topics:
Ethics and Privacy
Harry Glaser (Periscope Data)
Average rating:
(5.00, 2 ratings)
What is the moral responsibility of a data team today? As AI and machine learning technologies become part of our everyday life and as data becomes accessible to everyone, CDOs and data teams are taking on a very important moral role as the conscience of the corporation. Harry Glaser highlights the risks companies will face if they don't empower data teams to lead the way for ethical data use.
Read more.
2:05pm–2:45pm Wednesday, 09/12/2018
Location: 1E 14
Level: Intermediate
Secondary topics:
Machine Learning in the enterprise,
Model lifecycle management
David Talby (Pacific AI)
Average rating:
(4.40, 5 ratings)
Machine learning and data science systems often fail in production in unexpected ways. David Talby shares real-world case studies showing why this happens and explains what you can do about it, covering best practices and lessons learned from a decade of experience building and operating such systems at Fortune 500 companies across several industries.
Read more.
2:55pm–3:35pm Wednesday, 09/12/2018
Location: 1E 10/11
Level: Non-technical
Average rating:
(5.00, 1 rating)
Friederike Schuur and Rita Ko explain how the Hive (an internal group at USA for UNHCR) and Cloudera Fast Forward Labs transformed USA for UNHCR, enabling the agency to use data science and machine learning (DS/ML) to address the refugee crisis. Along the way, they cover the development and implementation of a DS/ML strategy, identify use cases and success metrics, and showcase the value of DS/ML.
Read more.
2:55pm–3:35pm Wednesday, 09/12/2018
Location: 1E 12/13
Level: Non-technical
Secondary topics:
Data preparation, governance and privacy,
Ethics and Privacy
Andrew Burt (bnh.ai)
Average rating:
(5.00, 2 ratings)
Machine learning is becoming prevalent across industries, creating new types of risk. Managing this risk is quickly becoming the central challenge of major organizations, one that strains data science teams, legal personnel, and the C-suite alike. Andrew Burt shares lessons from past regulations focused on similar technology along with a proposal for new ways to manage risk in ML.
Read more.
2:55pm–3:35pm Wednesday, 09/12/2018
Location: 1E 14
Level: Intermediate
Secondary topics:
Machine Learning in the enterprise,
Media, Marketing, AdvertisingAverage rating:
(4.00, 3 ratings)
Creating a successful big data practice in your organization presents new challenges in managing projects and teams. Ted Malaska and Jonathan Seidman share guidance and best practices to help technical leaders deliver successful projects from planning to implementation.
Read more.
4:35pm–5:15pm Wednesday, 09/12/2018
Location: 1E 10/11
Level: Beginner
Adil Aijaz (Split Software)
Average rating:
(5.00, 1 rating)
Many products, whether data driven or not, chase “the one metric that matters.” It may be engagement, revenue, or conversion, but the common theme is the pursuit of improvement in one metric. Product development teams should instead focus on the design of metrics that measure our goals. Adil Aijaz shares an approach to designing metrics and discusses best practices and common pitfalls.
Read more.
4:35pm–5:15pm Wednesday, 09/12/2018
Location: 1E 12/13
Level: Non-technical
Secondary topics:
Ethics and Privacy,
Machine Learning in the enterprise
Kimberly Nevala (SAS)
Average rating:
(5.00, 1 rating)
Too often, the discussion of AI and ML includes an expectation—if not a requirement—for infallibility. But as we know, this expectation is not realistic. So what’s a company to do? While risk can’t be eliminated, it can be rationalized. Kimberly Nevala demonstrates how an unflinching risk assessment enables AI/ML adoption and deployment.
Read more.
4:35pm–5:15pm Wednesday, 09/12/2018
Location: 1E 14
Level: Non-technical
Secondary topics:
Machine Learning in the enterprise,
Media, Marketing, Advertising
Cassie Kozyrkov (Google)
Average rating:
(4.30, 10 ratings)
Many organizations aren’t aware that they have a blindspot with respect to their lack of data effectiveness, and hiring experts doesn’t seem to help. Cassie Kozyrkov examines what it takes to build a truly data-driven organizational culture and highlights a vital yet often neglected job function: the data science manager.
