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  • 1. The Case for Big Data in the Financial Services Industry WHITE PAPER Sponsored by: HP Mic hae l Vers ace Kar en Masse y Se p tember 2 012www.idc-fi.com IDC FINANCIAL INSIGHTS OPINION Big Data technologies and new analytic capabilities are the primary platforms for innovation across business lines in the financial services market today. Business analytics, applied to the disciplines ofF.508.988.6761 consumer engagement, relationship pricing, capital management, compliance, IT governance, security, and fraud management, is the core innovation platform for improving decision making. Analytics and the ability to efficiently and effectively exploit Big Data, advanced modeling, and in-memory and real-time decisioning across channelsP.508.620.5533 and operations will distinguish those that thrive in uncertain and uneven markets from those that fumble. IDC Financial Insights believes that Big Data trends, among others including risk and regulatory compliance, are driving analyticGlobal Headquarters: 5 Speen Street Framingham, MA 01701 USA investments. Big Data is seen as a years-long journey for financial organizations. This journey has begun with the current effort of organizations to address the requirements of their traditional data infrastructures, but it is expected to grow in complexity in tandem with the further maturation of Big Data technologies, better articulation of business potentials, identification of ideal use cases, and buy-in from decision makers. SITUATION OVERVIEW The Financial Services Environment The financial services industry is experiencing disruptive change throughout the world. The drivers of change include regulatory reforms still pending following recent failures, ailing consumer and business sentiment, the continuing economic crisis in some markets and rapid expansion in others, the availability and cost of new technology and business models, and industry consolidation. This disruptive change can be characterized by macro-level trends and strategies for IT investments. The macro-level trends include: September 2012, IDC Financial Insights #FIN236979
  • 2. ● Changing business models. Fueled by the factors noted previously, financial institutions find themselves in a market that is fundamentally different from the market of even three years ago. Successful financial institutions must be able to more quickly apply changes to and build agility into their business models or risk losing market share and the confidence of shareholders and customers.● Technology advancement. New technologies and the consumerization of technology are creating the foundation for a new platform of financial applications, evolving business models, and a core basis for change. For example, as the bank branch has evolved into the "alternative delivery channel," financial institutions are looking at new methods of collecting, consolidating, and consuming information to improve the way in which they engage with customers online and through mobile offerings.● Changing workforce and consumer. New business models and technologies require a different workforce, and financial institutions need to step up their efforts to attract, retain, and grow their base of knowledge workers. Without an infusion of a new generation of bankers, the industry will continue to face an uphill climb toward improved profitability.● Expanding consumer expectations, Apple, Google, Amazon, and other expanding content providers have redefined consumers expectations for digital banking services. Banks are struggling to act quickly enough to match or exceed this online experience due to ingrained business and technology silos, fragmented processes, and less than optimal data and content quality, making any customer interaction more painful than it needs to be.At the same time, the IT industry is ushering in disruptive 3rd Platformtechnologies of Big Data and analytics, cloud computing, socialbusiness, and mobility, as shown in Figure 1. Financial executivesunderstand that their IT infrastructure and services providers must besuccessful in embracing these new technologies and the digitalapplications they enable. If these IT providers are unsuccessful in theirefforts, financial firms risk losing traction in the continuous marchtoward competitive advantage.Page 2 #FIN236979 ©2012 IDC Financial Insights
  • 3. FIGURE 13rd Platform Financial ServicesSource: IDC Financial Insights, 2012As with most technology decisions, business priorities that call for theinvestigation and use of Big Data technologies are being influenced bya set of internal and external factors, including the need to satisfyregulatory and compliance requirements, improve product andrelationship margins, and optimize capital and liquidity amidcontinued economic instability and regulatory oversight around theworld. To help financial firms maintain a strong foothold in the market,these investments must deliver and promote tangible value bydemonstrating a higher degree of competitive advantage, strength inchannels, and improved agility of IT infrastructures and at the sametime promote operational efficiency and a strong corporate culture.©2012 IDC Financial Insights #FIN236979 Page 3
