Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Schedule: Data-driven business management sessions

9:00am–12:30pm Tuesday, 09/11/2018
Location: 1A 12/14 Level: Non-technical
Secondary topics:  Machine Learning in the enterprise
Joshua Poduska (Domino Data Lab), Patrick Harrison (S&P Global)
Average 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 (Hortonworks)
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 enterprise
Average 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: Expo Hall Level: Non-technical
Secondary topics:  Data Integration and Data Pipelines, Financial Services
Usama Fayyad (Open Insights & OODA Health, Inc.), Troels Oerting (WEF Global Cybersecurity Center)
Average rating: ***..
(3.00, 1 rating)
Usama Fayyad and Troels Oerting share outcomes and lessons learned from building and deploying a global data fusion, incident analysis/visualization, and effective cybersecurity defense based on big data and AI at a major EU bank, in collaboration with several financial services institutions. 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 enterprise
Tony Baer (dbInsight), Florian Douetteau (DATAIKU)
Average 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.
2:55pm–3:35pm Wednesday, 09/12/2018
Location: 1E 10/11 Level: Non-technical
Friederike Schuur (Cloudera), Rita Ko (USA for UNHCR)
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.
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.
4:35pm–5:15pm Wednesday, 09/12/2018
Location: 1A 12/14
Sarah Catanzaro (Amplify Partners), Rama Sekhar (Norwest Venture Partners), Zavain Dar (Lux Capital), Jonathan Lehr (Work-Bench), Crystal Huang (NEA)
In this panel discussion, venture capital investors explain how startups can accelerate enterprise adoption of machine learning and explore the new tech trends that will give rise to the next transformation in the big data landscape. 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
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.
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.
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.
3:30pm–4:10pm Thursday, 09/13/2018
Location: 1A 01/02 Level: Intermediate
Ajay Kulkarni (TimescaleDB)
Average rating: ****.
(4.00, 2 ratings)
Ajay Kulkarni explores the underlying changes that are characterizing the next wave of computing and shares several ways in which individual businesses and overall industries will be transformed. 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 enterprise
Francesca Lazzeri (Microsoft), Jaya Susan Mathew (Microsoft)
Average 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.