Sep 23–26, 2019

Data Case Studies

Tuesday, September 24, 2019 | Location: 1A 06

Data is changing every industry it touches. From retail to entertainment to logistics, collecting and analyzing work helps us improve it. A recent MIT study shows organizations that embrace data do 5-6% better a year in competitiveness and profitability—compounded, data is the difference between dominance and obscurity.

In this day-long series of case studies, we bring together a dozen examples of data in action across a wide range of companies and verticals. Get an inside look at the business models, infrastructure, and processes that leading companies have deployed. And hear hard-won lessons you can put to work immediately.

Tuesday, 09/24/2019

9:00am

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9:00am–9:05am Tuesday, 09/24/2019
Tutorial
David Boyle (Audience Strategies)
Introduction to the Data Case Studies daylong tutorial. Read more.

9:05am

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9:05am–9:30am Tuesday, 09/24/2019
Secondary topics:  Financial Services
Rosaria Silipo (KNIME)
Rosaria Silipo reviews a number of AI case studies, from classic customer intelligence to the IoT, from sentiment in social media to user graphs, from free text generation to fraud detection, and so on. You'll leave inspired to apply AI in your own domain. Read more.

9:30am

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9:30am–10:00am Tuesday, 09/24/2019
Richard Evans (Statistics Canada)
Join Richard Evans to find out how Statistics Canada, Canada’s national statistical organization, created and put in place its first AI/ML team, applying Lean Startup principles and a proactive hiring strategy to formulate a strategy that within months was delivering production-ready ML models in a complex and demanding data processing environment. Read more.

10:00am

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10:00am–10:30am Tuesday, 09/24/2019
DCS
Muhammed Idris (Capria VC | TeraCrunch)
Muhammed Y. Idris offers an overview of Atar, the first-ever virtual advocate bot, and shares a practical case study of how technology can help streamline and scale service delivery for refugees. Read more.

10:30am

10:30am–11:00am Tuesday, 09/24/2019
Morning break sponsored by Microsoft (30m)

11:00am

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11:00am–11:30am Tuesday, 09/24/2019
Leah Xu (Spotify)
Spotify Wrapped is a "year in music" for active consumers and artists. Wrapped surfaces nostalgic insights derived from dozens of petabytes of user listening data. Leah Xu sheds light on how Spotify creates Wrapped for hundreds of millions of users in an ecosystem ingesting millions of events per second and discusses the engineering trade-offs given demanding requirements and stringent deadlines. Read more.

11:30am

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11:30am–12:00pm Tuesday, 09/24/2019
Arup Nanda (Capital One)
Arup Nanda explains how combining multiple data elements, businesses, and systems and charting visitor drop-offs, along with A/B testing—using Cloudera, Airflow, and machine learning with regression and classification models—has allowed Priceline to redefine its business, change product design, and offer incentives to customers. Read more.

12:00pm

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12:00pm–12:30pm Tuesday, 09/24/2019
Predictive maintenance predicts the future of machines, using data science to establish the machine’s unique lifecycle and increase efficiency. Using a maritime case study, Victoriya Kalmanovich explains why, in a world full of machines, we need to be the bridge connecting the methods of the past to the opportunities of the future. Read more.

12:30pm

12:30pm–1:30pm Tuesday, 09/24/2019
Lunch (1h)

1:30pm

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1:30pm–2:00pm Tuesday, 09/24/2019
Moise Convolbo (Rakuten)
Customer satisfaction is a key success factor for any business. Moise Convolbo highlights the process to capture relevant customer behavioral data, cluster the user journey by different patterns, and draw conclusions for data-informed business decisions. Read more.

2:00pm

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2:00pm–2:30pm Tuesday, 09/24/2019
DCS
Attribution of media spend is a common problem shared by people in many different roles and industries. Many of the solutions that are simplest and easiest to implement don't drive the right behavior. Tushar Mukherjee shares practical lessons learned developing and applying multivariate attribution models at Lenovo. Read more.

2:30pm

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2:30pm–3:00pm Tuesday, 09/24/2019
DCS
Moderated by:
David Boyle (Audience Strategies)
Panelists:
Richard Evans (Statistics Canada), Leah Xu (Spotify), Victoriya Kalmanovich (Navy), Moise Convolbo (Rakuten)
Often, the difference between a successful data initiative and failed one isn't the data or the technology, but rather its adoption by the wider business. With every business wanting the magic of data but many failing to properly embrace and harness it, we will explore what factors our panelists have seen that led to successes and failures in getting companies to use data products. Read more.

3:00pm

3:00pm–3:30pm Tuesday, 09/24/2019
Afternoon break sponsored by Dataiku (30m)

3:30pm

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3:30pm–4:00pm Tuesday, 09/24/2019
Martin Mendez-Costabel (Bayer Crop Science)
Cloud architecture is an extremely flexible environment for deploying solutions. However, the first build of a solution, even using open source software, may quickly exceed initial cost estimates and could outpace ROI if not managed properly. Martin Mendez-Costabel explains how Bayer Crop Science manages its geospatial platform and how it increased ROI. Read more.

4:00pm

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4:00pm–4:30pm Tuesday, 09/24/2019
Secondary topics:  Health and Medicine
gloria macia (Roche AG)
Healthcare is emerging as a prominent area for AI applications, but innovators aiming to seize this chance face one major issue: achieving regulatory compliance. Using a real industry case study, Gloria Macia walks you through the current American and European regulatory frameworks for medical devices and provides a step-by-step guide to market for AI applications. Read more.

4:30pm

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4:30pm–5:00pm Tuesday, 09/24/2019
Gwen Campbell (Revibe Technologies)
It’s no surprise that Revibe needed to learn how to evolve to satiate the current data hungry market. It launched its first hardware-only device in 2015 and quickly learned that to stay alive, the company needed to get its hands into data. Gwen Campbell discusses Revibe's metamorphosis from a hardware company to a data company and shares lessons learned along the way. Read more.

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