Dissecting Big Data and The Cloud in Capital Markets

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Dissecting Big Data and The Cloud in

Capital Markets

December 2014

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Agenda

• Some Background and Drivers

• Big Data Baby Steps

• The Real Capital Markets Cloud

• Recommendations

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Agenda

• Some Background and Drivers

• Big Data Baby Steps

• The Real Capital Markets Cloud

• Recommendations

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FS Technology No Longer A Leader

Navigational DBs: • Mainframe • COBOL • GML • DBs - IBM IMS 1970 1980 1990 2000 2010 Relational DBs: • Mainframe RDBMS • DBs - Oracle, IBM SQL/DS • AutEx • SWIFT SQL: • SQL ANSI Standard • DBs - dBASE, IBM DB2, Sybase • Desktop PC • DOC, XLS • SGML • ISDA • Bloomberg Terminal The Server: • Unix, Windows • DBs - Microsoft SQL Server, MySQL • Web Servers - Apache, Microsoft IIS • HTML, PDF, XML • FIX Protocol • Google NoSQL: • DBs – Apache Cassandra, MarkLogic, MongoDB, Redis • Apache Hadoop • AWS • Microsoft SharePoint • SmartLogic • FIXML • FpML • G-20 Agreement • QuickFIX • RDF • RegNMS • SalesForce.com • SwapsWire Semantic Web: • Apache Hadoop HDFS • Dodd-Frank Act • FIBO • Knowledge Graph • HTML5 • SPARQL • Thomson Reuters Eikon • Triplestores

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Data Everywhere

API, .DOC, FIX, FIXML, FpML, HTML, HTML5, JDBC, JMS, ODBC, .PDF, SOAP, Text, XBRL, .XLS, .XML …

Semantic Search

Trading

AML FATCA KYC

COB Risk Management Compliance Surveillance Regulatory Compliance Investment Management Research Management Reference Voice Recordings Chat/ IM Logs Compliance Earnings/ .DOCs HTML RDBMS NoSQL Share Point ERP HR Text Web Servers Enterprise E-Mail Platforms Financial Analysis eDiscovery Queries & Search Data Management

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Global Search for Alpha

Not too or not at all challenging 23% Somewhat challenging 37% Very challenging 33% Extremely challenging 7%

Q. In current market conditions, how challenging is alpha generation? (n=30)

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Electronic Trading

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Adoption of Electronic Trading, 2001 to e2014

Equities Futures Options FX

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Algorithmic Trading

0%

10%

20%

30%

40%

50%

60%

70%

2004

2005

2006

2007

2008

2009

2010

2011

2012

e2013

e2014

Projected Adoption of Algorithmic Trading by Asset Class, 2004 to e1014

Equities

Futures

FX

Options

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Algorithmic Trading

25% 28% 33% 38% 45% 52% 60% 63% 64% 65% 65% 3% 7% 12% 18% 22% 25% 28% 32% 36% 39% 42% 1% 1% 2% 4% 9% 13% 16% 20% 24% 28% 31% 0% 10% 20% 30% 40% 50% 60% 70% 2004 2005 2006 2007 2008 2009 2010 2011 2012 e2013 e2014

Projected Global Adoption of Algorithmic Trading by Region, 2004 to e2014

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Agenda

• Some Background and Drivers

• Big Data Baby Steps

• The Real Capital Markets Cloud

• Recommendations

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Big Data Components

Accuracy of data/data quality Scalability Business sponsorship of concept High data availability Centralization Expertise of data scientist Technology Timeliness of response for queries

Budget

Governance framework

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(Big) Baby Steps

• Cool Tools – Commoditized and Open-Source

Software

• “Big Data” tools for not so big FS problems

• Mostly tactical

implementations in

specific areas

• Majority of firms

lack enterprise-wide

big data strategy… and

those that do are often

stuck in governance.

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Big Data Survey Firm Types

Bank 32% Broker-dealer 18% Asset or wealth manager 27% Third-party administrator 14% Hedge fund 9%

Respondent Firm Type (N=22)

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Data Management Priority

High priority 55% Medium priority 36% Low priority 9%

Internal Priority Level for Data Management and Analytics (N=22)

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The Current Data IT Stack

32% 45% 45% 45% 50% 55% 59% 59% 77% NoSQL databases Hadoop Open-source databases Time series database CEP technology Data quality monitoring tools Standard data integration tools Business intelligence tools Standard relational databases

Q. What kinds of data management technology does your organization have in place?

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Who’s invested in Big Data?

Yes 50% No 41% Are actively considering 9%

Q. Does your organization currently have a big data initiative or strategy in place?

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Big Data is in the house… why?

10 9 9 8 7 7 7 7 3 2 1 2 3 3 4 4 4 3 6 7 1 2 1 2 1 2 2 3 3 3 1 1 1 1 Faster data analytics

Better insight System scalability Granular drilldowns Proactive compliance Better predictive ability Faster time to market Metadata support Documentation tagging E-discovery

Big Data Benefits of Importance (n=13)

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Big Data doubters… why not?

1 1 2 2 3 5

Don't know enough about it Budget allocated elsewhere Don't understand the benefits No need to use it Perceived to be too expensive

Lack of business

support/executive commitment

Q. Why is your organization not considering a big data technology approach?

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Who owns Big Data?

Data management 37% IT Operations 18% Trading 9% Research and development 9%

Respondent Job Function (N=22)

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Who knows Big Data?

