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1 Big Data Special Report trading technologies for financial-market professionals Sponsored by: June 2012

2 READY TO OUTPERFORM? HOW MUCH DO YOU EXPECT FROM MACHINE-READABLE NEWS? EXPECT MORE. WHETHER YOU ARE RUNNING BLACK BOX STRATEGIES THAT NEED SUB-MILLISECOND DATA OR MANAGING MEDIUM-TO-LONG-TERM INVESTMENTS, THOMSON REUTERS NEWS ANALYTICS ENABLES YOU TO OUTPERFORM THE COMPETITION. Be the first to react to market-moving economic or company events. Analyze thousands of news stories in real time to exploit market inefficiencies or manage event risk. Use statistical output from our leading-edge news analytics to power quant trading strategies across all frequencies and provide additional support to your decision makers. With unmatched depth, breadth and speed of news, razor-sharp news analytics and both hosted and on-site deployment options, we have everything you need to gain critical insight. And turn that insight into profit. THOMSON REUTERS NEWS ANALYTICS. DISCOVER. DIFFERENTIATE. DEPLOY. For more information: Thomson Reuters All rights reserved. Thomson Reuters and the Kinesis logo are trademarks of Thomson Reuters KNOWLEDGE TO ACT

3 Editor-in-Chief Victor Anderson tel: +44 (0) US Editor Anthony Malakian European Staff Writers James Rundle Steve Dew-Jones US Staff Writers Jake Thomases Tim Bourgaize Murray Head of Editorial Operations Elina Patler Contributors MaxBowie,Editor,InsideMarketData Michael Shashoua, Editor, Inside Reference Data Global Commercial Director Jo Garvey tel: +44 (0) US Commercial Manager Bene Archbold US Business Development Manager Melissa Jao EuropeanBusinessDevelopmentExecutiveMark Garvey Senior Marketing Manager Claire Light Design Lisa Ling Group Publishing Director Lee Hartt Chief Executive Tim Weller Managing Director John Barnes Incisive Media Head Office Broadwick Street London W1A2HG,UK Incisive Media US 55 Broad Street, 22nd Floor New York, NY tel: Incisive Media Asia 20th Floor, Admiralty Center, Tower 2 18 Harcourt Road Admiralty, Hong Kong, SAR China tel: fax: Subscription Sales HusseinShirwaTel:+44(0) Dominic Clifton Tel: +44 (0) Incisive Media Customer Services Haymarket House Haymarket London SW1Y 4RX Tel(UK): Tel (International): +44 (0) To receive Waters magazine every month you must subscribe to Buy-Side Technology online, Sell-Side Technology online or one of our multi-brand subscription options. For more information andsubscriptiondetails,visit Waters (ISSN ) is published monthly (12 times ayear)byincisivemedia.printedintheukbywyndeham Grange, Southwick, West Sussex. IncisiveMediaInvestmentsLimited,2012.Allrights reserved.nopartofthispublicationmaybereproduced, stored in or introduced into any retrieval system, or transmitted,inanyformorbyanymeans,electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of the copyright owners. The Industry s Gold Rush Startinginthemid-1800swiththeonsetoftheCaliforniaGoldRushandculminatinginthe20thcenturywiththeriseandproliferationoflargefactoriesand ever-more sophisticated techniques, mining has always been a money-maker. In the second decade of the 21st century, mining is once again big business data mining, that is. Forthoseorganizationsthatcanassimilate,interrogate,andderivemeaningfrom large, unstructured data sets, a fortune awaits. The judicious application of Big Data tools and technologies can go a long way toward addressing rapidly changing regulatoryrequirements,whiletraderscantapintothefullpotentialofsocialmediaandother sentimentdata,andriskmanagerscanmonitortheirfirm scounterparty,assetclass and country exposure on an intra-day basis. In the financial services industry, data is king, and taming Big Data, therefore, holds the key to firms controlling large portions oftheiroperatingenvironments. Butthequestionremains:Willthecapitalmarketsbeonthecuttingedgeofthis fast-emerging revolution? When it comes to cloud computing, the adoption of mobile technology, the harnessing of social media data, and the implementation of fieldprogrammable gate arrays (FPGAs) to super-charge compute-intensive processes, the capital markets has, by and large, lagged other industries in terms of adapting to change. Even in the realm of Big Data, the pharmaceutical industry and the military have been leading the charge. But successfully addressing the Big Data challenge offers game-changing potential, which,iffullyutilized,canbringaboutacompetitiveadvantage.recently,statestreet chiefscientistdavidsaul,spoketowaters about the exciting prospect of attacking Big Data using semantic database technology. He described the technology as cool and exciting stuff that has limitless potential in the financial services industry. With various technologies readily available, now is not the time to sit on the sidelines andwaitforthetechnologytomatureandtrickledown.nowisthetimetobeanearly adopter this is where research-and-development dollars should be going. This is the financial services industry s gold rush. Victor Anderson Editor-in-Chief Sell Side

