Reference Data Management: The Case for a Utility Model


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For capital markets firms to successfully innovate in the RDM function, they must first focus on developing new data management models, while being open to receiving RDM as a service over the long term.

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Reference Data Management: The Case for a Utility Model

  1. 1. • Cognizant ReportsReference Data Management:The Case for a Utility ModelFor capital markets firms to successfully innovate in the referencedata management function, they must first focus on reengineering anddeveloping new data management models, while being open to receivingRDM as a service over the long term. Executive Summary regulatory reporting, cross-product selling, risk Capital markets firms are grappling with the management and operational efficiencies. challenges of increased regulatory oversight and reporting requirements. The growing emphasis We believe firms should deal with these on risk management, industrialization1 of the challenges by employing different strategies in sector and the need to address traditional fault both the short-to-medium and long terms. In the lines has exposed the inefficiencies of reference short-to-medium term, they should reengineer data management (RDM). RDM by establishing an enterprise-wide data governance framework, as well as rationalize Operating in a low-returns era, capital markets data procurement and processing by using firms are struggling to meet increased regula- an appropriate data management model tory compliance requirements and associated (centralized, federated or hybrid) that suits their costs. At a time when gradual industrialization is business needs. forcing them to do more with less, the traditional ways of managing reference data are proving to Businesses are under pressure to lower costs as be cost-intensive and inefficient. The lack of data the operating environment continues to be char- governance and universal standards compounds acterized by low margins and decreasing rev- the problem. enues. For businesses to survive and grow in such a scenario, we believe that committed players, As organizations realize that data management including service providers, should form a mutu- plays an important role in revenue generation, ally dependent ecosystem in order to establish the need is growing for next-generation reference standards for delivering reference data as ser- data management capabilities. Firms must focus vice utilities. These utilities should address the on reinventing RDM capabilities and exploiting the needs of players across all markets in which they power of new technologies such as virtualization operate, offering a level playing field and provid- and analytics, as well as using social media data ing cost-efficient services in a secure and reliable to design better trade strategies and improve environment. cognizant reports | november 2012
  2. 2. The utilities should offer standardized processes operational efficiency and profitability (see built around universally accepted data standards subsection, ”Addressing Traditional Fault Lines”). and industry best practices,Following the global with payment based on a Regulatory Challenges financial crisis, per-use basis. The owner- An unprecedented level of regulation is unfold- it has become ship of infrastructure and ing on both sides of the Atlantic to address maintenance costs is trans- systemic risks, as well as the soundness and paramount to have ferred to the utility, which transparency of the financial industry. These a holistic view of is operated by an objective include the Dodd-Frank Act, Basel III, Markets risk exposures and expert third party, leav- in Financial Instruments Directive (MiFID) II, ing firms with the flexibility European Market Infrastructure Regulation from a particular to address market volatil- (EMIR), Foreign Account Tax Compliance Act counterparty, ity. However, the success of (FATCA) and Know Your Customer (KYC). The new index, region or utilities will depend on col- emphasis on regulatory reporting encompasses laboration among the vari- data on trades and counterparty identities to adverse event. ous industry participants. verify compliance and reduce systemic risk. Ultimately, organizations that are prepared to Organizations will need consistent and accurate recast their RDM will secure a significant advan- reference data to understand the risk profiles and tage in these uncertain and volatile times. underlying dynamics of financial instruments and their legal entities. Regulatory changes requiring Forces of Change over-the-counter (OTC) derivatives to be traded Four principal forces are driving the need to through exchanges and cleared through central overhaul the RDM function. They include: counterparty