Big Data Solutions in Capital Markets

an IBM Company

Big Data SolutionS in Capital MarketS a reality CheCk

February 2012

An industry briefing prepared by a-team group for

Researched and written by:


An industry briefing prepAred by a-teaM group for platforM CoMputing 2

3. introduction

4. executive Summary

5. Data Challenges Facing Capital Markets practitioners

8. What is Big Data and What role Can it play?

10. how Big Data technologies are Being used today

14. Building the Business Case

15. Conclusion

table of Contents



introductionBig Data has emerged in recent months as a potential technology solution to the issue of dealing with vast amounts of data within the enterprise. As in other industries, financial services firms of all kinds are drowning in data, both in terms of the sheer volume of information they generate and / or have to deal with, and in terms of the growing and diverse types of data they confront in those efforts.

But the relative immaturity of Big Data solutions, and widespread lack of understanding of what the term really means, leads some to question whether Big Data is no more than a technology solution looking for Big Problem to solve.

So is Big Data for real? Can so-called Big Data solutions provide relief to the embattled data architects at financial institutions? Or is Big Data a solution looking for a set of problems to solve?

Research conducted by A-Team Group on behalf of Platform Computing suggests that current market sentiment, financial hardships and regulatory scrutiny may be conspiring to create the perfect conditions for Big Data solutions to provide value to financial institutions.

And with a growing number of technology alternatives emerging in the Big Data space, practitioners are finally taking a serious look at how the concept might help address specific business issues relating to management of large volumes of often unstructured data in order to meet business-side and regulatory requirements for more transparency across the enterprise.

A-Team Group interviewed innovative data technologists within financial institutions, market utilities, regulators and service providers to understand attitudes toward so-called Big Data solutions, and how they could be applied to solve data management issues within Capital Markets. It supplemented this primary research with a poll of attendees at its recent Data Management for Risk, Analytics & Valuations conference in London, and secured examples of use cases from its network of financial IT industry contacts, in all gaining the opinion of some 35 financial data professionals.

This paper draws upon that research and looks at some of the current headaches facing data architects within financial institutions of all shapes and sizes, and suggests that Big Data technologies could be deployed to help solve some tricky data problems. Further, it cites recent market research to offer real-life examples where Big Data approaches are being considered and employed to solve real-life problems.



executive SummaryThe research found wide-ranging perceptions of Big Data solutions, with many respondents unclear on definitions. This stemmed from the widespread belief that Big Data technologies those data platforms capable of handling vast quantities data and/or combinations of structured and unstructured data are relatively immature and unproven.

That said, a number of practitioners held the belief that Big Data could help address major data management challenges stemming from large quantities of structured and unstructured data; issues around data quality and data formats; the inability of existing data warehouse and business intelligence platforms to handle this scale of data and this range of data formats; and a rising tide of regulatory scrutiny across global market centres that will drive the need for more and more-detailed information.

Others, meanwhile, were either working on, considering or had identified the applicability of, Big Data in the following areas: Litigation response/regulatory compliance; Control over internal data (and applications); Risk analytics/enterprise risk management; and Trading analytics/on-demand database analytics.

Even those firms in the midst of fully funded Big Data projects reported management resistance to enterprise-scope projects in the current economic environment, making it difficult to secure funding for Big Data projects promoted as such. Several recommended a stealth approach to securing project approval, through the selection of highly targeted, business-led sub-projects that build a broader enterprise environment using Big Data to meet data management challenges.

The message from the research is that capital markets institutions of all types buy-side, sell-side, exchanges, regulators and service providers are considering and in some cases implementing Big Data solutions in response to the huge volumes of data they are forced to deal with. While Big Data solutions may be in their infancy, market practitioners are taking their promise of handling large volumes of structured and unstructured data seriously, and expect to see deployments in the next 12 to 18 months.