4 Special Report Big Data NYSE Technologies Bows Hosted TAQ Analytics Lab NYSE Technologies, the data and trading technology arm of NYSE Euronext, will unveil a new service by the end of this month, dubbed Market Data Analytics Lab (MDAL), which will provide access to a central, managed database of its historical trade and quote (TAQ) data as well as a range of hosted analytics and tools for querying the data, enabling clients to back-test and implement trading strategies more easily and without the cost of acquiring and managing the entire TAQ database in-house. An extension of the exchange s Capital Markets Community cloud-based connectivity platform, MDAL allows customers to churn a lot of data within our environment before bringing it into theirs, especially when performing calculations on large volumes of market data within specific time periods, says Todd Watkins, product manager for US cash and data products at NYSE Technologies. And it benefits NYSE Technologies because we can deliver the dataset they need rather than the entire database. But we will continue delivering the data via FTP and summary files by , Watkins explains. Clients no longer have to buy and store these different datasets on their own site. Instead, they can download only the subset they need, says Brian Fuller, business development manager for global market data at NYSE Technologies, who adds that the service also includes a library of pre-built, commonly used functions, ranging from simple equations to more sophisticated, moving average-type calculations, which can be created in a simplified version of XML using drop-down menus accessed via a web interface. MDAL provides historical TAQ data for NYSE Euronext s US equity markets, and the exchange is looking to expand the service to cover other asset classes traded on markets within its parent group, such as derivatives and bonds, as well as data from other exchanges in the Northeast US, and other away markets, based on client demand, Fuller says. Users will be able to upload their own datasets into MDAL in spreadsheets or CSV files, and link them to the exchange s datasets to filter data according to their list of securities. Currently, users can buy TAQ content online as an FTP download, but cannot perform the calculations themselves in a managed fashion. With MDAL, not only will clients be able to perform calculations online using hosted datasets and hence not have to manage the capture and storage of that data onsite but will also be able to download the resulting calculations and underlying data in a variety of file formats. We think we will see a wide range of users, starting with smaller buy-side and quantitative shops, or mid- and back-office staff in larger firms who don t need access to the entire dataset, Fuller says. Pricing for MDAL will be a monthly per-user fee, the cost of which will vary according to the number of concurrent accesses, Watkins says. Twitter Hedge Fund Eyes Rebirth as DCM Capital Launched last year, Derwent Capital Markets Absolute Return fund fused capital markets and social media. It based its investment decisions on sentiment analysis from Twitter. After posting decent results for its first month of trading, the fund went quiet, eventually wrapping up its operations for a new direction. We couldn t have timed it worse to try and launch a new and innovative fund with the US losing its AAA rating and equity markets in freefall, says Paul Hawtin, CEO and founder of Derwent Capital Markets, which has now rebranded as DCM Capital. The fund held around $40 million in seed capital initially. Hedge funds need a certain amount of capital $100 million-plus before they can reach critical mass, he says. So, we made the decision to move out of the hedge fund industry, and open up our technology to the mass markets for an online trading platform that combines a research tool embedded within it. DCM plans to launch the platform, aimed at retail investors, in late summer. The technology powering it has been developed in-house using the financial resources from Derwent s ill-fated venture, building on a core of the sentiment analysis tools initially built for the hedge fund. We ve spent the last 18 months improving our technology initially, Paul Hawtin DCM Capital we were just focused on global sentiment, but now we re able to monitor it on any individual stock, currency or commodity, he says. DCM partnered with IG Group for the project. Client funds will be held by the latter. IG will then push the prices and tradable instruments to the platform. DCM says it has developed a nuanced approach to sentiment analysis the ability of funds to mine the sheer volume of data projected by social media, and generate alpha from that. It s quite a complex thing, but to simplify it, we listen to the Twitter firehose of data, says Hawtin. We do look at a few others as well, and we re looking at growing that, but predominantly it s Twitter. While the platform will be largely web-based, DCM plans to use HTML5 to roll out ios applications for Apple devices, with plans for a move to other devices. Other vendors have begun their own inroads into the space. Thomson Reuters recently released its own sentiment indicators, while other major vendors are looking at including sentiment in their market data feeds as an additional layer rather than an executable quality. What we ve found is that companies have fantastic tools, but you get information overload, Hawtin says. It s great, but what does that mean, and how can you use it to trade? With this, we ve spent a lot of time focusing on how to refine it for the end-user to understand and trade off it. 2 June 2012

5 News Traders Seek Profits From Unstructured Data Financial firms are increasing their focus on trying to derive value from analysis of unstructured data, from news to social media sources, though challenges to adoption remain around the trustworthiness and timeliness of nontraditional data sources. The first challenge lies in determining how much structure a dataset contains, and whether it can be analyzed by existing tools used for other datasets. There are at least three or four traditional areas of data that we re all used to dealing with structured data, such as ticks and quotes; and semi-structured data, such as news feeds, because they have some structure applied in terms of a headline and fields that one can filter, says Mark Fischer, vice president of product management at CQG. Then there s completely unstructured information, like Twitter or blogs, which have no structure associated with them but we are finding ways to mine that information, Fischer says. Structured data is just like market data. For example, non-farm payrolls we already have that in a structured format before it leaves the lockup, says Rich Brown, head of quantitative and event-driven solutions at Thomson Reuters. Unstructured data is where the opportunities are. With structured data, the opportunity is over in a thousandth of a second. But unstructured data applies for much longer time horizons, and offers the largest opportunities for people to differentiate their strategies. Sentiment analysis has been around for a long time, but it is slow and not what people use in terms of high-frequency trading. So what The Depository Trust & Clearing Corp. (DTCC) is expanding its India business center in Chennai into a technology infrastructure support and development office to help bolster its round-the-clock transactionprocessing, funds delivery, and data storage businesses. Over the next two years, the DTCC plans to expand its full-time staff in Chennai. In anticipation of this, the office has relocated to a larger site in Chennai where it can potentially acquire additional space as it expands. The DTCC began working with technology vendors in Chennai in 2004 and has people are trying to do now is figure out if they can get that any closer to real time, Fischer says. I m a skeptic about performing low- or semi-low-latency sentiment analysis for high-frequency trading. These tools won t be instantaneous reactions to the marketplace, but will Steve Ellenberg MDSL be longer-term and more thoughtful. Alexander Abramov, director and corporate relations committee chair for the Information Systems Audit and Control Association, says there s nothing new about the social media rumor mill and how it affects the decisionmaking process, adding that he hopes the industry will be the beneficiary of new technologies to enable firms to derive greater insight from analyzing unstructured data. Steve Ellenberg of MDSL, points to a key problem with basing decisions on data from social sources especially those on which algorithms base trading decisions at submillisecond speeds: News feeds are structured and have a certain authority. But there s a very low entry point to some forms of unstructured data and social media, he says. These challenges can make it hard for developers to fine-tune their systems to get the most out of the morsels of legitimate value DTCC Expands Chennai Office to Bolster Operations staffed an IT center and vendor oversight function there since Creating a stronger base in India helps us strengthen our presence and IT resources to support regional European and Asia- Pacific business initiatives. The geographic dispersal of our staff also allows us to sustain hidden in the universe of social media. In terms of social media, there are a lot of engineering problems: The signal-to-noise ratio is very low, and from an engineering perspective say, with Twitter we are working with snippets of text, so there is a lot of research to be done to overcome these limitations, says Ron Papka, global head of client analytics and market data distribution at Citi. However, he says that companies are increasingly using these channels to quickly disseminate important news or warnings to a mass audience. Over time, more companies are releasing information over Twitter rather than by traditional means. For example, when Total had a gas leak on its North Sea platform recently, they didn t issue a press release they tweeted it to get the information out there. So over time, this will become structured information for use in trading and risk management, Papka says. Still, pending the development of more sophisticated analysis tools, much of the universe of unstructured data from news to tweets will be used for risk management, to halt trading strategies in the event of unexpected news. If you look at the high-frequency space, there are uses but it is used more to stop trading, Brown says, though he adds that new tools that can use this information more proactively may not be far away. With news, you can get signals of the volume of news and can use that to build adaptive algorithms that react to news, rather than just stopping a strategy. our follow-the-sun workflow model for managing our IT, explains Robert Garrison, DTCC managing director and CIO. The decision to expand in Chennai also reflects the DTCC s push to sustain its operations and global data management businesses; provide technology research and development resources; ensure 24-hour business continuity and risk-mitigation support for the DTCC and other securities industry infrastructure organizations that contract with the DTCC for business continuity backup, and manage and support a broader range of IT vendors. June

