Presented By
O’Reilly + Cloudera
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
San Francisco, CA
March 25-28, 2019
San Francisco, CA
– Alistair Croll, Strata Conference Chair
Make data work for business.
The 2019 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.
Strata Business Summit provides 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
Platinum pass holders have access to to Strata Business Summit Mon–Thurs. Gold and Silver pass holders have access to Strata Business Summit on Tues–Thurs. Bronze pass holders have access to Strata Business Summit on Wed–Thurs.
| Monday, Mar 25 - Tuesday, Mar 26: 2-Day Training (Platinum & Training passes) |
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| Tuesday Mar 26: Tutorials (Gold & Silver passes) |
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| Wednesday Mar 27: Keynotes & Sessions (Platinum, Gold, Silver & Bronze passes) |
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| 8:45am | Location: Ballroom Strata Data Conference Keynotes |
| 10:30am Morning break |
| Thursday Mar 28: Keynotes & Sessions (Platinum, Gold, Silver & Bronze passes) |
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| 8:45am | Location: Ballroom Strata Data Conference Keynotes |
| 10:30am Morning break |
9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Location: 2010
Secondary topics:
AI and machine learning in the enterprise
Average rating:
(4.50, 4 ratings)
Michael Li and Rich Ott offer a nontechnical overview of AI and data science. Learn common techniques, how to apply them in your organization, and common pitfalls to avoid. You’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.
Read more.
9:00am–12:30pm Tuesday, March 26, 2019
Location: 2003
Secondary topics:
AI and machine learning in the enterprise
Average rating:
(4.60, 15 ratings)
The honeymoon era of data science is ending, and accountability is coming. Successful data science leaders must deliver measurable impact on an increasing share of an enterprise's KPIs. Joshua Poduska, Kimberly Shenk, and Mac Steele explain how leading organizations take a holistic approach to people, process, and technology to build a sustainable competitive advantage.
Read more.
9:00am–5:00pm Tuesday, March 26, 2019
Location: 2022
Alex Kudriashova (Astro Digital),
Jonathan Francis (Starbucks),
JoLynn Lavin (General Mills),
Robin Way (Corios),
June Andrews (GE),
Kyungtaak Noh (SK Telecom),
Taposh DuttaRoy (Kaiser Permanente),
Sabrina Dahlgren (Kaiser Permanente),
Craig Rowley (Columbia Sportswear),
Ambal Balakrishnan (IBM),
Benjamin Glicksberg (UCSF),
Patrick Lucey (Stats Perform),
Rhonda Textor (True Fit)
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, March 26, 2019
Location: 2003
Secondary topics:
AI and machine learning in the enterprise,
Ethics,
Security and Privacy
Andrew Burt (bnh.ai),
Steven Touw (Immuta),
richard geering (Immuta),
Joseph Regensburger (Immuta),
Alfred Rossi (Immuta)
Average rating:
(5.00, 2 ratings)
As ML becomes increasingly important for businesses and data science teams alike, managing its risks is quickly becoming one of the biggest challenges to the technology’s widespread adoption. Join Andrew Bur, Steven Touw, Richard Geering, Joseph Regensburger, and Alfred Rossi for a hands-on overview of how to train, validate, and audit machine learning models (ML) in practice.
Read more.
11:00am–11:40am Wednesday, March 27, 2019
Location: 2020
Secondary topics:
AI and Data technologies in the cloud,
AI and machine learning in the enterprise,
Security and Privacy
Mike Olson (Cloudera)
Average rating:
(3.80, 5 ratings)
It's easier than ever to collect data, but managing it securely in compliance with regulations and legal constraints is harder. Mike Olson discusses the risks and the issues that matter most and explains how an enterprise data cloud that embraces your data center and the public cloud in combination can address them, delivering real business results for your organization.
Read more.
11:00am–11:40am Wednesday, March 27, 2019
Location: 2024
Secondary topics:
Media, Marketing, Advertising
Average rating:
(4.50, 2 ratings)
As a fully closed model economy, games offer a unique opportunity to use analytics to create unique purchase opportunities for customers. Bysshe Easton and Thomas Dobbs explain how KIXEYE uses machine learning to create personalized offer recommendations for its customers, resulting in significantly increased monetization and retention.
Read more.
11:00am–11:40am Wednesday, March 27, 2019
Location: 2018
Secondary topics:
AI and Data technologies in the cloud,
Visualization, Design, and UX
Average rating:
(3.40, 5 ratings)
Jaipaul Agonus and Daniel Monteiro do Carmo Rosa detail big data analytics and visualization practices and tools used by FINRA to support machine learning and other surveillance activities that the Market Regulation Department conducts in the AWS cloud.
Read more.
11:50am–12:30pm Wednesday, March 27, 2019
Location: 2018
Swatee Singh (American Express)
Average rating:
(4.00, 3 ratings)
Organizations developing artificial intelligence and machine learning (AI/ML)-powered applications face two existential questions: Should they consider a fully or partially hybrid cloud environment for AI/ML deployments, and which public cloud will give them the most features and capabilities? Swatee Singh discusses available options for companies facing these challenges.
