Presented By
O’Reilly + Cloudera
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
San Francisco, CA
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Data Case Studies
9:00am - 5:00pm, Tuesday, March 26, 2019
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.
7:30am–9:00am Tuesday, 03/26/2019
Location: 2nd floor lobby
Early morning coffee
(1h 30m)
Average rating:
(5.00, 3 ratings)
Paco Nathan welcomes you to Data Case Studies.
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Data Case Studies
Location: 2022
Rhonda Textor (True Fit)
Average rating:
(4.25, 4 ratings)
Fashion recommendation problems are characterized by sparse datasets and large catalogs of styles that have short lifespans—areas traditional transaction-based approaches are not well suited to address. Rhonda Textor explains how to transform raw retail data into scalable recommendations using widely available machine learning libraries.
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Secondary topics:
Transportation and Logistics,
Visualization, Design, and UX
Alex Kudriashova (Astro Digital)
Average rating:
(4.40, 5 ratings)
It has become possible to use satellites to observe food growing at a global scale—using daily satellite images to glean agriculture-specific insights and predict productivity. Alex Kudriashova offers an overview of current publicly available satellite imagery data and explains how to inject it into your data pipeline and train and deploy AI/ML models based on it.
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Data Case Studies
Location: 2022
Patrick Lucey (Stats Perform)
Average rating:
(5.00, 4 ratings)
Patrick Lucey describes methods to find play similarity using multiagent trajectory data and predict fine-grained plays, using examples using STATS SportVU data in basketball and soccer. Patrick then discusses how to go beyond center-of-mass tracking (i.e., dots) and capture body-pose information from broadcast video to take analysis to the next level.
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10:30am–11:00am Tuesday, 03/26/2019
Location: 2nd floor lobby
Morning break
(30m)
Secondary topics:
AI and machine learning in the enterprise,
Retail and e-commerce
Jonathan Francis (Starbucks)
Average rating:
(2.25, 4 ratings)
Jon Francis explains how he and Arun Veetill improved the performance of an AI-based personalization solution by 2x through continuous AI-enabled experimentation and learning.
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Secondary topics:
AI and machine learning in the enterprise,
Text and Language processing and analysis
JoLynn Lavin (General Mills)
Average rating:
(4.33, 3 ratings)
General Mills engages millions of consumers in conversations every year through traditional 1-800 numbers and text messaging with call center agents, online conversations on social media, comments on its recipe websites, and chatbots. JoLynn Lavin explains how General Mills applies machine learning to listen to the voice of its customers, arguably the most powerful force in today’s market.
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Secondary topics:
Visualization, Design, and UX
Robin Way (Corios)
Average rating:
(2.50, 4 ratings)
Why do we call it "artificial" intelligence? Did AI write itself? No, of course it didn't. We invented the math, built the computer technology, and harnessed the data sources. Robin Way argues that we should reposition what we do as "organic intelligence": we apply math and computers to data to tell a story about the human experience. Join in to learn what organic intelligence is all about.
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12:30pm–1:30pm Tuesday, 03/26/2019
Location: 2nd and 3rd floor lobbies
Lunch
(1h)
June Andrews (GE)
Average rating:
(3.75, 4 ratings)
Companies have adopted data into their DNA using a variety of methods, including data driven, data enabled, and data informed, but many implementations have fallen short of the promised ROI, the result of a gap between the cost of investing in people and infrastructure and the business value delivered. June Andrews investigates the ROI of using data and shows how to become data competitive.
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Secondary topics:
Visualization, Design, and UX
Kyungtaak Noh (SK Telecom)
Average rating:
(2.00, 4 ratings)
In the analysis of the mobile world, everyone starts with the question, "Where?" SK Telecom is trying to meet these needs. Kyungtaak Noh explains how the company provides geospatial analysis by processing geospatial data through Druid with Lucene.
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Secondary topics:
Automation in data science and big data,
Ethics,
Health and Medicine
Average rating:
(3.00, 3 ratings)
The healthcare industry requires accuracy and highly interpretable models, but the data is usually plagued by missing information and incorrect values. Enter AutoML and auto-model interpretability. Taposh DuttaRoy and Sabrina Dahlgren discuss tools and strategies for AutoML and interpretability and explain how KP uses them to improve time to develop and deploy highly interpretable models.
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3:00pm–3:30pm Tuesday, 03/26/2019
Location: 2nd floor lobby
Afternoon break
(30m)
Secondary topics:
AI and machine learning in the enterprise
Craig Rowley (Columbia Sportswear)
Average rating:
(4.25, 4 ratings)
Few analytics organizations are successfully delivering actionable insights that make it further than a Keynote or PowerPoint presentation. Join Craig Rowley to learn why successful analytics projects must also consider the human element.
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Secondary topics:
Visualization, Design, and UX
Ambal Balakrishnan (IBM)
Average rating:
(3.50, 6 ratings)
Whether you are a tech or biz professional, you must master the art of visual storytelling with data. But first, you have to find the story worth telling that's hidden in your data. Join Ambal Balakrishnan to learn how. As with many things in life, visual storytelling with data will take practice. But that doesn't mean you can't accelerate your learning from others' mistakes and successes.
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Benjamin Glicksberg (UCSF)
Average rating:
(4.75, 4 ratings)
Sequencing cancer genomes has transformed how we diagnose and treat the deadliest disease in America: cancer. Benjamin Glicksberg explains how coupling cancer genomic data with treatment data through the blockchain will empower patients and citizen scientists to rapidly advance cancer research.
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