6 Special Report Big Data IBM:Using Watson for Analysis Is Elementary IBM is working with investment banks to identify potential uses for its Watson supercomputer which appeared as a contestant on US game show Jeopardy! as a data and sentiment analytics engine. The vendor signed a deal to explore potential uses for Watson at Citi around its retail operations, and is now in discussions with a number of banks around using Watson to support wholesale and investment banking, says Likhit Wagle, global industry leader for banking and financial markets at IBM Global Business Services. Banks are facing exponential growth in the volumes of data they need to process and draw information from; internal data that is not necessarily accurate; and a lot of this data is not structured, Wagle says, adding that Watson can address these issues through its ability to process vast volumes of data. It s an adaptable system that learns through doing, so the more you give it, the better output you get and not just for structured data: Likhit Wagle IBM Watson can also draw insights from unstructured data, such as news items and blogs, to give analysts more views of data and sentiment, to enhance the quality of their recommendations, he adds. Banks could use Watson to obtain a better view of risk associated with specific clients, or to analyze large volumes of data to identify drivers of systemic risk. Another potential use especially in emerging markets where data is not readily available is around identifying suitable clients for firms wealth management and private banking services. Implementations of Watson will be on a bespoke basis. We see Watson working alongside humans to enhance the quality of the advice being provided, and it depends on parameters set by human beings, he says. However, he says firms could seek to use Watson as an additional input to the development and execution of sentiment-based trading strategies, and IBM would look at building a solution that automates some activities, if clients demand it. ISE, Hanweck Unveil Hosted Tick Database The International Securities Exchange (ISE) recently released its managed ISE Premium Hosted Database (ISE PhD) of options and equities data and options analytics, developed in partnership with options analytics provider Hanweck Associates, to support traders back-testing and analysis requirements. The ISE began piloting the service which has been in development for almost two years with hedge fund, market-maker and bank clients late last year, and is now making it publicly available. ISE PhD provides options tick data from the Options Price Reporting Authority, underlying Level-1 US equities data, and tick-by-tick implied volatilities and greeks calculated by Hanweck s Volera hardware-based options analytics engine, all dating back to The service includes ISE s proprietary open/close prices, which are already available as a separate historical product, and are primarily used by quantitative trading firms and proprietary trading groups to create analytical models and test trading strategies. PhD is certainly a quant offering, so we included that dataset because many quants who already use that data now will also want to use PhD, says Jeff Soule, head of market data at ISE. In addition, ISE is providing a database of corporate actions as part of PhD, which users can apply to a query, depending on their needs for example, to see when a Bear Stearns option became a JPMorgan option, to determine the pricing and implied volatility for the option at that specific point in time. The exchange is also talking to other, unnamed data providers and exchanges about including their content in the database, such as futures data, which Soule says he expects to add to PhD, and other content, to be driven by customer demand. The database supports back-testing, as well as pre- and post-trade analysis and transaction-cost analysis. ISE is also seeing interest from software vendors looking to incorporate the historical data to enhance their existing desktop applications, to support capabilities such as charting, trade idea generation and requests for time and sales data, Soule adds. At launch, the database will be updated daily after market close, though ISE plans to add real-time data integration by year-end. Once we add the real-time data, that will expand the prospect base significantly. For example, there are customers that will want to query the database intra-day for trading ideas, Soule says. Client systems can access PhD over the internet or by cross-connecting to ISE s servers within the exchange s primary datacenter at Equinix s NY4 facility in Secaucus, while traders can use pre-defined queries in PhD s web interface to quickly access the data they need for example, by simply entering the date range and symbols they want for back-testing or can use APIs to write their own queries for retrieving data. Soule says the managed database eliminates a key challenge for firms that may have considered building an infrastructure to store and manage this data themselves keeping up with the storage capacity and performance requirements of a growing dataset. We re talking about 200 terabytes of data, and there s a big cost factor for somebody to build this infrastructure out not just an upfront cost but significant ongoing cost, he says. ISE is offering a flexible pricing model for the database, to accommodate what it hopes will be a broad range of users. Clients can sign up for annual subscriptions to one-year chunks of content, allowing them to query or download data for the past 12, 24 or 36 months, dating back to Alternatively, users can pay for one-off queries for example, to run analysis on six months worth of data on a specific option that they are thinking of trading. 4 June 2012