Data Scientists

Statistical skills Programming skills Knowledge of financial markets

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Who’s hiring (Big) Data Scientists?

No, no plans to employ 45% Yes, currently employ 41%

No, but plan to employ in the next

24 months 9%

What is a data scientist?

5%

Q. Do you employ a data scientist? (N=22)

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14% 23% 32% 32% 45% 45% 50% 55% 59% 64% 73% Other Voice and messaging records Legal entity data Records retention Instrument data Performance data Corporate actions data Positional or transactional data Regulatory compliance data Risk data Market data

Q. Which data sets do you believe are best suited to a big data approach at your organization?

(N=22)

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Big Data Usage

1 3 4 4 5 5 5 5 6 6 6 7 8 Log data Reference or market data

Positional or transactional …

Strategy development and …

Marketing Regulatory reporting support Market surveillance or fraud Cost reduction Risk management or modeling Client management Revenue optimization Quantitative research Analytics for trading

Q. In which areas is your firm using or planning to use big data technology?

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Big Data Challenges

2 2

4

6

9

Technical problems Other Inadequate

business capabilities of

technology

Data privacy issues Inadequate

technical knowledge Challenges Faced During Big Data Projects

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What’s next for Big Data?

2 4 4 5 7 7 7 8 8 8 9 9 9 Other Development and testing Revenue optimization Marketing Market surveillance Reference or market data Cost reduction Analytics for trading Risk management or modeling Quantitative research Positional or transactional data Client management Regulatory reporting support

Q. In which of the following areas do you think your organization would consider using big data technology?

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Agenda

• Some Background and Drivers

• Big Data Baby Steps

• The Real Capital Markets Cloud

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“The Cloud”

Generally Smarter

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“The Cloud”

No-Brainer???

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Capital Markets Cloud Drivers

• Commoditization facilitating going off-premises.

• Exchanges are exiting infrastructure provision.

(NYSE Technologies valued at $0.)

• Global Search for Alpha

• Colocation reqs. making raw connectivity alone

less relevant.

• 3

rd

_{-Party Datacenter Proliferation}

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Capital Markets Cloud Drivers

Exchange Infrastructure Business Model

Old

Transitional

New

Datacenters

Own/Build

Lease/Build

Lease Rackspace

in 3

rd

_Party

Datacenters

Connectivity

Own Network

3

rd

_{Party Networks 3}

rd

_{Party Networks}

and POPs

Expenses

High

Medium

Low

Revenue Goals

High

Low

Low to None

IT Offerings

Many

Medium

Medium to None

Market Data Fees High

Medium

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Cloud Survey Firm Types

Broker-dealer/investment bank Asset manager 10% Exchange/ATS 5% Breakdown of Respondents (n=21)

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Who’s invested in The Cloud?

Yes 52% No

48%

Q. Does your firm currently have cloud initiatives in place? (n=21)

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Where’s The (Capital Markets) Cloud?

3

rd

_{-Party Datacenters}

Public cloud 37% Private cloud Combination of both 36%

Type of Cloud Services In Use (n=11)

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What’s The Capital Markets (3

rd

_-party

datacenter hosted) Private Cloud?

• Single-tenant architecture + Encryption + Shared

infrastructure hosted in independent

market/connectivity-agnostic 3rd-party datacenters

• Datacenter Best Practices

• Reasonably Economical Cross-Connects

• POPs

• Connectivity Providers

• Exchanges

• The Cloud (AWS, Azure, Google)

• Hosted & ASP Software

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House in The Cloud… why?

2 2 4 3 4 3 3 5 4 4 3 7 5 3 2 2 2 Reducing TCO System scalability Disaster-recovery and … Faster time to market High availability Moving from capex to opex

Q. Rank the importance of benefits that you may get from the cloud (n=11)

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House in The Cloud… why?

10% 10% 10% 19% 19% 24% 24% 29% 29% 29% 48% Other Need for a more structured approach to …

Strategic consolidation post-merger Coping with a greater variety of … Risk management function support

Regulatory initiatives Migration from legacy systems Client data focused initiatives Strategic data quality related programs Desire to rationalize data feeds/vendor …

Cost reduction

Q. Which factors have or are likely to influence your use of cloud technology for data?

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Not so fast… why not?

50% 40% 30% 30% 10% Lack of control Performance issues Reliability and availability Regulatory restrictions on

data storage Other

Concerns about Cloud Initiatives (n=10)

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Capital Markets Cloud Caution Ahead

• Data Sovereignty (Encryption is your friend!)

• Sovereign Jurisdictional Laws

• Civil law is way behind technology saddling off-premises

infrastructure with deficient legal protection.

• Servers and services located off-premises (i.e., no longer

installed) may cease to be real property… and their data

subject to access without notice.

• Much of the cloud’s economy of scale is predicated on

massive commoditization… often at the expense of

tailoring of infrastructure and contractual terms.

• “It seemed like a good idea at the time.”

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Agenda

• Some Background and Drivers

• Big Data Baby Steps

• The Real Capital Markets Cloud

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Recommendations

• Jump into the Big Data pool tactically and get your

feet wet… governance and strategic implementation

will follow.

• Adopt Big Data software tools not just for large

datasets.

• 3

rd

_{-party Datacenters are the real Cloud… choose}

based on strategic relationships and connectivity.

• Secure your data no matter where it is.

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