clearinghouses are expected to put additional pressure on the RDM function. The • Increased regulation. reason, according to TABB Group, is OTC market • Industrialization of banking and financial reforms in the U.S. and Europe, which will increase services. data rates by 400%, transaction volumes by • Inefficient siloed structures of traditional 20-fold and market data volumes by three- to four- RDM systems. fold from current levels.2 The push for straight- • Emergence of next-generation technologies through processing with shorter trade settlement capable of enhancing RDM capabilities. cycles and quote- or order-driven marketplaces will compound the pressure on clearing technol- Increasing financial regulation is subjecting ogy and infrastructure requirements. capital markets firms to higher levels of reporting, compelling them to devise new strat- Another significant impact of increased regula- egies for integrated risk management so they tion is the emphasis on risk management. Capital can measure and manage risk and capital across markets firms are expected to improve their risk a range of business activities. These challenges management systems for both compliance and will be compounded in this prolonged era of low business purposes. From the RDM standpoint, returns, which is expected to further industrialize their ability to understand their counterparty the sector. risks is impeded by a lack of universal standards for legal entity identifiers (LEI), as well as incon- In such a scenario, the inefficiencies in the tra- sistent reference data. Following the global finan- ditional RDM model — such as data silos, labor, a cial crisis, it has become paramount to have a cost-intensive data cleansing process, the inabil- holistic view of risk exposures from a particular ity to rationalize data costs and a fragmented counterparty, index, region or adverse event. vendor landscape — are compelling organizations to rethink RDM. In the aftermath of the global Industrialization of Banking and financial crisis, firms are increasingly looking at Financial Services next-generation RDM capabilities that harness The increasing cost of regulatory compliance, the power of virtualization, social media and coupled with higher levels of mandated capital, analytics to navigate volatile markets, capital- is expected to reduce profitability over time. This, ize on market opportunities and improve their in turn, is expected to further accelerate banking cognizant reports 2
  3. 3. and financial services industrialization. Banks’ was treated with benign neglect. Historically,return on equity (ROE) has decreased sharply, siloed departmental structures have evolvedfrom 15% pre-crisis to below 10% post-crisis to suit the nature of business, product-centric(see Figure 1).3 Similarly, the ROE of capital models and widespread M&A activity. Mostmarkets firms and investment banks stands at 7% organizations run RDM in independent silos,to 10%, which is 50% of historical levels and half resulting in avoidable costs, which are magnifiedof investors’ expectations.4 Today, the average by redundant subscriptions for data and cus-8% ROE earned by banks in the U.S. is less than tomized technology platforms to manage data.their cost of capital. As such, firms typically end up subscribing to and cleansing (a labor-and cost-intensive process)Basel III is expected to require some banks to a nearly identical set of reference data multiplehold capital at three times the level mandated times. The customized reference data require-by Basel II. European banks will need €1.1 trillion ments of individual business units require skilled($1.6 trillion) of additional Tier-1 capital, data specialists to manually fix inconsisten-€1.3 trillion of short-term liquidity and about cies and optimize data. The same siloed model€2.3 trillion of long-term funding to meet Basel is replicated even when the RDM operation isIII norms.5 The rules are expected to reduce outsourced or sourced to captives.ROE of the average bank by 3% in the U.S. and4% in Europe.6 An EIU survey reports that a Many reference and market data vendors have amajority of firms are concerned about new niche focus and specialize in certain asset classesregulations increasing their cost base, affecting and regions. Therefore, firms that cover severaltheir financial performance and competiveness, asset classes and regions must source data fromand hampering their abilities to introduce new multiple vendors. With no universal industry stan-products and services (see Figure 2, next page).7 dard in place, many data vendors and firms use proprietary standards to identify and enrich theirThe prospect of greater industrialization should reference data.motivate organizations to focus on addressinginefficiencies, reining in waste and considering Many organizations also fall short on enterprise-more innovative business-technology initiatives wide data governance and struggle to understandto enable them to survive if not prosper in the the lineage of data, how much of it is used and bylow-margin era. whom. Without a handle on how reference data is used across the organization, rationalizing dataAddressing Traditional Fault Lines handling costs becomes a daunting task.RDM has traditionally been viewed as aback-office cost center function and, as such,Return on Equity of U.S. and British Banks, Post-CrisisROE has dropped to historic lows. 30 Britain Banks’ return on equity % 20 10 0 U.S. -10 -20 1880 1900 1920 1940 1960 1980 2000 2011Source: Autonomous ResearchFigure 1 cognizant reports 3
