Dissecting Big Data and The Cloud in Capital Markets

Transcription

1 Dissecting Big Data and The Cloud in Capital Markets December 2014 David B. Weiss Senior Analyst Page 1 Page 1

2 Agenda Some Background and Drivers Big Data Baby Steps The Real Capital Markets Cloud Recommendations Page 2

3 Agenda Some Background and Drivers Big Data Baby Steps The Real Capital Markets Cloud Recommendations Page 3

4 FS Technology No Longer A Leader Navigational DBs: Mainframe COBOL GML DBs - IBM IMS 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 Page 4

5 Data Everywhere Queries & Search Trading Financial Analysis Investment Management Research Management Risk Management Compliance ediscovery Semantic Search COB Surveillance Regulatory Compliance AML FATCA KYC API,.DOC, FIX, FIXML, FpML, HTML, HTML5, JDBC, JMS, ODBC,.PDF, SOAP, Text, XBRL,.XLS,.XML Data Management HTML.XLS.DOCs Text XML Research Reports Platforms Earnings/ Financial Data Pricing Web Servers RDBMS Market Data Enterprise Risk NoSQL Order Book Portfolios/ Positions Share Point Transactions Chat/ IM Logs Voice Recordings Security Master ERP HR Customer Master Compliance Reference Data LinkedIn Internet Twitter Page 5

6 Global Search for Alpha Q. In current market conditions, how challenging is alpha generation? (n=30) Extremely challenging 7% Not too or not at all challenging 23% Very challenging 33% Somewhat challenging 37% Page 6

7 e2014 Electronic Trading Adoption of Electronic Trading, 2001 to e % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Equities Futures Options FX Fixed Income Page 7

8 e2013 e2014 Algorithmic Trading Projected Adoption of Algorithmic Trading by Asset Class, 2004 to e % 60% 50% 40% 30% 20% 10% Equities Futures FX Options Fixed Income 0% Page 8

9 Algorithmic Trading 70% 60% 50% 40% 30% 20% 10% 0% 25% Projected Global Adoption of Algorithmic Trading by Region, 2004 to e % 33% 12% 7% 3% 1% 1% 2% 38% 18% 4% 45% 52% 22% 25% 9% 13% 60% 63% 64% 65% 65% 28% 32% 36% 16% 20% 24% 39% 42% 28% 31% e2013 e2014 U.S. Europe Asia Page 9

10 Agenda Some Background and Drivers Big Data Baby Steps The Real Capital Markets Cloud Recommendations Page 10

11 Big Data Components Technology Scalability Centralization Analytics Budget Accuracy of data/data quality Business sponsorship of concept Expertise of data scientist Governance framework High data availability Timeliness of response for queries Page 11

12 (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. Page 12

13 Big Data Survey Firm Types Respondent Firm Type (N=22) Third-party administrator 14% Hedge fund 9% Bank 32% Asset or wealth manager 27% Broker-dealer 18% Page 13

14 Data Management Priority Internal Priority Level for Data Management and Analytics (N=22) Low priority 9% Medium priority 36% High priority 55% Page 14

15 The Current Data IT Stack Q. What kinds of data management technology does your organization have in place? (N=22) Standard relational databases Business intelligence tools Standard data integration tools Data quality monitoring tools CEP technology Time series database Open-source databases Hadoop NoSQL databases Big data analytical appliance HDFS Proprietary database software 5% 9% 27% 32% 59% 59% 55% 50% 45% 45% 45% 77% Page 15

16 Who s invested in Big Data? Q. Does your organization currently have a big data initiative or strategy in place? (N=22) Are actively considering 9% No 41% Yes 50% Page 16

17 Big Data is in the house why? Big Data Benefits of Importance (n=13) Faster data analytics Better insight System scalability Granular drilldowns Proactive compliance Better predictive ability Faster time to market Metadata support Documentation tagging E-discovery Unstructured data Very important Somewhat important Not important Don't know Page 17

18 Big Data doubters why not? Q. Why is your organization not considering a big data technology approach? (n=9) Lack of business support/executive commitment 5 Perceived to be too expensive 3 No need to use it 2 Don't understand the benefits 2 Budget allocated elsewhere 1 Don't know enough about it 1 Page 18