Read more.
5:25pm–6:05pm Wednesday, 09/12/2018
Location: 1E 12/13
Level: Non-technical
Secondary topics:
Media, Marketing, Advertising
John Thuma (Arcadia Data)
Average rating:
(5.00, 1 rating)
Forget about the fake news; data and analytics in politics is what drives elections. John Thuma shares ethical dilemmas he faced while proposing analytical solutions to the RNC and DNC. Not only did he help causes he disagreed with, but he also armed politicians with real-time data to manipulate voters.
Read more.
5:25pm–6:05pm Wednesday, 09/12/2018
Location: 1E 14
Level: Intermediate
Secondary topics:
Data preparation, governance and privacy
Sanjeev Mohan (Gartner)
Average rating:
(5.00, 1 rating)
If the last few years were spent proving the value of data lakes, the emphasis now is to monetize the big data architecture investments. The rallying cry is to onboard new workloads efficiently. But how do you do so if you don’t know what data is in the lake, the level of its quality, or the trustworthiness of models? Sanjeev Mohan explains why data governance is the linchpin to success.
Read more.
11:20am–12:00pm Thursday, 09/13/2018
Location: 1E 14
Level: Intermediate
Secondary topics:
Machine Learning in the enterprise,
Retail and e-commerce
Mikio Braun (Zalando)
Average rating:
(2.75, 4 ratings)
In order to become "AI ready," an organization not only has to provide the right technical infrastructure for data collection and processing but also must learn new skills. Mikio Braun highlights three pieces companies often miss when trying to become AI ready: making the connection between business problems and AI technology, implementing AI-driven development, and running AI-based projects.
Read more.
11:20am–12:00pm Thursday, 09/13/2018
Location: 1E 10/11
Level: Non-technical
Secondary topics:
Machine Learning in the enterprise,
Retail and e-commerce,
Transportation and Logistics
Michelangelo D'Agostino (ShopRunner)
Average rating:
(4.75, 4 ratings)
Data scientists are hard to hire. But too often, companies struggle to find the right talent only to make avoidable mistakes that cause their best data scientists to leave. From org structure and leadership to tooling, infrastructure, and more, Michelangelo D'Agostino shares concrete (and inexpensive) tips for keeping your data scientists engaged, productive, and adding business value.
Read more.
11:20am–12:00pm Thursday, 09/13/2018
Location: 1E 12/13
Level: Beginner
Secondary topics:
Ethics and Privacy
Nuria Ruiz (Wikimedia)
The Wikipedia community feels strongly that you shouldn’t have to provide personal information to participate in the free knowledge movement. Nuria Ruiz discusses the challenges that this strong privacy stance poses for the Wikimedia Foundation, including how it affects data collection, and details some creative workarounds that allow WMF to calculate metrics in a privacy-conscious way.
Read more.
1:10pm–1:50pm Thursday, 09/13/2018
Location: 1E 12/13
Level: Intermediate
Secondary topics:
Data preparation, governance and privacy,
Ethics and Privacy
Jean-Michel Franco (Talend)
Average rating:
(3.50, 2 ratings)
GDPR is more than another regulation to be handled by your back office. Enacting the GDPR's Data Subject Access Rights (DSAR) requires practical actions. Jean-Michel Franco outlines the practical steps to deploy governed data services.
Read more.
1:10pm–1:50pm Thursday, 09/13/2018
Location: 1E 14
Level: Non-technical
Secondary topics:
Machine Learning in the enterprise,
Transportation and Logistics
Brandy Freitas (Pitney Bowes)
Average rating:
(4.50, 6 ratings)
Data science is an approachable field given the right framing. Often, though, practitioners and executives are describing opportunities using completely different languages. Join Brandy Freitas to develop context and vocabulary around data science topics to help build a culture of data within your organization.
Read more.