  4. 4. Adopting Next-Generation Technologies platforms, asset types and regions to co-exist asto Enhance RDM Capabilities if they are physically residing on the same serverOrganizations realize that data management is — can help these firms overcome the challenge ofno longer a cost center but an important compo- centralizing reference data from multiple reposi-nent of revenue generation. Capital markets firms tories. Organizations can save on infrastruc-looking to improve their operational efficiencies ture costs by eliminating the need for physicaland flexibility need to understand that it can only centralization of data.9 This can also help com-be possible with a next generation of reference panies avoid data redundancies and fragmenteddata management capabilities that make use datasets and enable better lifecycle managementof social media, analytics and virtualization. In of reference data.addition, such capabilities can help firms improvetheir regulatory reporting, risk measurement and Overhauling RDM: Two Approachestrade strategies. Deciphering the impact of region-specific regula- tions on RDM operations is difficult for businessesWith markets-related news often breaking first that engage in cross-border, cross-asset trading.on social media, businesses can now deploy ana- Also, improving existing RDM infrastructure willlytics to decode, interpret and analyze the data require a significant investment. According toto feed their trading strategies in order to gain TABB Group, the financial industry’s 2011 globala first-mover edge over competitors.8 Traders spending on clearing and back-office technologycan also make effective decisions using powerful to comply with OTC derivatives regulations wasanalytics solutions that provide insights gleaned projected to reach $3.3 billion.10from thoroughly organized data repositories inuser-friendly visualization approaches, such as The prospect of operating in a prolonged era ofdashboards. low margins and high cost of operations is forcing organizations to seek innovative solutions. Busi-Organizations that are overwhelmed by opera- nesses will, therefore, need to focus on address-tions that span multiple assets and multiple ing inefficiencies and improving operations.regions require exceptional data managementstrategies and acumen to excel at risk manage- Even as capital markets firms deal with the inef-ment and enable the front office to make the most ficiencies of their siloed structures, the costof market opportunities. Creating a virtualization involved in RDM is rising rapidly (see Figure 3, nextlayer — software that enables data from multiple page).11 In 2011, data costs accounted for aboutImpact of Proposed New RegulationsRespondents indicated a significant adverse impact on costs and financial performance. It will increase our cost base 82% It will hamper our ability to introduce new products and services 53% It will adversely affect our financial performance 53%It will harm our overall competitiveness 44% It will divert management attention away from more pressing issues 42% It will make it more difficult for us to attract and retain customers 17% It will weaken our balance sheet 17% Other 6%Response base: 160 senior financial service executives from Western EuropeSource: Economist Intelligence UnitNote: Respondents were asked to select all options that applied to them.Figure 2 cognizant reports 4
  5. 5. $333 billion, roughly 83% of the $400 billion Optimizing data management also helps rational-spent on technology globally by the sector.12 Using ize data costs. It enables businesses to accuratelyinaccurate reference data leads to failed trades identify data requirements, consolidate them andand can cost firms millions of dollars in lost negotiate on scale with data vendors. Centralizingrevenue and financial liabilities. It can also lead to the procurement of data eliminates redundantmisrepresented corporate actions, high reconcilia- subscriptions, improves data management andtion costs, reduced efficiency, adverse effects on enables optimal utilization of data specialists,pre-transaction risk assessment and increased resulting in considerable cost savings. Organiza-costs of repairing failed trades. The absence of an tions can save 10% to 15% of addressable costsindustry-wide standard for LEI is a major impedi- for market data and exchange fees through selec-ment for firms in measuring and reporting risk. tive use of market data providers.13 They can also benefit from improved operations that produceBusinesses can cope with these challenges by high-quality data, eliminate data silos and enablecreating long- and short-term strategies: them to generate customized views of their exposure to risk.