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

Financial services conference sessions

16:35–17:15 Friday, 3/06/2016
Calum Murray (Intuit)
As Intuit evolved QuickBooks, Payroll, Payments, and other product offerings into a SaaS business and an open cloud platform, it quickly became apparent that business analytics could no longer be treated as an afterthought but had to be part of the platform architecture as a first-class concern. Calum Murray outlines key design considerations when architecting analytics into your SaaS platform.
17:25–18:05 Thursday, 2/06/2016
Mark Donsky (Okera), Chang She (Cloudera)
Mark Donsky and Chang She explore canonical case studies that demonstrate how leading banks, healthcare, and pharmaceutical organizations are tackling Hadoop governance challenges head-on. You'll learn how to ensure data doesn't get lost, help users find and trust the data they need, and protect yourself against a data breach—all at Hadoop scale.
16:30–17:00 Wednesday, 1/06/2016
Michal Galas (University College London)
Experimental computational simulation environments are increasingly being developed by major financial institutions to model their analytic algorithms. Michal Galas introduces the key concepts underlying these environments, which rely on big data analytics to enable large-scale testing, optimization, and monitoring of algorithms running in the virtual or real mode.
9:00–12:30 Wednesday, 1/06/2016
Jonathan Seidman (Cloudera), Mark Grover (Lyft), Gwen Shapira (Confluent), Ted Malaska (Capital One)
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.
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.
12:05–12:45 Friday, 3/06/2016
Hellmar Becker (Hortonworks), Frank Albers (ING)
How do you connect a Hadoop cluster to an enterprise directory with 100,000+ users and centralized role and access management? Hellmar Becker and Frank Albers present ING's approach to aligning Hadoop authentication and role management with ING’s policies and architecture, discuss challenges they met on the way, and outline the solutions they found.
11:15–11:55 Friday, 3/06/2016
Thomas Wiecki (Quantopian)
Thomas Wiecki explores the prevalence of backtest overfitting and debunks several common myths in quantitative finance based on empirical findings. Thomas demonstrates how he trained a machine-learning classifier on Quantopian's huge and unique dataset of over 800,000 trading algorithms to predict if an algorithm is overfit and how its future performance will likely unfold.
16:35–17:15 Thursday, 2/06/2016
Ben Sharma (Zaloni)
Risk data aggregation and risk reporting (RDARR) is critical to compliance in financial services. Big data expert Ben Sharma explores multiple use cases to demonstrate how organizations in the financial services industry are building big data lakes that deliver the necessary components for risk data aggregation and risk reporting.
14:55–15:35 Thursday, 2/06/2016
Deenar Toraskar (Think Reactive)
Value at risk (VaR) is a widely used risk measure. VaR is not simply additive, which provides unique challenges to report VaR at any aggregate level, as traditional database aggregation functions don't work. Deenar Toraskar explains how the Hive complex data types and user-defined functions can be used very effectively to provide simple, fast, and flexible VaR aggregation.
14:05–14:45 Thursday, 2/06/2016
Fergal Toomey (Corvil), Pierre Lacave (Corvil Ltd.)
Fergal Toomey and Pierre Lacave demonstrate how to effectively use Spark and Hadoop to reliably analyze data in high-speed trading environments across multiple machines in real time.
12:05–12:45 Thursday, 2/06/2016
Gwen Shapira (Confluent), Jeff Holoman (Cloudera)
Kafka provides the low latency, high throughput, high availability, and scale that financial services firms require. But can it also provide complete reliability? Gwen Shapira and Jeff Holoman explain how developers and operation teams can work together to build a bulletproof data pipeline with Kafka and pinpoint all the places where data can be lost if you're not careful.