  • 4. Analytics Is Core to Successful CompetitionInformation is the currency of financial services competition. It is thefoundation upon which financial services firms compete. Traditionally,services are wrapped around data to differentiate products and services.For instance, knowing which customers represent the best creditrevenue opportunity to a bank is a question that only data and analyticscan solve. We believe that as an extension, Big Data and the advancedanalytics that are part and parcel to this trend are core to excellingcompetitively now. However, may firms struggle to uncover theuntapped value that exists by extracting meaning from all information— structured and unstructured, internal and external, private andshared — and translating this untapped value into customerengagement opportunities, products, and services.It is increasingly vital for firms to harness Big Data into insights thathelp inform actionable, optimized, and timely decisions; keep risks atanticipated and acceptable levels; and uncover opportunities to stayahead of the competition. For example:● The NYSE creates 1 terabyte of market and reference data per day covering the use and exchange of financial instruments. In comparison, Twitter feeds generate 8 terabytes of data per day (or 80MB per second) of social interactions.● 10,000 payment card transactions execute per second across the globe.● 210 billion electronic payments were generated worldwide in 2010. This number is expected to double by the end of the decade.● Between 2009 and 2014, the total number of U.S. online banking households will increase from 54 million to 66 million.● In 2012, 46% of financial services CIOs are exploring the potential of cloud computing, up from 33% in 2010.● Market data volumes grew 10x between 2007 and 2011 and are still growing strong.● Some of the top European insurers report a sixfold increase in the amount of data and analytic reporting required by just the first pillar of Solvency II insurance reform regulation.● IDC Financial Insights estimates that worldwide spending on core financial crime and fraud management solutions and infrastructure will top $28 billion in 2012, a growth rate of over 8% compared with 2011.Page 4 #FIN236979 ©2012 IDC Financial Insights
  • 5. Risk Management and RegulationRisk management and regulation is a primary external influencer ofBig Data and analytic trends. Increased regulatory uncertainty,regulatory pressures, and global business demands are also forcingfinancial services firms to rethink the value of the technologies, datamanagement, and business processes they use to operate effectively,compete, and manage risk. For example, the Basel Accord hasestablished new requirements for strengthening capital positions andfor managing counterparty risk exposures, placing new requirementson data management, analytics, and reporting functions of the chieffinancial and risk offices of the organization.As shown in Figure 2, regulation can have a drastic impact onoperating efficiency. According to IDC Financial Insights Risk OfficerSurvey, 67% of risk managers feel that increased regulatoryobligations and scrutiny have an unproductive impact on theiroperations.FIGURE 2Risk Managers Believe Increased Regulatory Mandates AreUnproductiveQ. What is your reaction to the following statement? I am concerned that an increase in regulatory queries is straining my compliance team and resources. Somewhat disagree (8.3%) Completely agree (41.7%) Neutral (25.0%) Agree (25.0%) n = 12Source: IDC Financial Insights Risk Officer Survey, 2010©2012 IDC Financial Insights #FIN236979 Page 5
  • 6. Economic and Financial Performance PressuresIn addition to technology and regulatory pressures, firms continue tobe squeezed by global economic instability, expectations to increaserevenue and reduce costs, and persistent pressure to optimize capitaland enhance customer engagement. The performance of key marketsand economies will continue to impact the financial performance offinancial services institutions. Given the current short-term forecasts,this means a continuing cost-conscious environment in establishedmarkets and a higher relative spend in emerging economies withuneven spending worldwide overall. Increasing revenue has becomechallenging for banks in particular as they seek ways to replacerevenue lost to regulations, including interchange and penalty fees.Why Big Data?Against this backdrop, what is Big Data and why is it so important tounderstand?Big Data describes a new generation of technologies and architecturesdesigned to economically extract value from very large volumes of awide variety of data by enabling high-velocity capture and discoveryand/or analysis at very low cost compared with earlier solutions.The term is generally applied, not so much to the data itself but to theclass of technologies and solutions that enable management, access,and analysis of much larger sets of data at a much faster rate than hasconventionally been possible.IDCs definition of Big Data encompasses hardware and software thatintegrate, organize, manage, and present data that is characterized bythe "four Vs" — volume, variety, velocity, and value.● Volume. The term Big Data alludes to massive volumes of data, but it is a relative term. The actual quantity will vary by case for each industry sector, organization, and application.● Variety. The variety of data sources is a critical attribute that is changing. The variety of data sources and the variety of data formats are key determinants of the challenges in storing, analyzing, and gaining actionable insights from the data.● Velocity. The speed at which data can be analyzed and delivered is another critical factor. Technologies that enable vast amounts of data to be analyzed in minutes rather than hours or days have a dramatic impact on the ability of organizations to respond to changes in the market, changing customer preferences, or evidence of fraud.Page 6 #FIN236979 ©2012 IDC Financial Insights
  • 7. ● Value. This refers to the cost of the technology and the value derived from the deployment of the technology. Of greatest consequence for the value of Big Data is what is different — in certain sectors, large data warehouses have existed, as have real- time data management and unstructured content analysis, but now a combination of open source software and decreasing hardware prices has made technologies only previously affordable to the largest government agencies or the largest companies in select industries more broadly available. It is important to understand that the consumerization of technology is putting in play vast amounts of new data. This data is becoming more important to products, services, and the underlying relationships banks keep with customers, partners, and regulators.A challenge for financial firms will be to balance Big Data investmentstrategies with what is needed to better manage risk and what isneeded for innovation in products, services, and operations. As shownin Table 1, although risk and innovation are somewhat competinginterests, the use cases being implemented in the early stages of BigData deployments are monetizing the value of Big Data on both endsof the spectrum by creating new business insights through thecollection, consolidation, and consumption of data in the form of:● Account, transactional, and historical data● Market, trade reference, and financial data● Risk and finance data● Lifestyle-related information, spending patterns, neighborhoods● Behavioral and tendency data● Demographics● Geospatial information and location intelligence● Online and social media interactions● Mobile usage trends, activities, friends, and likes and interests©2012 IDC Financial Insights #FIN236979 Page 7