• Short-term strategy: Reengineer RDM and use an appropriate data management model Reengineering of RDM also paves the way for (centralized, federated or hybrid). developing “golden copies,” or single versions of• Long-term strategy: Develop a collaborative the truth, thus doing away with inconsistent data. cross-industry ecosystem that facilitates the It improves the quality and accuracy of the data delivery of RDM as service utilities. and reduces the refining required. It also enables consolidation of reference data from multiple ven-Reengineering RDM and Using an Appropriate dors and internal enrichment by business units.Data Management Model This consistent set of data is then disseminatedOrganizations must prepare their reference to business units, providing them with a completedata management systems for the future. As a picture of the risk profiles and underlying dynam-first step, they need an accurate understand- ics of financial instruments and their entities. Theing of their data requirements. Establishing an data also allows them to effectively manage theirenterprise data governance framework helps exposure to risk in various segments of the market.determine the lineage of data and identify boththe kinds of data being used and the patterns of Firms can meet the unique requirements of busi-usage. A data governance model can establish ness units with customized golden copies madeboundaries, hierarchies and ownership of data. available through various data managementThis, in turn, will allow capital markets firms to models suiting their business needs. Central-gather and distribute accurate and consistent ized, federated and hybrid models offer a rangedata to their business units. of alternatives that enable proper data ownershipMarket Data Spending Initiatives 2011Respondents’ top spending priority was new technologies to manage data of new asset classes and geographies. New technologies 48% Aite Group believes prioritization of new technologies Data feeds 39% is linked to the need New data from different for data that covers geographical regions 38% additional asset classes and Staffing: Business geographies. analysis/management 35% Data and applications for risk management 28% Data on new asset classes 24%Response Base: 34Source: Aite Group, 2011Figure 3 cognizant reports 5
  6. 6. and well-defined data governance appropriate to of other mature industries, such as the semi- the firm’s business needs. conductor business. An ideal solution for firms to operate effectively in such a scenario will be • Centralized model: In this approach, the ref- to tap utilities that are aided by a collaborative erence data is captured, maintained and dis- ecosystem formed by the capital markets players. tributed by a sharedOver the long term, service organization. Regulatory support can aid in establishing uni- capital markets This model is suitable versal standards for reference data. In addition firms will operate for large diversified banks with multiple to this support, it is critical to build a collab- orative ecosystem that ensures a level playing in environments offerings, as multiple field is created for all participants by eliminat- characterized by lines of business (LOBs) ing a multitude of data standards and providing lower revenues may clientservingImple- same be base. the players with a legal certainty to invest in sanc- tioned standards. This largely stable, standard- and decreasing menting such a model ized and commoditized reference data can margins, which requires political will in then be provided by RDM utilities to capital will exert greater complex be expensive and can organizations markets firms. As the reference data is produced and consumed by industry players, the ecosystem pressure on them and time-consuming. can provide a neutral ground and the required to lower costs. As a compromise, large business support to start building successful banks are increasingly RDM utilities. adopting the hybrid model. • Federated model: Here, a data “hub” is The utilities will own the sophisticated infrastruc- created to maintain limited data and cross- ture, people and processes to store and retrieve reference information. A separate shared complex reference data, effectively assuming services organization maintains this hub and the