7 Sponsor s Statement Unlocking the Value in Big Data With growing volumes, velocity and variety of data, it is no longerenoughforfinancialservicesfirmstolimittheiranalysis totraditionalmarketdata.tounlocktherealbenefitsof BigData,oneneedstoanalyzebroadersources,suchas unstructured data, and combine that information with existing signalstodifferentiateandenhancetrading,investment,and risk models. By Richard Brown Richard Brown Big Data has been a big IT story for many years now, but it is only recently that the concept has caught the attention of the financial services industry. While market data volumes have skyrocketed in recent years, some might say the data the financial services industry currently looks at is just the tip of the iceberg. The more complex and interesting aspect of Big Data in financial services lies in its variety, however. While some businesses deal with more isolated data types that do not necessarily span multiple disciplines, there is a significant range in the variety of data that can have an impact on a firm s risk measurements as well as its trading and investment performance. Unstructured data that may impact the market include such types as broker research, industry or economic reports, premium and internet news feeds, blogs, tweets, and audio and video programming. When analyzing this vast array of content, one needs to do it in a consistent manner and note key aspects including the source and motivations of the data who wrote it, who published it, for what audience and for what purpose, what it is about, and to what extent the people, companies, places, and so forth; the relevance of the data; the tone in which it is being talked about; how unique/repetitive/ popular the story is and any acceleration of trends; the psychological aspects being conveyed; the contextual backdrop; and the potential implications for certain trading/ investment decisions, to name just a few. Doing this on hundreds, thousands, or even millions of sources can easily overwhelm most systems and cause analysts to quickly become lost in the tsunami of data. Thomson Reuters News Analytics enables users to understand these key attributes among a wide variety and massive quantity BigDatahasbeenabigITstoryformanyyearsnow, butitisonlyrecentlythattheconcepthascaughtthe attention of the financial services industry. While market data volumes have skyrocketed in recent years, some might say the data the financial services industry currentlylooksatisjustthetipoftheiceberg.themore complex and interesting aspect of big data in financial services lies in its variety, however. of this unstructured content. It analyzes the data in a consistent, intelligible way to help users quickly unlock the potential in big unstructured data. Whether it be for systematic investment and trading strategies or to deepen a human s comprehension of data, Thomson Reuters News Analytics transforms this qualitative data into structured, quantitative forms to support a variety of analytic use-cases. Analyzing the Analysis One of the main goals of this process is to understand the implications the information has to various business processes. When the data has been transformed into a digestible format, it is ready for a broader, or more common, analytics process. To do that, it is necessary to understand the holistic information value chain. Combining the unstructured data analysis with more traditional sources such as pricing and reference data, parent/subsidiary information, supply-chain dynamics, people/titles/roles and products/brand databases, and doing so with an accurate point-in-time perspective is not easy, but it is required in order to support the downstream uses. The outcome of the analytics process will likely vary depending on who is ultimately consuming the information, but one of the important things to consider is that for the most part, the more attributes one has on the data, the more extensible it becomes. Thomson Reuters can provide the content, technology, and data management capabilities to properly analyze this wealth of unstructured data, enabling financial services firms to focus on the implications to their investment and trading strategies. Together, we can unlock the value of Big Data. Richard Brown is global head of quant and event-driven trading at Thomson Reuters. Visit for more information. June

8 Special Report Big Data BIG Challenges Regulatory and competitive pressures, liquidity fragmentation, and increasingly sophisticated trading strategieshaveledtoballooningdatavolumesthat traditional technologies are no longer equipped to handle. Known as Big Data, these massive data sets mustbeminedandanalyzedtoallowcapitalmarkets firmstostayabreastwiththeircompetitors.other industries have tackled Big Data, but financial services firms have been relatively late to the game, and are looking at new technologies to address the challenges. Q HowdoyoudefineBigData?Isthisanewphenomenon, or simply the next phase of enterprise data management with a catchy new name? Louis Lovas, director of solutions, OneMarketData: Big Datacanbedefinedbytwosalientpoints.Firstthereissupporting hardware. Bigger, faster, parallel hardware architectures havenotonlyenabledgreatercomputepowerbutalsomassive growthinstoragecapacities.thisclassicmoore slawmodelhas createdmaximalefficienciesinstorageperdollar.yethardware haslongbeensubjecttocommoditization.practicallyspeaking, itisanecessity,butsuchentropycreatesatrajectorythatmakes hardware srelevanceinthebigdataequationequaltothatof electricity. Theadvancementsinthisfoundationalcomputepower havepavedthewayforthetrueadvantage,derivingbusiness benefitthroughfocusedbigdatasolutions.theabilitytotell astorywiththedataiswhatelevatesabigdatasolutionover the underlying commodity hardware and storage architectures. Thestoryisgermanetoanindustrysuchasfinanceandcreates relevanceandmonetizesthedataforabusiness. 6 June 2012

9 Roundtable Peter Chirlian Chief Executive Officer Armanta, Inc. Tell: Web: Peter Chirlian, CEO, Armanta Inc: With competition, new regulationsandshortenedproductlifecycles,managersareforcedtorun adata-drivenbusinessinsteadofsimplyrelyingoninstinct.bigdata represents the convergence of trends in software and hardware, along withbillionsinventurecapital,whichhasledtotheemergenceof sgivenbusinesses theabilitytodeploymanyplatforms,eachsuitableforaclassofbusinessquestions.bigdataoffersthepromiseoffinallyenablingatruly data-andanalytics-drivenenterprise.insuchanenterprise,analytics isn tjustapointsolution.itisanend-to-endprocessinvolving everything from data gathering and cleansing to operationalizing businessprocesses acrosstheentirespectrumofnewbigdatatools andexistingdatainfrastructure. Dennis Smith, managing director, BNY Mellon: Big Data is datathathasanyofthefollowingcharacteristics:extremevolume, widevarietyofdataformats,highvelocityofrecordcreation,along with variable latencies and the complexity of individual data types within formats. Note that it is about more than just volume. There isabitofanevolution.existingtechnologieshaveallowedusto perform analysis of historical data. Big Data has the potential to not onlyprovideusbetterinsightintothecurrentsituation,butalso positionsustobemorepredictive. Andrew Poulter, head of risk analytics and methodology technology, RBS: I think there is certainly a cultural shift in terms of how people think about data, the importance of data retention, and how this can be fed back to improve business processes and ultimately margins. Technically, I see Big Data as an evolution as opposed to a new phenomenon or revolution. Marcus Kwan, vice president of product strategy and design, CQG: Big Data is the issue surrounding the massive increases in the number of data sources, volume of the data, the speed, and granularity of the data compounded over history. It has become more relevant in the past few years because of the number of exchanges going electronic, data collection methods, and the rate of technological advances. Big Data has become an issue for financial services due to pressures both regulatory and competitive and the need to identify opportunities for profit. Traders used to make trading decisions plotting charts with pen and paper. The technology and complexity is now light years from that time. IlyaGorelik,founderandCEO,Deltix:In the world of quantitativeresearchandtrading,wedefinebigdatabysize (interabytes),irregularity,andrateofnewdataarrival.itisone thingtodealwithvastquantitiesofdata itisquiteanotherto dealwithdataarrivingatratesmeasuredinmillionsofmessages persecond,especiallywhenthedataisdistributedirregularly overtime.marketdatavolumeshavemassivelyincreasedsince thefragmentationoftradingvenuespost-regulationnmsand themarketsinfinancialinstrumentsdirective(mifid),andthe increasing adoption of technology, allowing trading firms to increasethenumberofordersbeingsenttotradingvenues,sowe regard2007asthestartofbigdata. Rich Brown, global head, quantitative and event-driven solutions, Thomson Reuters: The volume, velocity and variety ofdatathatcharacterizebigdataisunprecedentedandwhile thepopularityof BigData astheindustry slatestcatchphrase continuestoreachnewheightseachday,itsimplicationscannot beignored.traditionalenterprisedatamanagementchallengesare dwarfedbythescaleandscopeofproblemsparticularlysurroundingthevarietyofdata.singleasset-classpricingdataandcross-asset depth-of-bookarenothingcomparedtochallengesinanalyzing unstructureddatasuchasnews,socialmedia,audioandvideo. BigDataoffersthepromiseoffinallyenablingatruly data-andanalytics-drivenenterprise.insuchan enterprise,analyticsisn tjustapointsolution.itisan end-to-end process involving everything from data gatheringandcleansingto operationalizing business processes across the entire spectrum of new Big Data tools and existing data infrastructure. Peter Chirlian, Armanta Q What are the specific business applications for Big Data across the buy side and sell side? Which business processes are most affected by the continued growth of data volumes, in addition to its complexity and variety of sources? Kwan: WelookatthemarketdatarealmofBigDatainthe frameworkoftwopillars:collectionanddistribution,andanalysis andexecution.thebusinessfirsthastobeclearonwhatitsstrategy isandthenchoosesolutionsforthesetwopillarsthatfit.ifyou choosecollectionanddistributionsystemsbefore,ormisaligned to, strategy, then it s simply an expensive science experiment. For example, within collection and distribution, firms must decide whethertogofordirectconnectionstoexchangesorsourcedata fromanaggregator.thedecidingissuesarearoundhowfastyou needthedataversusthecostofmaintainingadirectconnection, infrastructuretocollectandhousethedata,andsoforth. June