Read more.
11:50am–12:30pm Wednesday, March 27, 2019
Location: 2020
Secondary topics:
Ethics
Bill Franks (International Institute For Analytics)
Average rating:
(4.67, 3 ratings)
Concerns are constantly being raised today about what data is appropriate to collect and how (or if) it should be analyzed. There are many ethical, privacy, and legal issues to consider, and no clear standards exist in many cases as to what is fair and what is foul. Bill Franks explores a variety of dilemmas and provides some guidance on how to approach them.
Read more.
11:50am–12:30pm Wednesday, March 27, 2019
Location: 2024
Secondary topics:
AI and machine learning in the enterprise
Maryam Jahanshahi (TapRecruit)
Average rating:
(4.80, 5 ratings)
Hiring teams largely rely on both intuition and experience to scout talent for data science and data engineering roles. Drawing on results from analyzing over 15 million jobs and their outcomes, Maryam Jahanshahi interrogates these “common sense” judgments to determine whether they help or hurt hiring of data scientists and engineers.
Read more.
2:40pm–3:20pm Wednesday, March 27, 2019
Location: 2018
Secondary topics:
Data preparation, data governance, and data lineage
John Haddad (Informatica)
Average rating:
(4.60, 5 ratings)
Just like a powerful space telescope that scans the universe, a data catalog scans the data universe to help data scientists and analysts find data, collaborate, and curate data for analytic and data governance projects. John Haddad explains how a data catalog can help you find the data you need and trust for analytic and data governance projects.
Read more.
2:40pm–3:20pm Wednesday, March 27, 2019
Location: 2024
Secondary topics:
AI and machine learning in the enterprise
Average rating:
(4.79, 14 ratings)
A/B testing has revealed the fallibility in human intuition that typically drives business decisions. Eric Colson and Daragh Sibley describe some types of systematic errors domain experts commit, explain how cognitive biases arise from heuristic reasoning processes, and share several mechanisms to mitigate these human limitations and improve decision making.
Read more.
2:40pm–3:20pm Wednesday, March 27, 2019
Location: 2020
Greg Quist (SmartCover Systems)
Average rating:
(4.00, 1 rating)
SmartCover Systems has been providing an IoT solution to its customers for 15 years, using techniques honed in defense and remote sensing, gathering more than 200 million hours of sewer data. Greg Quist shares case studies and results from applying the IoT and AI to underground infrastructure.
Read more.
4:20pm–5:00pm Wednesday, March 27, 2019
Location: 2020
Secondary topics:
AI and machine learning in the enterprise,
Data preparation, data governance, and data lineage
Paco Nathan (derwen.ai)
Average rating:
(3.67, 6 ratings)
Effective data governance is foundational for AI adoption in enterprise, but it's an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more. Join in to learn what impact machine learning has on data governance and vice versa.
Read more.
4:20pm–5:00pm Wednesday, March 27, 2019
Location: 2018
Secondary topics:
Visualization, Design, and UX
Maxime Beauchemin (Lyft)
Average rating:
(4.50, 4 ratings)
Maxime Beauchemin offers an overview of Apache Superset, discussing the project's open source development dynamics, security, architecture, and underlying technologies as well as the key items on its roadmap.
Read more.
4:20pm–5:00pm Wednesday, March 27, 2019
Location: 2024
Ashley Fontana (Zetta),
Katherine Boyle (General Catalyst),
Sarah Catanzaro (Amplify Partners),
Arif Janmohamed (Lightspeed Venture Partners),
Lan Xuezhao (Basis Set Ventures)
Average rating:
(4.00, 1 rating)
What does it mean to be an AI investor? How is this approach different from traditional venture capital? Ash Fontana and Katherine Boyle share their perspectives on investments in machine intelligence and data science.
Read more.
5:10pm–5:50pm Wednesday, March 27, 2019
Location: 2018
Secondary topics:
AI and machine learning in the enterprise
Dave Stuart (Department of Defense )
Average rating:
(4.38, 8 ratings)
Many organizations look to add data science to their skill portfolios through the hiring of data science experts. Dave Stuart shares a complementary way to build a data science-savvy workforce that nets tremendous value by using Jupyter to add introductory data science practices to domain experts and business analysts.
Read more.
5:10pm–5:50pm Wednesday, March 27, 2019
Location: 2020
Secondary topics:
AI and machine learning in the enterprise
BARKHA GVALANI (GV)
Average rating:
(2.50, 4 ratings)
How do you decide if you should invest in upskilling business teams? The question is no longer "if" but "when" and "how." Barkha Gvalani shares a framework for developing and delivering analytics training to nontechnical users.
Read more.
5:10pm–5:50pm Wednesday, March 27, 2019
Location: 2024
Secondary topics:
Media, Marketing, Advertising
Average rating:
(3.00, 1 rating)
Eric Bradlow and Zachery Anderson discuss the Wharton Customer Analytics Initiative research opportunity process and explain how some of EA’s solved some of its business problems by sharing its data with 11 teams of researchers from around the world.