total costs and risks of RDM ownership. The publishes the required information to other utilities can charge on a pay-per-use basis, which (subscribing) applications as needed. This is more economical and allows firms to convert model suits large retail banks and mid-sized Cap-Ex into more manageable Op-Ex, a critical or small institutions with limited product offer- option given increasing margin and compliance ings, with different LOBs serving individual cli- pressures. These utilities would use industry- ent bases. accepted standards and embed standardized RDM • Hybrid model: This approach works with a processes built around industry best practices. specific set of reference data that is centrally managed by capture and distribution services, Reference data comprises “public reference data” although some business units continue to — data that does not provide competitive advan- capture and maintain reference data specific tage — and “private reference data” — proprietary to their needs. The data hub maintains cross- data, such as calculated prices and analytics data. reference information. This model is suitable These utilities must ensure that private data is for banks wishing to move to more central- protected and firms have full control over it and ized management of data, with different LOBs that public data is delivered at the lowest possible maintaining specialized reference data that is price point.14 not used by others. Consistent operations enable better execution, Developing a Collaborative Ecosystem to Foster reduce individual firm risk, support growth, cut RDM Utilities costs and improve customer experience. With the Over the long term, capital markets firms will entire RDM function managed by utilities, banks increasingly operate in environments character- can quickly respond to changing markets and ized by lower revenues and decreasing margins, regulations and spend less time expanding into which will exert greater pressure on them to other geographies and asset classes.15 lower costs. The burden of the costs and risks of ownership of the infrastructure will compel these The participation and contribution of all players firms to seek innovative solutions along the lines is essential for creating a utility that is global in cognizant reports 6
  7. 7. nature to support all types of trading activities with the right regulatory push and cooperation ofand lower the cost of reference data services. industry participants.The ecosystem and utilities can enable cost-effective access to new markets, technology and The Road Aheadinfrastructure. We believe as more and more The BFS industry continues to be characterizedcapital markets firms partner to build utilities by low margins, cost pressures, volatile marketsand subscribe to reference data services, road- and neglected back-office processes. Understand-blocks will be removed for increased uptake of ably, cost containment and efficient risk manage-utility services. ment will top firms’ strategic priorities, along with building RDM capabilities that will help themInside the ecosystem, firms should collaborate to improve their profitability in a radically changingaddress concerns over latency times, the culture operating environment.change involved in sourcing reference data froma utility and issues around ownership and shar- Capital markets firms can perhaps learn froming of legal risks. The ecosystem should strive to the experience of the semiconductor industry.improve the utility services to prepare and scale Companies quickly found that it was more effi-them to serve their needs in an ever-changing and cient to outsource the component fabricationvolatile market. Utilities should be able to serve to a specialized manufacturer and focus on chipplayers of all sizes and businesses by designing design. Similarly, capital markets firms facingfeasible models. For example, an ideal scenario relentless pressure on their profitability and mar-would be utilities offering standardized processes gins can move the management of commoditizedwith minimal switching costs. reference data to specialized utilities, while they focus on using the data to improve their business.An ecosystem must address key challenges, suchas getting the critical mass of players to build As more utilities significantly reduce costs,and buy into utilities, while providing effective capital markets firms will have sown the seeds ofparticipation incentives. Nevertheless, building a new model capable of meeting the demands ofRDM utilities is a feasible idea that will succeed the industrialization era.Footnotes The scenario in which financial firms embrace the rigors of manufacturing process excellence to opti-1 mize their low-margin and low-returns businesses by focusing resources on core functions that drive competitive advantage and tasking contextual activities to third-party experts.2 Finn Christensen and Kevin McPartland, “OTC Derivatives Clearing Technology: Bringing the Back Office to the Forefront,” TABB Group, September 2011, files/page/V09-031 OTC Clearing Tech.pdf.3 “Investing in Banks: The Not-for-Profit Sector,” The Economist, May 2012, node/21554193.4 “Global Capital Markets 2012: Tough Decisions and New Directions,” The Boston Consulting Group, April 2012, 80-104055.pdf.5 “Compliance and Competitiveness,” Economist Intelligence Unit, June 2011, upload/eb/EIU_Sybase_FS_regulation_Web_June_16.pdf.6 Ibid.7 Ibid. cognizant reports 7