10 Special Report Big Data Marcus Kwan Vice President, Product Strategy and Design CQG Tel: Web: Inthepillarofanalyticsandexecution,weseeamoreimportant shift.firmsneedteamswhonotonlycanunderstandthenuancesof thedata,butcanformulatetherightbig-picturequestions.though these people may be rooted in mathematics and quantitative analysis, theoutputsneedtobeasystemthatprovidesdecisionmakers,who maynotbeastechnicallyversed,theabilitytoparticipateeffectively. Advanced visualization tools need to be able to mash up the multiple sourcesandthecomplexanalysis,andsumitupinsuchawaythata businesspersoncangrowitandmakeanintelligent,well-informed decision. Gorelik: We see three main applications.onthebuyside, research into alpha-generation is key. This involves access to granular (Level-1 or marketdepth)data,andthemeansto do quantitative research on this data. The second application is in modeling execution quality. We are often asked whyanalphamodelwith an apparently high Sharpe Ratioinback-testingdoes not perform well in live trading.thereare,ofcourse,severalreasonswhythismightbethe case.oneisorderexecution.thesmallertheprofitpotentialineach trade,themoresusceptiblethemodelistoexecutioncosts,especially slippage. The effective modeling of, and subsequent improvement in executioncosts,isachievedbysimulationusingmarket-depthdata. Thirdly, there are some firms that are using Big Data sets for doing original alpha-generation research. Twitter inevitably appears in such discussions,butlessprosaically,quantitativeresearchersaredoing serious research combining market data with machine-readable news, stock-loan and broader economic data. WelookatthemarketdatarealmofBigData intheframeworkoftwopillars:collection and distribution, and analysis and execution. Thebusinessfirsthastobeclearonwhatits strategy is and then choose solutions for these twopillarsthatfit.ifyouchoosecollectionand distribution systems before, or misaligned to, strategy, then it s simply an expensive science experiment. Marcus Kwan, CQG Chirlian: Riskmanagement,acriticalBigDataapplication,has historicallybeenlimitedbytechnologyanduse-cases.beforethe financialcrisis,staticriskreportsbasedonindependentsilosofdata weredeemedsufficient.thisisnownotthecase.inthepastfewyears, thevolumeofdataandthecomplexityofthecalculationssurrounding risk have grown significantly. Existing systems can no longer provide theneededresults bothforregulatoryandbusinessmanagement purposes.thedemandfordynamic,real-timeriskmeasurement oftenoutpacesexistingtechnologycapabilities taskslikeliquidity management require complex analysis across vast numbers of existing systems.thereisnowaseachangebothinthewayfinancialinstitutionslookatriskandthetechnologyplatformsavailabletoenablethis change. Poulter: ThespecificbusinessapplicationforwhichRBSisusingBig Dataistosupportinternalmodelmethod(IMM)defaultriskcapital calculations.thecalculationsrequirethousandsofmontecarlopaths ofmarketdata,representingthefutureevolutionofmarketdata. ApacheHadoopisusedtoholdtheevolvedmarketdataandlow-level resultsofinteresttothebusiness. Smith: Three come to mind. The first two are pretty common, while the third will probably become more common: performing batch operationsonamassiveamountofdata,oftenasafront-endtoexistingtools,suchasdatawarehouseappliances;analyzinglargeamounts of varied data to predict tendencies or future outcomes; and processing rapidlychangingdatasuchasthatnowassociatedwithcomplexevent processing (CEP) systems. Brown: Firms are challenged from the front office through the back office and IT departments with issues rangingfromdatabasemanagement, hardware and software upgrades, and network management, to data sourcing, permissioning and reporting requirements. Firms are turning to ThomsonReutersforourmanaged services offerings, looking to offload basic non-proprietary functions so they can focus on the higher valueaddedactivitieslikebettermanaging riskorfindingalphainthisvastarray of Big Data. Lovas: AndyPalmer,co-founderofVertica,oncewrote: BigDatais useless unless you architect your systems to support the questions that end-usersaregoingtoask. Businessisnotaimingforado-it-yourselfBigDatasolution,nor do they want to be pioneers with a vendor. Competitive pressures demand fit-for-purpose solutions. Quant researchers look to combine differingdatasetstounleashnewdiscoveryfaster.vendorsthatcan deliver an analytical and data management platform fit-for-purpose forriskmanagement,pricediscovery,andfraudmanagementwillhit the mark. Q Why has the financial services industry seen such significant growth in data volumes, and how has this growth impacted firms ability to efficiently manage large data volumes? Gorelik: Reg NMS in the US and Mifid in Europe resulted in fragmentation of trading, giving rise to more sources of market data. Cheaper hardware and software platforms have made 8 June 2012