Data Challenges Facing Capital Markets practitionersIndustry practitioners within all types and sizes of financial institution are being challenged by data management requirements posed by a variety of factors. Despite lower trading volumes, data volumes continue to rise across the board, in part due to the increased complexity of the underlying business, the generation of huge numbers of quotes from high frequency trading techniques, and far greater granularity of meta-data demanded by clients as they seek to understand the data model that underpins any given data metric.

This increase in data volumes is being accompanied by inconsistency of data both in terms of quality and formats indeed, much of it may be unstructured making the higher volumes that much more difficult to manage. At the same time, the heightened emphasis on transparency generated by a range of new regulatory initiatives is forcing firms to address their data issues. Among the incoming regulations cited by respondents were:

Markets for Financial Instruments Directives 1 & 2 (MiFID/MiFID2), best execution Markets for Financial Instruments Regulation (MiFIR), derivatives market transparency/fungibilityDodd-Frank, legal entity identifiers (LEIs) Solvency II, reporting obligations on asset managers holdings of insurers investmentsBasel IIIKnow Your Client (KYC)

The fear of having to handle a discovery process perhaps from litigation or a regulatory audit/investigation related to incoming rules is emerging as a key driver behind the need to manage data of all types (structured and unstructured) across the enterprise. A-Teams research suggests that firms were well equipped to handle structured data, including pricing and tick data, reference data and other commercially sourced information.

But few had solutions to the challenge of managing unstructured data, and several of those interviewed indicated the need to manage (search and manipulate) email, instant messaging, PDF documents, audio files, video and social media messages in response to litigation or the threat of it. In the belief in a growing trend toward litigation from clients, employees and other stakeholders data architects were genuinely concerned about their ability to respond to onerous audit demands of regulators or the courts.

In light of these concerns, firms are anxious to gain control over the data being held within the enterprise. Control was a common goal among A-Team survey respondents, with a widespread belief among them that an accurate inventory of data of all types (including, in some cases, applications/code and rogue databases) would allow their institution to gain control over their data. This would in turn allow them to apply that data in a useful or valuable way, or where appropriate delete it.

Many saw a major storage challenge in gaining this control over their data. With genuinely huge volumes of data involved with more being generated in real time firms were perplexed by the prospect of deploying vast storage capabilities in order to take data off-line in order to perform inventory tests on it. While acknowledging the relatively low current cost of storage, these respondents suggested that versioning and other data management challenges associated with such huge volumes of data would make a thorough inventory process prohibitive using existing technologies.

As pressing for many practitioners is senior managements focus on risk. Aside from the stick of regulatory action, many financial institutions are working toward true enterprise risk



management on a near real-time or on-demand basis seen by some as the Holy Grail. Gaining an on-demand view of business performance across the breadth of a firms activities is seen by many as a key business driver for gaining control over data within the enterprise.

But due to the issues of data quality and consistency as well as the different operating methodologies, applications and data models underpinning firms internal data systems getting a consistent view of performance and risk is a monumental data challenge.

That notwithstanding, senior management C-level executives and board members among them is exerting pressure on data architects to come up with solutions to this data management issue. Many of those interviewed suggested that demand for enterprise risk or on-demand P&L calculations are driving the push toward a consistent data model across the enterprise.

Finally, data managers are facing demands from the front office as well. Trading groups everywhere are seeking to identify and exploit new business opportunities. Increasingly, these under-served and potential lucrative market segments involve analysis of unstructured sources of data, with new types of trading analytics such as sentiment analysis drawing upon web-based services, unconventional information services (like machine-readable news) and social media (like Twitter).

In short, firms are facing major data management challenges stemming from:

Largequantities(measuredinterabytesandpetabytes)ofstructuredandunstructureddata.

Issuesarounddataqualityanddataformats(whichaswellasstructuremarket,reference and tick data include email, PDF, video, audio, machine-readable formats, etc.)

Performanceissuesrelatedtomanagingthesedisparatedatasetsandformats. Theinabilityofexistingdatawarehouseandbusinessintelligenceplatformstohandle

this scale of data and this range of data formats. Arisingtideofincreasedregulatoryscrutinyacrossglobalmarketcentresthatwilldrive

the need for more and more-detailed information.