  8. 8. 8 Social media platforms are faster than traditional media in disseminating news. They also provide leading indicator data, which asset managers, equity analysts and high frequency traders are using to their advantage. In addition, they are using social media data for sentiment analysis, as a "breaking news" stream for their investment decisions and trading strategies.9 Virtualization is helping firms across industries reduce data center costs through efficiency gains and improved server utilization. Citi, for example, reduced its 72 global data centers to 20 as part of its five-year data consolidation plan, increasing server and storage utilization from 10% to 60%. In another case, Metro Health reduced its data center infrastructure costs by 30%, using a virtualiza- tion solution from Cisco.10 “OTC Derivatives Clearing Technology: Bringing the Back Office to the Forefront,” Tabb Group.11 “Reference Data Acquisition Challenges: Getting it Right From the Start,” Informatica, April 2011, Howard Rubin, “Technology Economics: The ‘Cost of Data,’” SAS, October 2011, knowledge-exchange/risk/integrated-risk/technology-economics-the-cost-of-data.13 “Global Capital Markets 2012: Tough Decisions and New Directions,” The Boston Consulting Group, April 2012, tcm80-104055.pdf.14 To enable this, the utility platform must support a public access area and a private access area. A well-architected platform can ensure the right level of access control.15 Euroclear Bank and SmartStream have partnered to create a centralized reference data utility that will allow clients to choose the precise format in which they need securities data. The utility will validate, cleanse and enrich securities data, sourced from stock exchanges, central securities depositories, data originators and data vendors.References• E. Paul Rowady Jr., ”Reference Data Management: Unlocking Operational Efficiencies,” Tabb Group, May 2012,• “Analytics Special Report,” Inside Market Data, May 2012, k7rd/12872/17491/IMD_REPORT_28.05.12_Recognia.pdf.• James Rundle, “Running With the Rules,” Inside Reference Data, January 2012, http://www.water-• Francis Gross, “Micro-Data as a Necessary Infrastructure – Standardization of Reference Data on Instruments and Entities as a Starting Point: Need for a Reference Data Utility,” IFC Bulletin, No 34, Bank of International Settlements, November 2011,• Alan Grody, “The Data Challenge of Systemic Risk,” Inside Reference Data, October 2011, http://www.• “Reference Data Technology: Special Report,” Inside Reference Data, June 2011, http://www.water-• Melanie Rodier, “Data Management a Top Priority for Wall Street Firms,” Wall Street & Technology, June 2010, cognizant reports 8
  9. 9. CreditsAuthor and AnalystAala Santhosh Reddy, Senior Research Analyst, Cognizant Research CenterSubject Matter ExpertSudhir Gupta, Vice-President, Cognizant Banking & Financial ServicesDesignHarleen Bhatia, Creative DirectorSuresh Sambandhan, DesignerAbout CognizantCognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business processoutsourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquarteredin Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep in-dustry and business process expertise, and a global, collaborative workforce that embodies the future of work. Withover 50 delivery centers worldwide and approximately 150,400 employees as of September 30, 2012, Cognizant is amember of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among thetop performing and fastest growing companies in the world.Visit us online at for more information. World Headquarters European Headquarters India Operations Headquarters 500 Frank W. Burr Blvd. 1 Kingdom Street #5/535, Old Mahabalipuram Road Teaneck, NJ 07666 USA Paddington Central Okkiyam Pettai, Thoraipakkam Phone: +1 201 801 0233 London W2 6BD Chennai, 600 096 India Fax: +1 201 801 0243 Phone: +44 (0) 207 297 7600 Phone: +91 (0) 44 4209 6000 Toll Free: +1 888 937 3277 Fax: +44 (0) 207 121 0102 Fax: +91 (0) 44 4209 6060 Email: Email: Email:©­­ Copyright 2012, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by anymeans, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein issubject to change without notice. All other trademarks mentioned herein are the property of their respective owners.