11 Roundtable Ilya Gorelik Founder and CEO Deltix Inc. Tel: Web: And it comes back to being able to articulate company strategy clearly.firmscaneasilygetoverwhelmedbythetideofbigdata, butkeepingthestrategyclearlyintheforefrontwillenablefirmsto effectivelywrestlewiththechallenge. Poulter: I think financial services has always had the ability to generatefarmoredatathanwaspossibleandrealistictostore forexample, pricehistories,transaction-levelriskdata,andsoon.bigdatahas madeitpossibletostoremoreofwhatiscurrentlygenerated,toenable moredetaileddrilldown,trendanalysisovertime. high-frequency trading now normal practice for many trading firms, which increases the volume of market data. There are very few tools commercially available that are able to use these large data sets for meaningful analysis. Some firms have been struggling simply to store data let alone create value from it through analytical research. Brown: The explosion of market data volumes and venues, the increaseinthenumberandtypesoftradedinstruments,andthe interconnectedness of global markets are dramatically increasing the complexity and cost of capturing, normalizing, processing, storing and adjusting these vast volumesofdisparatedata. Legacy systems and networks arenolongeradequate.single databasesarenoteasilyable to handle the various types of data or scale large enough or fast enough to enable users to react quickly to this information. Financial services firms arestrugglingtokeepupwith the changes, especially in this economic environment whereit snolongereasy to just throw money at the problem buy more hardware, hire more people in order to make the problem go away. Itisonethingtodealwithvastquantitiesofdata it is quite another to deal with data arriving at rates measured in millions of messages per second, especially when the data is distributed irregularly over time. Market data volumes have massively increased sincethefragmentationoftradingvenuespostregnms andmifid,andtheincreasingadoptionoftechnology, allowing trading firms to increase the number of orders beingsenttotradingvenues. Ilya Gorelik, Deltix Smith: In many ways, the data has always been there, but we could not cost-effectively do anything with it. Additionally, with the need to become more competitive, organizations realize that there could be benefits to including more and different data types into the mix. Kwan: The growth of the data has been exponential. Firms used to trade across a small/finite set of instruments. Even with the most robust set of analytics applied against them, no problem. There has been rapid expansion of the electronic markets, multiplied by the speed and granularity of the data per instrument now in microsecond ticks. Factor that with the wealth of internal performance/risk metrics that firms are collecting, and then with advanced analytics across all of that data. Though computing speeds continue to increase, this complexity can bring any system to its knees. Lovas: LookingatUSlistedoptions,theOptionsPriceReporting Authority s daily peak reached 14 million messages in 2011, an increase of 131 percent over the previous year. This resulted in total messagevolumegrowing78percent.thescaleoftheoptionsmarket isquintessentiallybigdata.anumberoffactorshavecontributedto this exponential growth. Venues such as the Chicago Board Options Exchange s C2 Options Exchange and new products including WeeklyOptionsandVolatilityIndex-basedproductshaveincreased tradingvolumesinstrikesandunderliers.thisproliferationhasput optionsontheforefrontasastrategicinvestmenttool.theresulthas beenanexplosivegrowthinmessagetraffic.theinformationflowis aflood atsunami ofmarket data.onahumanscale,you cannotconsumeormakesense of what s inside that tidal wave without fit-for-purpose Big Data solutions. Chirlian: The financial services industry has always been an information business. So it makes sensethatfirmswiththemost informationandthebestand fastestanalyticsareatasignificant advantage. This competitive factorhasconsistentlydriven financialservicesfirmstogatherasmuchdataastheycanaccess.but ithasalsostrainedeventhelargestdatacenters.firmscontinuetolook forbetterandmorecost-effectivesolutionsfordealingwithgrowth adatasolutionalone,however,isnotenough.theyalsomust apply sophisticated analytic capabilities across this vastly expanded datascape, whichhasputadditionalstressontheirinfrastructure. Q What are the technology and operational challenges that need to be considered when dealing with Big Data? What technologies are available to firms looking to address thisbigdatachallenge? Poulter: Datarecoveryandregenerationoptionsneedtobefully considered, with any business-impacting outage understood. Challengesexistintrainingstaff,acrossthedevelopmentandsupport teams,andensuringthecorrectinfrastructuralsupportisavailable. Specialist consultancies are being used for training, support and consultancyaroundtheimplementationitself.duetothetechnology being relatively nascent, there are few experts across the whole community. June

12 Special Report Big Data Louis Lovas Lovas: Big Data is messy. Market data comes in many shapes, sizes and encodings. It continually changes and requires corrections and an occasional tweak. Discovering new alpha and optimizing existing strategies demands a confidence in the resulting derived analytics. Big Data solutions must manage the vagaries of data sources and complex order-book structures, map ticker symbols across a global universe of exchanges and geographies, and accurately reflect pricing through cancelations, corrections, corporation actions and symbol changes. These are challenging financial-data management obstacles beyond the scope of ordinary storage architectures or file systems. Content-aware solutions leveraging the best of high-performance, scalable compute power are uniquely tuned to fulfill the demanding needs of quantitative analysts and algo traders. Director of Solutions OneMarketData Tel: Web: Brown: In financial markets today, BigDataoffersmanytypesofcontent,bothstructuredandunstructured,thatneedtobecollected,analyzedandstored. Management of these types of data has been a challenge for capital market firms in generalandincludesissuesliketighterbudgets,askillsshortage both withnewtechnologiesaswellasnewdataanalysistechniques legacy systems inabilitytoscale,anincreasednumberofcompetitorswho maybemorenimble,andtheneedtokeepupwithregulatory requirements.thesolutionstosomeoftheseproblemshavebeen knownforawhileandcanbesummedupasfollows: Shared-nothing, highly distributed database architectures. Consistencyisveryhardtofulfillinlargedatasets.Relax most problemscanbesolvedwitheventualconsistency. Don tinsistonnormalization e.g.,hierarchicaldatasetsdon t normalize well. Functional programming frameworks are better at solving most parallel distributed problems. Dataoutagescanbehandledbymaintainingenoughreplicas. TechnologiessuchasCassandra,Hadoop,andMapReducegive firms the ability to massively parallel-process data using functional programmingconstructs,storehugedatasetsinbothdistributedmemoryanddirectattachedstorage,andadeclarativeinterfacethatis notlimitedbysql srelianceonrelationalalgebra. Kwan: Thetechnologyisevolvingrapidly.Whilethedaysofproprietaryformatsaregivingwaytoapplicationprogramminginterfaces (APIs)andmoreflexibleformats,manyfirmsaren tpreparedtomake the switch. Legacy systems are so entrenched into processes, that even thethoughtofreplacementistoopainful.thestrategy,benefitsand return on investment has to be clear before commitment. Chirlian: BigDataallowsenterprisestodeployavarietyofplatforms, eachtargetingacertainanalyticchallenge.businessesthereforemust think through the kinds of analytic applications they want to build andthentailortheirtechnology.typically,bigdataplatformsare incrementaltoexistingdatasilosintheformofrelationaldatabases andfilesystemsalreadyontheenterprise.acriticalgoalistoprovide analystsandbusinessuserswithaccesstoallofthisdata,acrosssilos, for on-demand analytics. From an IT perspective, the question is how tobuildanintegratedarchitecturethatallowsthebusinesstoview allthedataintheenterpriseandbeyond,retrievethedataasneeded, and analyze it at high performance and across any scale. This may be achieved by bringing together multiple independent solutions for each layerofthearchitecture and integrating them. Alternatively, financial BigDataismessy.Marketdatacomesinmany shapes, sizes and encodings. It continually changes andrequirescorrectionsandanoccasionaltweak. Discovering new alpha and optimizing existing strategiesdemandsaconfidenceintheresulting derivedanalytics.bigdatasolutionsmustmanage thevagariesofdatasourcesandcomplexorder-book structures. LouisLovas,OneMarketData services institutions could useanintegratedplatform such as Armanta, which is purpose-builttoenablethis end-to-end analytic process for business applications. Gorelik: Firstly, recordingmarketdataisakinto drinkingfromafirehose, so this is the first challenge. Thereareonlyahandfulofvendorproductsonthemarketthatcan dothis.secondly,onceyoustorethisdata,youdonotwanttobe physicallymovingitfarbecauseofthesheersize.thus,youneedtobe,thattypicallymeansleavingitinornear thedatacenterwhereitwasfirstcollectedorwherethereareticker plantslocated.secondly,intermsofprocessing,ametricmorerelevant thansizeisthenumberofdatapoints,or messages. Becausemarket dataismeasuredinhundredsofthousandsormillionsofmessagesper second,thenanyprocessingneedsbeabletoprocessatasimilarrate. Finally,therearechallengesrelatedtothenormalization,cleansingand filteringofdatawhichoftenrequiremultiplesetsofcomplexanalytical transformations. All these challenges dictate solutions involving a built-for-purpose time-series data warehouse, event-processing and mathematical libraries, all capable of processing data at hundreds of thousandsofmessagespersecond. Smith: There are many challenges, one is that these technologies are not outofthebox andtechnicalskillsintheseareasarenotplentiful. It also changes some of our current thinking in data management, security, and compliance. There are numerous associated technologies. IrecentlyspoketoagroupaboutthevariousHadoopprojectsand sub-projects.iidentifiedatleast20.injusttheareasofmodeling/ 10 June 2012