  • 8. TABLE 1 Big Data Use Cases: 2012 Examples Case Achievements European hedge fund In-memory analytics to optimize price discovery and investment strategies for large portfolio trades and swaps Global investment bank Common operational data stores to speed post-trade settlement, confirmation, and access to common data Retail banking innovation leader Using geolocation data to create merchant intelligence and assist in optimizing offers and pricing to retail customers; driving mobile banking growth Asia/Pacific national bank Tracking social media into finely tuned market campaigns Expanding U.S. property insurer Granular microtargeting of customer segments and individuals with specialized pricing based on historical risk performance and forecast data Global European institution Using social analytics to gauge sentiment toward key product and service initiatives Investment research institution Using data from private sector satellite companies to understand traffic patterns and parking lots and fill rates of major retailers; fill rates and trends inform investment advice Community bank Analyzing transactional and unstructured data (voice) collected to anticipate workloads and staffing needs in call centers and branches Source: IDC Financial Insights, 2012FUTURE OUTLOOKThe financial services sector is in the very early stages of the Big Datainitiative. Big Data is seen as a years-long, iterative journey forfinancial institutions. For many, the journey begins from the currenteffort to improve traditional data infrastructures as they addressexisting major programs of work — usually associated with customerdata management, risk, multichannel effectiveness, and mobility.These programs may not specifically focus on the implementation ofBig Data technologies and approaches immediately, but there areexpectations that the programs will evolve further and lead to follow-on Big Data deployments in the mid- to long term.Clearly, the sector is beginning to build out road maps of where BigData could deliver the most value within this broader set of technologyinvestments, but many financial services firms are cautious aboutmaking broad-based investments in these new and relatively nascentBig Data tools.Page 8 #FIN236979 ©2012 IDC Financial Insights
  • 9. At the end of 2011, IDC Financial Insights forecast that over 40% of alltier 1 firms would gear up to execute Big Data/analytics business andtechnology strategies in 2012. Today, this forecast appears conservative,as a more recent IDC Financial Insights study shows that:● Almost 48% of firms surveyed have implemented, are implementing, or are evaluating technologies such as Hadoop and MapReduce.● Over 21% of respondents plan to invest over $1 million in their Big Data strategies in 2012, with a small percentage of firms (less than 4%) planning to invest over $100 million.IDC Financial Insights expects that over the next three years, there willbe notable and significant progress toward Big Data deploymentswithin financial services. Furthermore, use cases will be articulatedmore thoroughly and new partnerships will begin to emerge betweeninstitutions and leading Big Data partners.However, IDC Financial Insights does not expect large-scale "one sizefits all" deployments. Instead, institutions will use an incremental anditerative approach over the next few years, building Big Datatechnologies, practices, and standards in to existing projects whilebuilding in parallel new core competencies to exploit new capabilitiessuch as context- and pattern-based analytics.Overview of HP Big Data OfferingsHP offers a wide range of information management and analytics(IM&A) capabilities drawn from its enterprise services, softwareproducts, and cloud and security platforms to address Big Datachallenges and opportunities in the financial services sector.HP IM&A services include information strategy and organization,information management and architecture, business analytics andinformation delivery, and social intelligence. HP provides theseconsulting services around its own Big Data software assets,Autonomy and Vertica, and third-party software assets, in particularSAP HANA and Microsoft SharePoint/BI platform. HP supports thiswith a variety of software and solution delivery models including on-premise installation, hosted, software as a service (SaaS), cloudcomputing, and multitenant SaaS (cloud deployment).HP focuses its capabilities to enable financial services firms toproactively manage information-related business risk, enhancecustomer experiences, and optimize business performance to maintainreliability, reduce the costs of operation, and protect revenue.HPs Autonomy software asset helps financial services firms developconnected intelligence from structured and unstructured data foractionable decisions that improve business performance.©2012 IDC Financial Insights #FIN236979 Page 9
