Post on 20-May-2015
description
Financial Services Data - Use It or Lose It!
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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
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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;
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Computer World!”
- Kraftwerk 1981
2
Financial Services & Typical “Intensive” Problems
WholesalePortfolio Construction
Retail
Demand Forecasting
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Price Forecasting
Risk Calculation
Compliance Surveillance
Batch Processing
Targeted Marketing
Compliance Surveillance
Batch Processing
3
Data & Compute Intensity in FS
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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
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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?
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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?
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• 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 ?
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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
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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
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• 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!
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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
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& 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
� John.Avery@SunGard.com
� On Twitter
� @john_avery
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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.
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