Against the backdrop of this environment, practitioners are facing demand for more and better data in a number of areas, including reference data (with structured, unstructured and public data), real-time trading analytics, predictive business analytics, risk management (enterprise risk management, credit scoring, market risk scoring, etc.), improved and more timely regulatory compliance, and improved stress testing.

Firms appear to have identified the need to handle unstructured data as they address these issues (see Figure 1).

Figure 1. Is your organisation equipped to handle unstructured data as well as traditional structured data?



With both the challenges facing data management specialists and enterprise infrastructure architects within financial institutions, and the opportunity to deploy Big Data solutions in a number of value-added areas apparently in alignment, the research yielded a number of real-life use cases that meet specific and real challenges being faced today.

Among the key business drivers identified by the research (see Figure 2) were:

Litigationresponse/regulatorycompliance Controloverinternaldata(andapplications) Riskanalytics/enterpriseriskmanagement Tradinganalytics On-demandenterpriseanalytics.

Figure 2. What kinds of challenges can Big Data solutions help address?



What is Big Data and What role Can it play?Faced with this onerous set of data challenges, and in the belief that existing data management technologies are unable to meet them, financial data architects are casting their nets wide to identify potential solutions. For many, Big Data appears to hold the promise of an approach that could help address the need to manage and manipulate large quantities of widely varying data types.

But ask 35 data professionals what Big Data means, and youll get 35 different answers. A-Teams research did just that. Perhaps unsurprisingly there were wide variations in what market practitioners understood the term Big Data to mean. For some, it merely involves management of large volumes of data, perhaps from more than one source, often on more than a single server. For others, it refers to the ability to handle data on an enterprise basis, perhaps in pursuit of a specific goal (e.g., enterprise risk management, on-demand P&L data).

Anecdotally, those respondents from the US or working from US-based organizations appeared to have greater familiarity with the term Big Data and were more sophisticated in their responses than their European counterparts.

For these and other interviewees that appeared better versed, Big Data was loosely defined as the identification, classification and use of unstructured data alongside or with traditional structured data sources at high volumes. Some of these respondents preferred to define it in terms of data volumes perhaps from 500 terabytes or 1 petabyte that need to be integrated, manipulated, correlated and managed. Practitioners suggested requirements for response times were shifting toward millisecond latencies, from end-of-day batch processes.

The more knowledgeable respondents to the A-Team survey saw Big Data as a potential solution to the challenge of managing new unstructured data sets alongside established data management techniques for handling transaction or tick data and other structured data they are currently able to warehouse using existing technologies.

With unstructured data, the challenge is to support data capacity on demand, supporting access to a broad range of data types, including market data, reference data, performance data and analytical data, sourced internally from transaction flow, or externally from data vendors, all from the same infrastructure. As the range of data types issued by financial institutions expands, data architects are now exploring how to add video, voice, text documents, email, Bloomberg messaging and PDFs to the mix. Big Datas embrace of existing data models and potentially new ones appears, in the eyes of many data architects, to qualify it for consideration as a solution to this kind of data consistency issue.

Meanwhile, many of those surveyed see Big Data as a possible solution toward gaining an enterprise-wide view of risk, if not in real time then on an on-demand basis. A typical challenge facing global risk data architects is to provide the global head of markets a single report that gives him a view of his global business. The task requires pulling in a lot of data from multiple systems, with the risk that integration introduces errors into the source data.

Big Data can help avoid such situations by ensuring any data aggregation platform does not interfere with live, production systems in, say, the front office where much of the required data is originated. In essence, the enterprise is a series of vertical businesses, and Big Data technologies can provide the framework for developing an overarching model to meet the global business heads requirement, by building desk- or activity-specific solutions that meet



the more detailed needs of the front-office traders.