Read more.
11:00am–11:40am Thursday, March 28, 2019
Location: 2018
Secondary topics:
AI and machine learning in the enterprise,
Health and Medicine
Marc Paradis (UnitedHealth Group)
Average rating:
(4.75, 4 ratings)
Data Science University (DSU) was established to bring analytics education to UnitedHealth Group, the world’s largest healthcare company, with over 270,000 employees. Marc Paradis explains how DSU was built out over time in an era of rapidly changing analytics technology and capabilities in an industry ripe for disruption, covering the challenges faced and lessons learned.
Read more.
11:00am–11:40am Thursday, March 28, 2019
Location: 2020
Secondary topics:
Financial Services,
Security and Privacy
Nick Curcuru (Mastercard)
Average rating:
(4.50, 2 ratings)
Data—in part, harvested personal data—brings industries unprecedented insights about customer behavior. We know more about our customers and neighbors than at any other time in history, but we need to avoid crossing the "creepy" line. Nick Curcuru discusses how ethical behavior drives trust, especially in today's IoT age.
Read more.
11:50am–12:30pm Thursday, March 28, 2019
Location: 2018
Secondary topics:
Data Platforms,
Retail and e-commerce
Francesco Mucio (Francescomuc.io)
Average rating:
(4.00, 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.
11:50am–12:30pm Thursday, March 28, 2019
Location: 2020
Secondary topics:
Model lifecycle management
David Talby (Pacific AI)
Average rating:
(4.90, 10 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.
1:50pm–2:30pm Thursday, March 28, 2019
Location: 2020
Secondary topics:
Open Data, Data Generation and Data Networks
Average rating:
(4.80, 5 ratings)
At the rate data sources are multiplying, business value can often be developed faster by joining data sources rather than mining a single source to the very end. Ken Johnston and Ankit Srivastava share four years of hands-on practical experience sourcing and integrating massive numbers of data sources to build the Microsoft Business Intelligence Graph (M360 BIG).
Read more.
1:50pm–2:30pm Thursday, March 28, 2019
Location: 2018
Secondary topics:
AI and machine learning in the enterprise
Stuart Buck (Arnold Ventures)
Average rating:
(4.50, 4 ratings)
Academic research has been plagued by a reproducibility crisis in fields ranging from medicine to psychology. Stuart Buck explains how to take precautions in your data analysis and experiments so as to avoid those reproducibility problems.
Read more.
2:40pm–3:20pm Thursday, March 28, 2019
Location: 2018
Secondary topics:
Health and Medicine,
Open Data, Data Generation and Data Networks
Mei Fung (People Centered Internet)
Average rating:
(4.67, 3 ratings)
Data sharing necessitates stakeholders and populations of people to come together to learn the benefits, risks, challenges, and known and unknown "unknowns." Data sharing policies and frameworks require increasing levels of trust, which takes time to build. Join Mei Fung for trail-blazing stories from Solano County, California, and ASEAN (SE Asia), which offer important insights
Read more.
2:40pm–3:20pm Thursday, March 28, 2019
Location: 2020
Secondary topics:
Security and Privacy
Average rating:
(4.33, 3 ratings)
The implications of new privacy regulations for data management and analytics, such as the General Data Protection Regulation (GDPR) and the upcoming California Consumer Protection Act (CCPA), can seem complex. Mark Donsky and Nikki Rouda highlight aspects of the rules and outline the approaches that will assist with compliance.
Read more.
3:50pm–4:30pm Thursday, March 28, 2019
Location: 2018
Secondary topics:
Graph technologies and analytics,
Media, Marketing, Advertising,
Storage,
Text and Language processing and analysis
Pierre Romera (International Consortium of Investigative Journalists (ICIJ))
Average rating:
(4.67, 6 ratings)
The ICIJ was the team behind the Panama Papers and Paradise Papers. Pierre Romera offers a behind-the-scenes look into the ICIJ's process and explores the challenges in handling 1.4 TB of data (in many different formats)—and making it available securely to journalists all over the world.
Read more.
3:50pm–4:30pm Thursday, March 28, 2019
Location: 2020
Secondary topics:
Streaming, realtime analytics, and IoT
Dean Wampler (Anyscale)
Average rating:
(4.33, 6 ratings)
Your team is building machine learning capabilities. Dean Wampler demonstrates how to integrate these capabilities in streaming data pipelines so you can leverage the results quickly and update them as needed and covers challenges such as how to build long-running services that are very reliable and scalable and how to combine a spectrum of very different tools, from data science to operations.
Read more.
4:40pm–5:20pm Thursday, March 28, 2019
Location: 2020
Secondary topics:
AI and machine learning in the enterprise
Michael Li (The Data Incubator)
Average rating:
(3.75, 4 ratings)
As their data and AI teams scale from one to thousands of employees and the maturity of their analytics capabilities evolve, companies find that the analytics journey is not always smooth. Drawing on experiences gleaned from dozens of clients, Michael Li discusses organizational growing pains and the best practices that successful executives have adopted to scale and grow their team.
Read more.
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