20–23 April 2020

Case Studies

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11:1511:55 Wednesday, April 22, 2020
Location: Capital Suite 11
Conor Sayles (Bank of Ireland)
Conor Sayles details how Bank of Ireland led a data value realization strategy, yielding a return of over €70M and incorporating infrastructure investment, agile management, and design thinking. An analytic system including Tableau, Teradata, SAS, and Cloudera provides a cornerstone for decision making across multiple functions. Underlying the success is a growing data community. Read more.
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11:1511:55 Wednesday, April 22, 2020
Location: S11 D
Flávio Santos (Spotify)
Data has been a first-class citizen at Spotify since the beginning. It is an important component of the ecosystem that allows data scientists and analysts to improve features and develop new products. Events collected from instrumented clients and backends go through a complex system before they are available for internal teams. This talk goes deep into how event delivery is built inside Spotify. Read more.
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12:0512:45 Wednesday, April 22, 2020
Location: Capital Suite 11
Kumar Sambhav (Barclays)
People analytics has become key to unlocking human resource insights to understand and measure policy effectiveness and implement improvements by embedding intelligent decision making. Kumar Sambhav draws on people analytics use cases from Barclays to discuss the pipeline it developed and the corresponding controls and governance model that was implemented. Read more.
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12:0512:45 Wednesday, April 22, 2020
Location: S11 D
Enterprise IT has been delivering BI on Hadoop for a few years, but frustrated business analysts and data scientists want self-service data and ML in the cloud, so they can go much faster. Phillip Radley explores the challenges when enterprise IT teams have to quickly pivot from caring for an elephant on-premises to farming herds of clusters, pipelines, and models in clouds. Read more.
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14:0514:45 Wednesday, April 22, 2020
Location: Capital Suite 11
bhargavi reddy (Netflix)
Bhargavi Reddy outlines the driving forces for effective data lifecycle management (DLM) at Netflix and the current state of Netflix’s S3 data warehouse, offers an overview of the S3 access logs collection process using SQS and Apache Iceberg, and details how the S3 logs are used for improving the efficiency and security posture of Netflix's cloud infrastructure at scale in the DLM realm. Read more.
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14:0514:45 Wednesday, April 22, 2020
Location: S11 D
David Benham (Chesapeake Energy)
Cloudera and Chesapeake Energy present a real-world use case for anomaly detection at scale to reduce time-to-action in response to pipeline blockage. You'll apply these to the use case, including the business context, the problem, the machine learning approach taken, the technical architecture employed, and the lessons learned. Read more.
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14:5515:35 Wednesday, April 22, 2020
Location: Capital Suite 11
Martin Goodson (Evolution AI)
Combining the exacting requirements of a leading data provider with a university’s expertise led to breakthrough technology that reads balance sheets more accurately than humans. But the journey wasn’t smooth. Martin Goodson shares the project’s structure, outcomes, and mistakes made along the way. Read more.
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14:5515:35 Wednesday, April 22, 2020
Location: S11 D
Gabor Kotalik (Deutsche Telekom), Vaclav Surovec (Deutsche Telekom)
Deutsche Telekom is fourth biggest telecommunication company in the world, and every day millions of its customers use their mobile services in roaming. Gabor Kotalik and Václav Surovec explain how the company designed and built its machine learning processes on top of the Cloudera Hadoop cluster to support its commercial roaming business. Read more.
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16:3517:15 Wednesday, April 22, 2020
Location: Capital Suite 11
Kelly Carmody (Dramatic Solutions), Yaakov Bressler (Dramatic Solutions)
Dynamic pricing implemented properly by Broadway, the West End, and smaller theaters shows the promise of increasing revenue while selling more tickets and lowering prices. Kelly Carmody and Yaakov Bressler dig into their work proving the statistics behind dynamic pricing using probability distributions and a variety of modeling techniques in Python. Read more.
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16:3517:15 Wednesday, April 22, 2020
Location: S11 D
Ben Sykes (Netflix)
Ensuring a consistently great Netflix experience while pushing innovative technology updates is no easy feat. Ben Sykes takes a look at how Netflix turns log streams into real-time metrics to provide visibility into how devices are performing in the field. You'll discover some of the lessons Netflix learned while optimizing Druid to handle its load. Read more.
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17:2518:05 Wednesday, April 22, 2020
Location: Capital Suite 11
Mike Lutz (Samtec)