For whatever purpose, 75% of practitioners participating in the research appeared to be accepting the need to at least explore Big Data possibilities in the next 12 months, with some even having started deployment (see Figure 3).

Figure 3. Does your organisation expect to deploy Big Data technologies in the next 12 months?

Because of the relative immaturity of Big Data as a data management concept, there are many diverse views of precisely what it is and how it can be applied to capital markets. Its clear, however, that data management and infrastructure innovators have settled on the idea that Big Data could provide the solution for a number of data management and quality challenges involving high volumes of data often in a range of structured and unstructured formats.

Pioneers and early adopters believe Big Data can help address the challenge of managing large volumes and disparate formats and qualities of data across the enterprise, yielding significant business benefits ranging from control of data through to on-demand analytics and risk management.



how Big Data technologies are Being used todayMarket practitioners interviewed by A-Team Group saw a range of potential uses for emerging Big Data technologies, and several were already in the midst of implementing such solutions to meet business needs. Others were exploring how Big Data solutions could be used to address identified data management problems.

Many interviewees saw specific enterprise technologies as useful for specific use cases, with different approaches for structured (OLTP/data warehouse) data and for unstructured data, where they saw a coordinating or overarching role for emerging Big Data technologies like MapReduce and Hadoop.

Interviewees cited a number of solution types for handling the task of unstructured data integration. These included:

Datagridswhichusedistributedcachingtomanagelargevolumesofdataacrossanetwork of servers (cited by 17% of respondents).

Computegridswhichofferawayofparallelizingprocessesacrossmultipleservers,handling capacity/failure issues and orchestrating tasks across the grid (cited by 20% of respondents)

Massivelyparallelprocessorswhichinvolvesthecoordinatedprocessingofaprogramme between multiple independent computers, leach with its own operating system and memory (cited by 31% of respondents)

In-memorydatabaseswhicharedatabasethatstoredatainmainmemoryratherthanona disk, as is the case with traditional databases (cited by 17% of respondents)

NoSQLwhichareshellrelationaldatabasemanagementsystemsthatdontuseStructuredQueryLanguage,aremoresimplethantraditionaldatabasesandwhosetablesare compatible with a wide range of external platforms (cited by 14% of respondents).

Several respondents indicated that they were using highly specialized data management applications to address specific sub-tasks within broader Big Data enterprise-wide projects. These ranged from specialist risk information management systems to complex event processing platforms, which were often deployed in tandem with broader enterprise platforms.

Respondents were widely familiar with MapReduce and Hadoop, which are frequently cited as key examples of Big Data technologies (See Figure 4, below). MapReduce originated from a research paper published by Google that illustrated how Google indexes the web. Yahoo adopted a similar approach, leading to the emergence of Hadoop, an open-source platform that includes a MapReduce framework for executing many separate compute tasks in parallel.

Figure 4. Do you use or would you consider using Big Data solutions like Hadoop/MapReduce and others to address your organisations enterprise data management challenges?



Among the specific use case examples identified by the research were:

(i) On-demand enterprise risk management and other analytics. Practitioners saw this as the Holy Grail of data management, driven by more rigorous scrutiny of risk activities by regulators everywhere.(ii) Preparation for actual or anticipated discovery process as part of a litigation or regulatory audit of activities, data, systems and so on.(iii) On-demand data mining for use by clients, allowing them to dig into meta-data to deconstruct/reconstruct data models according to their needs.(iv) Pre-trade decision-support analytics, including sentiment measurement and temporal/bi-temporal analytics.

Among the specific examples unveiled by the research were:

Tier 1 US Investment Bank

Project Objective: To shift risk management and P&L monitoring toward real-time environment (driven by fear of audit).

Data Types: Market pricing and trade data.

Volume Level/Update Frequency: Multi-terabyte (10GB/day of tick data, shifting from millisecond to microsecond updates).

Approach Adopted: Combined use of specialist risk data integration software with Oracle and XML environments.

