Presented By O'Reilly and Cloudera
Make Data Work
31 May–1 June 2016: Training
1 June–3 June 2016: Conference
London, UK

Ecommerce conference sessions

14:55–15:35 Friday, 3/06/2016
Apache Eagle is an open source monitoring solution to instantly identify access to sensitive data, recognize malicious activities, and take action. Arun Karthick Manoharan, Edward Zhang, and Chaitali Gupta explain how Eagle helps secure a Hadoop cluster using policy-based and machine-learning user-profile-based detection and alerting.
14:05–14:45 Friday, 3/06/2016
Marton Trencseni (Facebook)
At first glance A/B testing is a simple matter: take a few numbers, put them into an online calculator, and read off the statistical significance. But in fact it's a complex topic with amazing opportunities (and pitfalls) for organizations. Marton Trencseni offers a deep dive into A/B testing to provide attendees the information needed to improve their organizations' experimentation cultures.
16:10–16:30 Wednesday, 1/06/2016
Pete Williams (Marks and Spencer)
Starting a big data journey by bagging a unicorn and corralling it in your newly acquired big data stable will not necessarily lead to success or lasting change. So how do you drive value from big data? Drawing on first-hand experience at Marks and Spencer, Pete Williams shares practical examples and advice on how to take your data culture and capability from walk through trot to gallop.
9:00–12:30 Wednesday, 1/06/2016
Jonathan Seidman (Cloudera), Mark Grover (Cloudera), Gwen Shapira (Confluent), Ted Malaska (Blizzard)
Jonathan Seidman, Mark Grover, Gwen Shapira, and Ted Malaska walk attendees through an end-to-end case study of building a fraud detection system, providing a concrete example of how to architect and implement real-time systems.
14:30–15:00 Wednesday, 1/06/2016
Mikio Braun (Zalando SE)
Mikio Braun explains why, in practice, hardcore data science is not just about learning methods but also about bringing these methods to production. This does not mean simply reimplementing methods in production systems. Rather, you must successfully deal with issues like data updates, cultural differences between data science and developers, and how to monitor and test in practice.
12:05–12:45 Thursday, 2/06/2016
Dan Jermyn (Royal Bank of Scotland), Connor Carreras (Trifacta)
Big data provides an unprecedented opportunity to really understand and engage with your customers, but only if you have the keys to unlock the value in the data. Through examples from the Royal Bank of Scotland, Dan Jermyn and Connor Carreras explain how to use data wrangling to harness the power of data stored on Hadoop and deliver personalized interactions to increase customer satisfaction.
14:55–15:35 Friday, 3/06/2016
Nicholas Turner (Incited)
Nick Turner offers an insightful view on how technology is delivering self-service analytics through visualization and enabling business users to quickly explore their data at scale.
14:55–15:35 Thursday, 2/06/2016
Lucian Lita (Intuit), Mita Mahadevan (Intuit), Shalin Mantri (Uber), Gabrielle Gianelli (Etsy)
A data-driven culture empowers companies to deliver greater value to their customers, yet many organizations still struggle to break down cultural barriers and drive data-driven innovation across their products. Lucian Lita, Mita Mahadevan, Shalin Mantri, and Gabrielle Gianelli explore Intuit's, Uber's, and Etsy's A/B platforms, which enable experimentation and engender a data-driven mentality.
9:05–9:30 Wednesday, 1/06/2016
Mounia Lalmas (Yahoo)
Mounia Lalmas offers an overview of work aimed at understanding the user preclick experience of ads and building a learning framework to identify ads with low preclick quality.
10:00–10:30 Wednesday, 1/06/2016
Danny Bickson (1972)
A Netflix competition triggered a major academic research effort in recommender systems. However, there is still a big gap between academic research and industry. Danny Bickson covers the current state of recommender systems in industry and explains why, while user historical purchase data is understood very well, recommenders based on images and text are just starting to pick up.
11:15–11:55 Thursday, 2/06/2016
Emily Sommer (Etsy)
Bootstrapping is a statistical technique that resamples data many times over—an effective method for determining confidence in A/B test results but an expensive procedure in a world of big data. Emily Sommer explains how Etsy implemented the Bag of Little Bootstraps, a clever take on bootstrapping that involves examining many smaller subsets of one's data.