Presented By O'Reilly and Cloudera
Make Data Work
Sept 29–Oct 1, 2015 • New York, NY

Financial Services conference sessions

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Data, Money, and Regulation.

4:35pm–5:15pm Thursday, 10/01/2015
Steven Totman (Cloudera), Mark Donsky (Okera), Kristi Cunningham (Capital One), Ben Harden (CapTech Consulting)
Moderator: Steve Totman, Big Data Evangelist at Cloudera Panelist: Kristi Cunningham, VP Enterprise Data Management at Capital One Panelist: Susan Meyer, Business Leader - Fraud Management Solutions at MasterCard Worldwide Panelist: Ben Harden, Managing Director at Captech Panelist: Mark Donsky, Navigator Product Manager at Cloudera
2:55pm–3:35pm Wednesday, 09/30/2015
Arvind Prabhakar (StreamSets)
Modern data infrastructures operate on vast volumes of continuously produced data generated by independent channels. Enterprises such as consumer banks that have many such channels are starting to implement a single view of customers that can power all customer touchpoints. In this session we present an architectural approach for implementing such a solution using a customer event hub.
9:00am–12:30pm Tuesday, 09/29/2015
Sean Owen (Cloudera), Juliet Hougland (Cloudera), Sandy Ryza (Clover Health)
In this tutorial, attendees will get a taste of how large-scale data science techniques and technologies developed for the consumer internet can be applied in the world of finance. We will guide an exploration of the relationship between the traffic on Wikipedia pages to the movement of stock prices.
5:25pm–6:05pm Wednesday, 09/30/2015
Sandy Ryza (Clover Health)
How much can you expect to lose? The financial statistic Value at Risk seeks to answer this question, but is computationally intensive to estimate. At Cloudera, we’ve assisted several organizations in using Spark to compute VaR and other financial statistics. The talk, which walks through a basic VaR calculation, aims to give a feel for what it is like to approach financial modeling with Spark.
5:25pm–6:05pm Wednesday, 09/30/2015
Steven Totman (Cloudera), Sam Heywood (Cloudera), Nick Curcuru (Mastercard)
Technology offers amazing big data use cases, but according to Gartner it's important to avoid "crossing the creepy line." Governance and security experts from Cloudera and MasterCard discuss the legal and ethical usage of big data. Ethical behavior drives trust - they are inseparably linked. For customers to trust and continue to do business with us requires an ethical data usage framework.
11:20am–12:00pm Wednesday, 09/30/2015
Billy Newport (Goldman Sachs)
The combination of data, technology, and analytics creates previously impossible business intelligence opportunities. How well companies can capture and manage their data so that it can be easily and consistently queried will be a key differentiator in deriving commercial value from data. Learn how Goldman is developing an enterprise platform to unify and manage data across the firm.
3:50pm–4:10pm Tuesday, 09/29/2015
Kristi Marotta (Allstate)
Allstate data evangelists set out on a mission to create an 'encyclopedic' data resource at one of the world's biggest insurance companies, and a single centralized place to go where they could quickly and easily answer data questions. Hear about how their usage of Tableau on top of Cloudera Hadoop has helped them build a culture of analytics for professionals of all skill levels.
9:00am–12:30pm Tuesday, 09/29/2015
Gwen Shapira (Confluent), Jonathan Seidman (Cloudera), Ted Malaska (Capital One), Mark Grover (Lyft)
Looking for a deeper understanding of how to architect real-time data processing solutions? Then this tutorial is for you. In Part 1 of "Architecture Day," We will build a fraud-detection system, and use it as an example to discuss considerations for building such a system; how you’d integrate various technologies; and why those choices make sense for the use case in question.
2:05pm–2:45pm Wednesday, 09/30/2015
Jaipaul Agonus (FINRA)
Slides:   1-PDF    external link
This presentation is a real-world case study about moving a large portfolio of batch analytical programs that process 30 billion or more transactions every day, from a proprietary MPP database appliance architecture to the Hadoop ecosystem in the cloud, leveraging Hive, Amazon EMR, and S3.
2:55pm–3:35pm Thursday, 10/01/2015
Karen Rubin (Quantopian)
Slides:   external link,   2-PDF 
Karen Rubin has spent the last nine months exploring “What would happen if you invested in women CEOs?" In doing so, she has developed an investment algorithm that invests in the women-led companies of the Fortune 1000. Based on a simulation run from 2002-2014, this algorithm would have outperformed the S&P 500 by more than 200%. In this talk she will share her algorithm and results.
2:05pm–2:45pm Thursday, 10/01/2015
Margit Zwemer (LiquidLandscape)
Linked Immersive Visualization Environments (LIVE) is a framework that my startup, LiquidLandscape, has developed for combining multiple, high-volume data visualizations (d3, WebGL, WebVR) to provide comprehensive situational awareness for financial markets. We will discuss architecture and design challenges of visualizing real-time data at speed and scale, with lots of visual examples.
1:15pm–1:55pm Thursday, 10/01/2015
Sam Heywood (Cloudera), Nick Curcuru (Mastercard), Ritu Kama (Intel)
Slides:   1-PPTX 
Hadoop is widely used thanks to its ability to handle volume, velocity, and variety of data. However, this flexibility and scale presents challenges for securing and governing this data. To avoid your company making the front pages over a data breach, experts from MasterCard, Intel, and Cloudera share the Hadoop Security Maturity Model phase 0-4 and steps to get your cluster ready for a PCI audit.
4:35pm–5:15pm Thursday, 10/01/2015
Vasant Dhar (NYU)
Financial markets emanate massive amounts of data from which machines can, in principle, learn to invest with minimal initial guidance from humans. I contrast human and machine strengths and weaknesses in making investment decisions.
11:20am–12:00pm Wednesday, 09/30/2015
Richard Brath (Uncharted Software), Rob Harper (Uncharted)
Slides:   1-PDF 
Direct visual exploratory analysis of big data yields insights that are otherwise overlooked. By plotting all the data, patterns that can be obscured by traditional visualization methods are preserved. This presentation highlights the power of visualizing whole data sets through examining a market order book and identifying pricing strategies.
11:20am–12:00pm Wednesday, 09/30/2015
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? In this session, we will go over everything that happens to a message - from producer to consumer, and pinpoint all the places where data can be lost - if you are not careful.