The project manager believes Big Data can help the firm to gather together all of the relevant data into one place. Once it is in one place, he believes, its easy to manage and report on and does not require Big Data-type solutions. But getting, say, all trades and current pricing into one system in real-time is extremely challenging for a large corporation. Smaller firms do this easily; larger firms with difficulty or at T+1 or worse reporting timeframes.

Tier 1 European Investment Bank

Project Objective: Performance statistics monitoring, risk- and market-abuse compliance reporting, network and internal application optimization, and long-term archival compliance querying and reporting, plus disaster recovery, covering a broad range of trading room applications.

Data Types: Trade data.

Volume Level/Update Frequency: 5 terabytes of data handled by Exadata environment. Nearreal-timeperformancemonitoring.

Approach Adopted: Exadata for improved performance over existing database environment, plus disaster recovery.

The bank plans to apply the solution to analytics, trade and risk management challenges, onceithasbeenoptimizedtofurtherreduceSQLquerying.Thenewenvironmentsupportsthe high end of the banks high/medium/low- speed data-access scheme, and stores the previous weeks trading data, plus any data accessed by an internal-facing Web-site used for monitoring and querying.



Tier 1 Asian Investment Bank

Project Objective: Generation of on-demand business performance metrics across multiple global trading businesses, including risk measures.

Data Types: Pricing and trade data, counterparty data, risk exposure data.

Volume Level/Update Frequency: Real-time/near real-time.

Approach Adopted: Project in planning stage.

Project manager sees a challenge in gathering data from a number of different execution infrastructures within the bank, which generate different data types depending on their respective end-user environments, some of which may be external. The project is expected to involve the coordination of pre- and at-trade risk infrastructures and aggregation of their outputs in real time for monitoring. At the same time, the process needs to be coordinated with the disparate reporting schedules of clearing organizations, including CCPs.

Tier 1 European Investment Manager

Project Objective: To gather all relevant information to respond as witness in litigation action against its prime broker.

Data Types: Pricing, trade, commentary and derived/commingled data. All statements/ communications by firm and its prime broker relating to specific markets (including email, IM systems, PDFs, voice and video).

Volume Level/Update Frequency: More than a decades worth of market information and communication with prime broker.


The firm is exploring how to implement a top-level data management layer to combine unstructured data with existing structured data sets. The project manager is seeking to manage vast amounts of data of this kind and add the ability to maintain some form of ownership over this data, and to blend it with external data. There is a need for metrics to indicate what data is being used and for what purposes.

Tier 1 US Investment Manager

Project Objective: Centralization of data and applications to gain control, apply governance policies and mitigate risk of damages from litigation discovery.

Data Types: Broad range of unstructured data, including: Documents, PowerPoints, video (YouTube); executable files (including unauthorized applications in Excel, Access, and downloaded from Web), applications on mobile devices (e.g., phones, iPads).

Volume Level/Update Frequency: 500 terabytes.


Project manager has not yet identified a single solution to his challenges.



Tier 1 Global Exchange

Project Objective: To provide global market participants with on-demand access to data and data-mining tools for trading, analytics and risk management in a cloud-based/hosted environment.

Data Types: Broad range of event-based pricing and descriptive/meta data relating to major US and European equities and derivatives markets.

Volume Level/Update Frequency: Low-latency/millisecond access to all listed instruments, plus breakdown of associated metadata.

Approach Adopted: Partnered with a range of storage system and analytical framework providers; evaluating how to handle unstructured data sets.

Project manager sees Big Data technologies cloud, databases, storage applications as powerful tools to act on granular information and meta data, allowing trading participants to deconstruct exchange-provided data.

Tier 1 US Regulator

Project Objective: Creation of a searchable library of research, econometric and other information generated by the regulators activities.

Data Types: Text-based research, econometric data, models, tick data.

Volume Level/Update Frequency: Multiple terabytes of data.

Approach Adopted: Research teams are using commercial tools like to mine the regulators rich data sets. The organization is exploring options for a platform to handle the processing of its huge volumes of data.