2:00pm–2:40pm Thursday, 09/13/2018
Location: 1E 10/11
Level: Beginner
Josh Laurito (Squarespace)
Joshua Laurito explores systems Squarespace built for acquiring and enforcing consistency on obtained data and for inferring conclusions from a company’s marketing and product initiatives. Joshua discusses the intricacies of gathering and evaluating marketing and user data, from raising awareness to driving purchases, and shares results of previous analyses.
Read more.
2:00pm–2:40pm Thursday, 09/13/2018
Location: 1E 12/13
Level: Non-technical
Secondary topics:
Health and Medicine,
Text and Language processing and analysisAverage rating:
(4.00, 5 ratings)
Chiny Driscoll and Jawad Khan offer an overview of a solution by Cloudera and MetiStream that lets healthcare providers automate the extraction, processing, and analysis of clinical notes within an electronic health record in batch or real time, improving care, identifying errors, and recognizing efficiencies in billing and diagnoses.
Read more.
2:00pm–2:40pm Thursday, 09/13/2018
Location: 1E 14
Level: Non-technical
Dean Wampler (Anyscale)
Streaming data systems, so called "fast data," promise accelerated access to information, leading to new innovations and competitive advantages. But they aren't just faster versions of big data. They force architecture changes to meet new demands for reliability and dynamic scalability, more like microservices. Dean Wampler shares what you need to know to exploit fast data successfully.
Read more.
3:30pm–4:10pm Thursday, 09/13/2018
Location: 1E 10/11
Level: Non-technical
Secondary topics:
Data Platforms,
Media, Marketing, Advertising,
Retail and e-commerce
Francesco Mucio (Francescomuc.io)
Average rating:
(3.50, 2 ratings)
Francesco Mucio tells the story of how Zalando went from an old-school BI company to an AI-driven company built on a solid data platform. Along the way, he shares what Zalando learned in the process and the challenges that still lie ahead.
Read more.
3:30pm–4:10pm Thursday, 09/13/2018
Location: 1E 14
Level: Non-technical
Paco Nathan (derwen.ai)
Average rating:
(3.00, 1 rating)
Deep learning works well when you have large labeled datasets, but not every team has those assets. Paco Nathan offers an overview of active learning, an ML variant that incorporates human-in-the-loop computing. Active learning focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore and exploit uncertainty in your data.
Read more.
3:30pm–4:10pm Thursday, 09/13/2018
Location: 1A 08
Level: Non-technical
Secondary topics:
Financial Services
Emily Riederer (Capital One)
Emily Riederer explains how best practices from data science, open source, and open science can solve common business pain points. Using a case example from Capital One, Emily illustrates how designing empathetic analytical tools and fostering a vibrant InnerSource community are keys to developing reproducible and extensible business analysis.
Read more.
4:20pm–5:00pm Thursday, 09/13/2018
Location: 1E 10/11
Level: Beginner
Secondary topics:
Transportation and Logistics
Yasuyuki Kataoka (NTT Innovation Institute, Inc.)
Average rating:
(3.00, 4 ratings)
One of the challenges of sports data analytics is how to deliver machine intelligence beyond a mere real-time monitoring tool. Yasuyuki Kataoka highlights various real-time machine learning models in both IndyCar and Tour de France, sharing real-time data processing architectures, machine learning models, and demonstrations that deliver meaningful insights for players and fans.
Read more.
4:20pm–5:00pm Thursday, 09/13/2018
Location: 1E 12/13
Level: Beginner
Secondary topics:
Machine Learning in the enterpriseAverage rating:
(2.67, 6 ratings)
With the growing buzz around data science, many professionals want to learn how to become a data scientist—the role Harvard Business Review called the "sexiest job of the 21st century." Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.
Read more.
4:20pm–5:00pm Thursday, 09/13/2018
Location: 1A 06/07
Level: Non-technical
Secondary topics:
Machine Learning in the enterprise
Bill Franks (International Institute For Analytics)
Drawing on a recent study of the analytics maturity level of large enterprises by the International Institute for Analytics, Bill Franks discusses how maturity varies by industry, shares key steps organizations can take to move up the maturity scale, and explains how the research correlates analytics maturity with a wide range of success metrics, including financial and reputational measures.
Read more.
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