19 Who owns Big Data? Respondent Job Function (N=22) Research and development 9% Trading 9% Data management 37% Operations 18% IT 27% Page 19

20 Who knows Big Data? Data Scientists Knowledge of financial markets Programming skills Statistical skills Page 20

21 Who s hiring (Big) Data Scientists? No, but plan to employ in the next 24 months 9% Q. Do you employ a data scientist? (N=22) What is a data scientist? 5% No, no plans to employ 45% Yes, currently employ 41% Page 21

22 Big Data Suitability Q. Which data sets do you believe are best suited to a big data approach at your organization? (N=22) Market data Risk data Regulatory compliance data Positional or transactional data Corporate actions data Performance data Instrument data Records retention Legal entity data Voice and messaging records Other 14% 23% 32% 32% 64% 59% 55% 50% 45% 45% 73% Page 22

23 Big Data Usage Q. In which areas is your firm using or planning to use big data technology? (n=13) Analytics for trading Quantitative research Revenue optimization Client management Risk management or modeling Cost reduction Market surveillance or fraud Regulatory reporting support Marketing Strategy development and Positional or transactional Reference or market data Log data Product design and testing Page 23

24 Big Data Challenges Challenges Faced During Big Data Projects (n=13) Technical problems Other Inadequate business capabilities of technology Data privacy issues Inadequate technical knowledge Page 24

25 What s next for Big Data? Q. In which of the following areas do you think your organization would consider using big data technology? (n=15) Regulatory reporting support Client management Positional or transactional data Quantitative research Risk management or modeling Analytics for trading Cost reduction Reference or market data Market surveillance Marketing Revenue optimization Development and testing Other Product design and testing Page 25

26 Agenda Some Background and Drivers Big Data Baby Steps The Real Capital Markets Cloud Recommendations Page 26

27 The Cloud Generally Smarter Page 27

28 The Cloud No-Brainer??? Page 28

29 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 OTCD Greenfield Projects ios icloud Everybody is a cloud expert! Page 29

30 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 Medium Example Old NYSE Nasdaq BATS, ICE Page 30

31 Cloud Survey Firm Types Breakdown of Respondents (n=21) Asset manager 10% Exchange/ATS 5% Brokerdealer/investment bank 85% Page 31

32 Who s invested in The Cloud? Q. Does your firm currently have cloud initiatives in place? (n=21) No 48% Yes 52% Page 32

33 Where s The (Capital Markets) Cloud? 3 rd -Party Datacenters Type of Cloud Services In Use (n=11) Combination of both 36% Public cloud 37% Private cloud 27% Page 33

34 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 Services Hotels Campuses Communities Page 34

35 House in The Cloud why? Q. Rank the importance of benefits that you may get from the cloud (n=11) Moving from capex to opex High availability Faster time to market Disasterrecovery and System scalability Reducing TCO 3 7 Not important Important Very important Page 35

36 House in The Cloud why? Q. Which factors have or are likely to influence your use of cloud technology for data? (n=21) Cost reduction Desire to rationalize data feeds/vendor Strategic data quality related programs Client data focused initiatives Migration from legacy systems Regulatory initiatives Risk management function support Coping with a greater variety of Strategic consolidation post-merger Need for a more structured approach to Other 10% 10% 10% 29% 29% 29% 24% 24% 19% 19% 48% Page 36

37 Not so fast why not? Concerns about Cloud Initiatives (n=10) Other 10% Regulatory restrictions on data storage 30% Reliability and availability 30% Performance issues 40% Lack of control 50% Concerns over security 90% Page 37

38 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. Page 38

39 Agenda Some Background and Drivers Big Data Baby Steps The Real Capital Markets Cloud Recommendations Page 39

40 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. Never forget the law of unintended consequences. Page 40

41 Aite Group: Partner, Advisor, Catalyst Aite (pronounced eye-tay ) Group is an independent research and advisory firm focused on business, technology, and regulatory issues and their impact on the financial services industry. David B. Weiss Senior Analyst dweiss@aitegroup.com Virginie O Shea Senior Analyst voshea@aitegroup.com Page 41