Project manager believes existing database technologies arent capable of handling the task in hand. He foresees a requirement for as many as 16 parallel processors running individual segments of data in order to handle unstructured data. Acknowledging that the large search engines and by extension platforms like MapReduce and Hadoop are capable of trolling unstructured data and documents, he believes the challenge is in constructing and applying the correct metadata.

Other use-cases not explored by this research but identified from the event poll and other A-Team research include:

Marketsurveillancewhichwascitedbyseveralsurveyrespondentsasanactivityrequiring processing of vast quantities of market information.

Fiduciarymanagementagrowingareaofinterestinwhichassetmanagersoutsourcemanagement of their portfolios to third-party administrators in order to benefit from economies of scale. Both these areas appear ripe for Big Data-type solutions, requiring as they do access to large amounts of data from a broad range of sources.

Both these areas appear ripe for Big Data-type solutions, requiring as they do access to large amounts of data from a broad range of sources.



Building the Business CaseSeveral specific potential objections to securing funding for Big Data projects were highlighted in the research, but most appear to stem from the perception of the technologys relative immaturity.

From our survey, a number of obstacles to the adoption of Big Data technologies were cited: data governance (or the lack of it); integration challenges; staff skill sets (or the lack of them); and interoperability and standardization issues. But the majority of interviewees involved in Big Data projects said organizational, governance or logistical issues represented the main obstacle (see Figure 5).

Figure 5. Besides budget, what are the main obstacles to securing funding for Big Data projects in the current economic environment?

Even innovators were wary of misstepping in their deployments, partly due to a lack of previous experience to draw upon. Conversely, this was seen as a strength of the technology, with the potential for gaining early-adopter advantage seen as compelling.

Another objection was the general reticence when it comes to so-called enterprise projects. This was due both to the current poor economic climate, wherein any large-scale IT project is unlikely to get senior management buy-in, and to the poor track record generally of enterprise projects seen by many as promising much, delivering little and costing too much.

The logistics and governance models for implementing a necessarily complex Big Data project was cited by several innovators. In some cases, architects felt stymied by the complexity or politics of their organization, which resulted in, say, a lack of buy-in from the IT department from a business-led initiative or vice versa. Others cited the difficulty of establishing ownership of a Big Data project, suggesting that it should originate anywhere but the IT department.

Several more innovative respondents suggested a hybrid tactical/strategic approach to getting Big Data projects funded and implemented. This approach entails deploying Big Data technologies to address a very specific business problem, scoring quick wins and moving to the next business challenge. In this way, several respondents suggested, architects can work toward an overarching enterprise solution while solving individual business problems.



ConclusionBig Data and the emerging technology solutions designed to address its challenges remain in their infancy. But the research suggests that the concept is more than a mere buzzword. Market practitioners are feeling overwhelmed by the glut of information they are being forced to deal with, by new regulatory requirements and new business imperatives, particularly relating to risk. For them, Big Data offers a ray of hope that they will be able to meet these new requirements.

As such, data architects are already earmarking Big Data technologies as potential solutions to specific business problems, including the creation of a true enterprise view of risk, the shift toward on-demand (as opposed to batch) business analytics, control over internal data (both to maximize its potential and to mitigate the risk of litigation or regulatory discovery) and trading analytics.

With funding resources under pressure, project managers are focusing on business solutions in order to secure cash for their data management initiatives, giving Big Data solutions a set of acute targets that can show what the technologies can do. In many cases, these targeted projects will form the basis of a more enterprise-wide approach to installing standard rules and practices around management of data from multiple silos, in multiple locations and in multiple formats.

In short, Big Data is being taken seriously by the capital markets community, and progress is expected over the next 12 to 18 months as firms react to the broad range of regulations that are coming into force and raising the bar with respect to enterprise data management.



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A-Team Group, founded in 2001, provides a range of global online news, in-depth research reports, and events focused on the business of financial information technology.

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