13 Roundtable development and storage/data management, there are three technologies associated with each: MapReduce, Pig, and Mahout with modeling/development; and Hadoop Distributed File System, HBase, and Cassandra with storage/data management. Q Are most firms approaching Big Data management through a rip-and-replace strategy, or are they layering it on top of their existing infrastructure? Chirlian: AninterestingthingaboutBigDatatechnologyisthatit isextendingenterpriseinfrastructureversusreplacingitbyadding newfit-for-purposedatamanagementandprocessingtools.the challenge today is that infrastructure management has become increasinglycomplicated bothfromanitpointofviewaswell asforbusinessuserswhomustlearnnewtools.enterprisesmust developawaytopackagethiscollectionoftoolsanddeliverthe benefit of the new technologies, enabling users to perform integrated, end-to-end analytics. Smith: Big Data technologies are complementary to our legacy products. Most firms are vetting use-cases and incorporating this key tool set into the overall solution set. Kwan: This is the perpetual enterprise question, and it depends on whichpartofthebigdatayou retalkingabout.we veseenanew generationoftoolsthatdoabetterjobinbothrealms.withthe sophisticationofapisandaggregationtoolsweseethatthelayering when maintenance costs canbesaved. Gorelik: The underlying ability to process vast quantities of market data is achievable only through a few products designed forpurpose.assuch,we seemostlyreplacement strategies. Weseeanumberofareasthatarenotfully appreciated when embarking on Big Data projects, particularlyintheanalysisofunstructureddata. Oftentimes,weseeclientsattempttoanalyzetext believingtheycanhavecontroloverthe secretsauce. Whilethemotivationsareunderstandable,itisavery difficultpropositiononwhichtosuccessfullyexecute andonecanconjureupthephrase, kids,don ttrythis at home. Richard Brown, Thomson Reuters Poulter: For the current implementation, Hadoop is being introduced alongside traditional relational database data stores. Reporting is done across both data stores, summary results are stored in the database, with detailed drill down functionality being provided using Hadoop. Summary results are held in this way to mitigate any risks with data retention for the newer technology. Lovas: BigDataisabigdealtocustomerssothey renotmaking infrastructuredecisionscausally.intheend,we llseedifferentfirms employ different models rip-and-replace and layering. The strategy will weigh in numerous factors, with cost being an important aspect.firmshavetoanalyzethehardwarecostandmaintenanceof insourcing versus outsourcing, and whether it s clustered storage or centralizedstorage,thencompareitagainstexistingarchitectures, factoringinpossiblesalvage.thereneverisaneasyanswer. Richard Brown Global Head, Quantitative and Event-Driven Solutions Thomson Reuters Phone: Web: Brown: Inmanycases,theinterdependenciesofvarioussystems wouldmakeitimpracticaltoripandreplaceitallatonce.wesee most new initiatives being brought up in isolated environments and legacysystemsordatamovingtothosenewtechnologiesafterthenew systemshavegonethroughthetypicalteethingpains.oncedevelopment and support staff are comfortable with the solutions, the pace at whichtheoldersystemscanberetireddramaticallyincreasesandfirms areabletoreapsomeofthepromisedrewardsoftheproject. Q Are there existing technologies cloud computing, for example thatcanbedeployedinacomplementaryfashion alongside specific Big Data technologies to help alleviate the BigDataburden? Brown: Cloud computing offers great promise for firms needing todynamicallyflextheirprocessingneeds,especiallyatpeaktimes such as market open and close, withouthavingtopayupfortheidle systemtime.thatcapacitycanbe balanced against other users needs, particularly in more public clouds, butfinancialfirmsarestillreluctant to place proprietary data or processes inthepubliccloud.instead,theyare increasingly building private clouds behindtheirfirewallstoexploitthe computational advantages, rescheduling batch jobs when possible to balanceworkloads,andreducingthe overallsystemfootprint.thisflexible computing environment also enables firms to deal with sudden data bursts, like the Flash Crash, which requireveryrapidandextensiveanalysisinordertoadjusttheirmodels torespondappropriatelythenexttimeitseessuchanevent,orevenat thenextmarketopen. Smith: Withcloudcomputing,absolutely.Theflexibleandscalable characteristicsofcloudcomputingmakeittheideal,underlying infrastructurelayeronwhichthebigdatastorage/datamanagement andmodeling/developmentlayerslie. Gorelik: Cloudcomputingisnotonlyanaturalcompanionto Big Data, but in the case of serious quantitative research, it is an enabler,andinsomecases,essential.theabilitytodistribute complexcalculationsacrossmultiplenodesinthecloudatan June

