Financial Services Data - Use It or Lose It

Financial Services Data - Use It or Lose It!

www.sungard.com/globalservicesProprietary and Confidential. Not to be distributed or reproduced without permission

Financial Services Data - Use It or Lose It!

CUNY High Performance Computing Center (HPCC)

Workshop on Data, Graphs and Combinatorics in Bio-Informatics,

Finance, Linguistics and National Security

July 26-27, 2011

Why Am I Here?

SunGard #2 in 2010 FINTECH 100

Professional

14+ years of development on Wall Street

Personal


Products & Services across the FS spectrum

Exploring impacts of Big Data approach for past 2+ years

Intersection of Global Services Advanced Technology & Information Management Practices

Development + Databases + Data Warehousing + Trading + Risk Management

1

Why Are We All Here?

“Interpol and Deutsche Bank, FBI and Scotland Yard;

Business, Numbers, Money, People;

Crime, Travel, Communication, Entertainment;


Computer World!”

- Kraftwerk 1981

2

Financial Services & Typical “Intensive” Problems

WholesalePortfolio Construction

Retail

Demand Forecasting


Price Forecasting

Risk Calculation

Compliance Surveillance

Batch Processing

Targeted Marketing

Compliance Surveillance

Batch Processing

3

Data & Compute Intensity in FS


What creates this intensity?

•Algorithmic Complexity•Time Series

•Calculus

•Graphs

•Semantic Inefficiency•Inefficient physical data structures

•Semantic starvation

Two flavors of intensity overload

•Unacceptable elapsed run time

•Herculean efforts to pare down size of input data set

Two flavors of intensity management

•Inter-machine parallelism & distribution•Grids

•BigData

•Sharding•Memory vs. Disk Arbitrage

•Intra-machine hardware acceleration•GPUs

•FPGAs

•Other (Cray, etc…)

4

Implementation Constraints in FS

Legacy Burden

• Architectures, languages, tools, systems, skills

RDBMS Centricity

• Jailed semantics, rigidity and specificity


Data Silos

• You don’t own that data, I do

Cost of Change

• Evolution is the only answer

5

Are We Close to Breaking Free of these Constraints?

Has a New Era Begun?

• Has financial crisis created a new era of information transparency that requires a new approach to comply?

• Are the economics of “cloud computing” disruptive enough to truly commoditize computing power?

• Will relational databases one day be seen as the “stone age” of information management?


Or Are We Plagued By the Current State?

• The cost of maintaining simple data relationships, with staunch determinism and specificity

• The effort required to design, store and consume richer information semantics

• The disconnect between computing power and information abstraction

6

The BigData Thesis in FS

• New technology not burdened by the past, with non-linear economic advantages at scaleWhy the allure?

• The markets need another source of wealth creationWhy the marketing

hype?

• By disrupting the pastHow is innovation

marketed?


• By disrupting the past

marketed?

• Replacing 35+ years of relational R&D

• Solution for semantically limited representation of data in current stateWhat is hype?

• Designed for more rapid scalability with “cloud” economics

• Reducing the time it takes to process data to both formulate questions and produce answers at scale

What is reality?

• Can BigData solve existing challenges in FS that couldn’t be solved before?

• Can BigData create new solutions in FS, unachievable with earlier incarnations of technolgy?

Theses

7

Is BigData Enough or do we need Smart Data?

Smart Data (?)

• Better relationship persistence ?


BigData (+)

• Cheaper to store

• Cheaper to index

• Cheaper to access in bulk

BigData (-)

• Doesn’t address semantic capability shortfall

• Legacy migration cost

• Re-training effort

• Linear return on investment

persistence ?

• Better relationship utility & consumption?

• Deeper economic impacts?

• Non-linear return on investment?

8

The Opportunity in FS - A Second Look

Cloud Economics

• Are Here

Consumer Data Success Stories

Competition

• Won’t Wait


Stories

• Are Noted

Social Media

• May Have Some Signal in all that Noise

Digital Exhaust

• May Also Have Some Signal

9

Financial Services Data - Use it or Lose it!

Solving linear problems yields linear returns

• Solving for scale doesn’t automatically solve for semantics

• Moore’s Law millionaires are in their 50s and 60s now…do you really want to bet on a 30 year-old strategy?

Go beyond the RDBMS, Go beyond BigData


• It’s the richness and practical utility of relationships that create non-linear economic growth and technology returns

• Think Semantics, Think Relationships, Think Graphs

$$$

• A new era of transparency is creating a competitive new era of monetized “digital exhaust”

• Farm relationships in the data you own or data you control

• Conquer the legacy constraints

10

Maximize Your Return on Relationships!

www.sungard.com/globalservicesProprietary and Confidential. Not to be distributed or reproduced without permission 11

Image Courtesy of @ValdisKrebs

In Closing, The Challenge To Us All

Our Finite Collective Effort in Industry & Academia

• Devote effort to game changing solutions with “non-linear” returns

Our Solution Focus across Academia & Financial Services


& Financial Services

• Addressing adoption constraints should be just as important as the implementation itself

Financial Services Data

• Use it or lose it!

12

Contact Info

� John Avery, Partner, SunGard Global Services

� [email protected]

� On Twitter

� @john_avery


COPYRIGHT STATEMENT

Copyright © 2011 by SunGard Data Systems (or its subsidiaries, “SunGard”). All rights reserved. No parts of this document may be reproduced, transmitted or stored electronically without SunGard’s prior written permission.


Economy & Finance

Financial Services Data - Use It or Lose It