Netflix proposed a novel best practice in using Jupyter notebooks as glue for working in the big data and AI-processing domain. You can follow a manufacturing company's adventure as it tries to implement Netflix's ideas on a dramatically smaller scale. Mike Lutz explains how Netflix's idea can be useful even for the small fry. Read more.
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17:2518:05 Wednesday, April 22, 2020
Location: S11 D
Gabriel Straub walks you through the BBC's experience with building a framework to build public service recommendations for the BBC, deploying in multiple clouds, following our machine learning principles, and reflecting editorial values to inform, educate, and entertain. Read more.
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11:1511:55 Thursday, April 23, 2020
Location: Capital Suite 11
Andras Szabo (Pivigo), Adam Hill (HAL24K)
Wildfires are a major environmental and health risk, with a frequency that has increased dramatically in the past decade. Early detection is critical, however most often wildfires are only discovered by eye-witness accounts. In this talk we will tell about a data science partnership between HAL24K and Pivigo aimed at building an automated wildfire detection system using NOAA satellite data. Read more.
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11:1511:55 Thursday, April 23, 2020
Location: S11 D
Melissa Singh (TD Bank), Pirabu Pathmasenan (TD Bank)
Melissa Singh and Pirabu Pathmasenan walk you through TD Bank's data-driven transformation. You'll learn how it started, where it is today, and where it's going with big data and AI. You'll uncover shifts in the company's cultural paradigm, along with the technical tools and practices used to transition traditional analytics teams into the world of big data and AI. Read more.
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12:0512:45 Thursday, April 23, 2020
Location: Capital Suite 11
Rick Houlihan (Amazon Web Services)
When Amazon decided to migrate thousands of application services to NoSQL, many of those services required complex relational models that could not be reduced to simple key-value access patterns. The most commonly documented use cases for NoSQL are simplistic. this session shows how to model complex relational data efficiently in denormalized structures. Read more.
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12:0512:45 Thursday, April 23, 2020
Location: S11 D
Criteo's infrastructure provides capacity and connectivity to host its platform and applications; the evolution of its infrastructure is driven by the ability to forecast traffic demand. Hamlet Jesse Medina Ruiz explains how Criteo uses Bayesian dynamic time series models to accurately forecast its traffic load and optimize hardware resources across data centers. Read more.
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14:0514:45 Thursday, April 23, 2020
Location: Capital Suite 11
Almost two years ago EnBW developed its core beliefs for the role of AI at EnBW and derived concrete actions that need to be taken to scale its AI activities. Rainer Hoffmann and Frank Säuberlich describe the actions and the challenges EnBW has faced on its journey so far and its approach to mastering these challenges. Read more.
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14:5515:35 Thursday, April 23, 2020
Location: Capital Suite 11
Nutsa Abazadze (TBC Bank), Aleksandre Lomadze (TBC Bank)
We will tell you how our failed attempt to build an ML model brought us to discovering institutional problems and kicked off improvement of existing business processes so that we would collect quality data for future modeling; and how we still managed to increase deposit profitability by 20% in the process. Read more.
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14:5515:35 Thursday, April 23, 2020
Location: S11 D
Lukumon Oyedele (University of the West of England)
The time spent by frontline construction workers can be reduced by 50% through a hands-free assembly support building information modeling (BIM) system. Lukumon Oyedele explains how to make it possible for onsite construction workers to seek support from BIM through verbal query and augmented display through conversational AI and augmented reality (AR). Read more.
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16:3517:15 Thursday, April 23, 2020
Location: Capital Suite 11
Kim Nilsson (Pivigo), Robert Grieg-Gran (Mindful Chef)
Mindful Chef is a health-focused company that delivers weekly recipe boxes. In order to create a more personalised experience for their customers, they teamed up with Pivigo to develop an innovative recommender system. In this talk we will tell about this project and the development of a novel approach to understanding user taste that had an unexpectedly large impact on recommendation accuracy. Read more.
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16:3517:15 Thursday, April 23, 2020
Location: S11 D
Jennifer Yang (Wells Fargo ECS)
Traditional rule-based data quality management methodology is costly and poorly scalable. It requires subject matter experts within business, data and technology domains. The presentation will discuss a use case that demonstrates how the machine learning techniques can be used in the data quality management on the big data platform in the financial industry. Read more.

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