14 Special Report Big Data Dennis Smith BNY Mellon affordableandvariablepriceisamajor differentiator of cloud computing architecture. In addition, in many cases, it is simply impractical to transfer significant volumes of historical market dataelectronically.ratherthanphysicallyshipharddrives,cloudcomputing servicesdeployedinthedatacenters wheredataisavailable,allowsanalysis to be done in situ. Poulter: Yes, we are using Hadoop and DataSynapse in tandem, with the databeingheldphysicallyonthesame machinesasthegridengines.hadoopis,ineffect,replacingthe roleforwhichcoherencehasbeenusedtraditionallyinenterpriseenvironments,providingaccesstocacheddata. Lovas: BigData scomplementarytechnologyisreal-time analysisthroughtheuseofcomplexeventprocessing.thesetwo technologiesdefineasolutions paradigmforquantitativemarket analysis covering quantitative trading, research and transactioncost analysis (TCA). The ideal case is to view historical activity andreal-timeasasingletimecontinuum.whathappenedyesterday,lastweekor10yearsagoissimplyasextensionofwhatis occurringtodayandwhatmayoccurinthefuture.quantslook to compare current market volumes to historic volumes, prices andvolatilityinthehuntforalphaandtocontroltradecosts. Chirlian: Absolutely. On the deployment side, cloud computing alleviates some Big Data concerns. Customers may now deploy Big Data solutions on-premises, on physical or virtual resources, orinthecloud.therearealsoinnovationsontheinfrastructure side that allow customers to tackle specific business problems. However,theseadvancesalsopushcomplexitytotheuserand require, as we said earlier, a packaging of the technology so userscanrealizethetruevalueofbigdata. Kwan: Theseexistingtechnologieshavetomeshwiththe strategyandtheappropriatetimelinessofthedata. Thecloud provides greater access, transparency, and ultimately speed, tocertaintypesofdata.marketdatafordecision-makingon a desktop or in an algo system many times has to be a direct connection. Q What do most firms tend to overlook when embarking on Big Data projects? Gorelik: Wefindthatthefocusonstoringdatasometimes resultsininsufficientemphasisbeingplacedontheuseofdata. Regulatoryrequirementsaside,storingdataisonlyusefulifsubsequentanalysisyieldsinformationthatisvaluable.Suchanalysis is computationally and mathematically complex and demanding. Havingtoolstodefineandtesttradingideasquickly,andthen refineideas,isamajorcompetitiveadvantageinatradingfirm. Byfocusingonthelogisticalheadachesofcollectingdata,an emphasis on analytical tools is often overlooked. Lovas: BigDataultimatelydefinestheendgame,thatHolyGrailfor profitability.firmsshouldnotlosesightofthat.thebigdatadump andthesolutionstomanageandanalyzeitarethefuelthatdrivesthe engine of the trade life cycle. That includes the profitability profile of newmodels,optimizingexistingmodels,re-balancingportfoliosand managingthefluidnatureoftransactioncosts.theyalldependonbig Datasolutionstoprovideaccurate,cleandataacrossafirm stradable markets.firmsneedaclearunderstandingthatbigdataispervasive acrosstheengineofthetradingbusinesstoensuresuccess. Kwan: Strategy, Strategy, Strategy. Have you really figured out howtomaketradingdecisionsoffsocialmediaandtweets?maybe. Historically,successfulfirmshavebeenabletofindpatternsinthe market.ibelievethegameisthesame,butthedatasetismuchmore complex.firmshavetobeabletoadapttonewmethodsofpattern finding.abigpartofthatisinfrastructure,ofwhichaveryimportant pieceisanewclassofadvancedvisualizationtools. Brown: Weseeanumberofareasthatarenotfullyappreciated whenembarkingonbigdataprojects,particularlyintheanalysis ofunstructureddata.oftentimes,weseeclientsattempttoanalyze textbelievingtheycanhavecontroloverthe secretsauce. While themotivationsareunderstandable,itisaverydifficultproposition onwhichtosuccessfullyexecuteandonecanconjureupthephrase, kids,don ttrythisathome. Challengesrangefromthedifficultyof aportfoliomanagertovetaqualifiedlinguisticanalystteam,tonot knowing what you have/don t have, until it s failed/finished. Weseealotoffalsestartsandabandonedprojectsinthisspaceand insomecasesitisduetounsuccessfullanguageprocessing,andin others,thefirmshavetroublegettingthosetechniquesintoproduction with the necessary fault-tolerant, fully resilient infrastructures to handle such information in the speed needed for financial markets. Webelievetherightsystemshouldbeflexibleenoughtoenableusers todowhattheywant,butrobustenoughforthemtosimplywant tofocusonthehighervalue-addedactivitieslikeinterpretingthe analytics for their investment or trading strategies. When it comes to unstructured/textanalysis,thomsonreutersnewsanalyticsoffersa greatmixatbothendsofthespectrumandeverywhereinbetween. Smith: Imentionedafewofthesebefore:skills,datamanagement, andsoforth.lookingatthedatamanagementlayer,bigdatamight causethethinkingtogofromaphysicalorientationtoalogicalorientation,wherethingsarerelativeforjustaperiodoftime.additionally, itmightchangethinkinginthedataqualityarea,fromthingsneeding tobe100percentaccuratetobeingdirectionallycorrect.thisalso highlightsthecomplementarynatureofthetechnologywhereitcould front-end traditional tools. Chirlian: BigDataisrevolutionizingthewaybusinessisconducted.It isn tenoughthatenterprisesinvestinnewtechnologiesformanaging andanalyzingdata.theymustnowbeabletoarmtheirbusinessusers witheasy,anytimeaccesstothedatatheywantandenablethemto analyzethisdatainteractively.theanalytics-drivenbusinessesofthe futurearethosethatunderstandthisend-to-endanalyticprocessand putinplaceawell-integratedtechnologysolutionempoweringthe businesses to be confident in their decisions. 12 June 2012

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16 June 2012

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