  • 10. HPs Vertica analytics database delivers scalable performance on BigData queries enabling real-time decision making to be embedded infinancial services processes in order to optimize business performance.Vertica and Autonomy deliver a powerful combination for real-timeanalytics and decision making using structured and unstructured dataacross the enterprise.Strengths and ChallengesHP offers a product and services portfolio that is consistent with whatIDC Financial Insights expects from a market-leading technologyprovider to the financial services industry. In addition to the softwareand services capabilities noted previously, HP provides the followinginfrastructure components to enable Big Data applicationdeployments:● Cloud computing. HP can offer clients a variety of enterprise- grade cloud computing solutions, including public, hybrid, and private. The ability to offer private cloud to financial services firms is considered a strength for a number of reasons, not the least of which is a tighter security model.● Security. HP offers clients a security strategy through the HP Security Framework, designed to offer end-to-end information security plans and execution road maps. Because of the sensitive nature of customer data and the requirements and penalties imposed by the regulators, security is top of mind for industry IT executives.● Mobility. HPs approach to enabling enterprise mobility is suited for organizations that wish to reach their constituents across multiple networks and devices by delivering applications, content, and services in a scalable, secure, and reliable way. This approach leverages HPs global applications services capabilities to provide the architecture, systems engineering, development, and support services. Combined, they help an organization simplify its applications and extend them where necessary as well as build mobile business-to-business, business-to-consumer, and business- to-employee applications. This approach also leverages HPs service-oriented architecture–based integration architecture and is enabled by development and security frameworks that help create componentized building blocks from monolithic legacy applications to develop and deploy mobile applications.Page 10 #FIN236979 ©2012 IDC Financial Insights
  • 11. HP faces several unique market challenges as well as many of thesame market challenges as other enterprise vendors servicing the BigData marketplace:● Demonstrating leadership in the transition from information management to Big Data. HPs legacy information management solutions, including TRIM, Data Protect, and others, are not broadly implemented across the financial marketplace. HP must more effectively demonstrate leading capabilities at the component level with high-profile Autonomy and Vertica wins as well as highlight its ability to deliver end-to-end Big Data software, services, and hardware with lighthouse and marquee clients.● Offering competitive, differentiated solutions portfolios. The financial services market, as a traditional early adopter of emerging technologies, senses the value of the Big Data trend and is budgeting accordingly. HPs competitors are also focused on expanding their solution portfolios on the Big Data trend in terms of breadth and depth of product capabilities, IT infrastructure, and professional services. HP must leverage its industry position and demonstrate the value of a Big Data relationship with HP to gain Big Data market share.● Channel network management. HPs channel strength is also a weakness. Successful execution of its Big Data strategy to broaden its portfolio will require that HP reinforce its position in the enterprise space without alienating the channel that has been so beneficial to the organization.ESSENTIAL GUIDANCEIDC Financial Insights offers the following guidance to end users fordefining their Big Data requirements.● Recognize that Big Data is not solely a technology issue. Although significant new technologies, including social media, mobile, cloud, analytics, and Big Data technology itself, are ushering in a new platform for future financial applications, financial executives must recognize that Big Data is not just an issue of technology. Instead, it is more about how to use the growing velocity, variety, and volume of information to make material changes and improvements in the way financial enterprises interact with customers, partners, regulators, and employees; manage risk; and run efficient operations.● Plan for a journey, not a project. The Big Data journey is unique to each organization, and it is dependent on many factors, including the maturity of its current data infrastructure, the type of business, the availability of skills within the organization, and so forth.©2012 IDC Financial Insights #FIN236979 Page 11
  • 12. A key challenge for the IT executive is how to prioritize Big Data projects — where Big Data technologies will be of most use and benefit and where they will have the highest chance of success. This will most likely require a balancing act that will straddle regulatory compliance, risk management, channel optimization, operations, and customer experience.● Demonstrate incremental progress. Big Data deployments need to start small and build out incrementally. Programs should be incorporated into "business as usual" activities and be part of an overall data management and analysis road map designed to deliver against a set of well-defined objectives.● Dont let Big Data become a big liability. The intersections between Big Data and traditional data, IT, and corporate governance programs are unclear and will most likely be defined over the next two years through industry best practices, standards, and regulatory guidance. During initial pilots and proof of concepts, financial executives must engage in discussions on a number of important governance issues, including the impact on personal and corporate privacy policy and ownership protection when external data is onboarded; the ability to scale existing information protection and security systems to maintain the confidentiality, integrity, and availability of Big Data; and establishing Big Data inventory and classification schemes and standards for corporate use.● Build Big Data competencies within an analytics strategy. Analytics excellence is core to innovation across the financial industry. Business executives in the financial industry must view analytics and the ability to efficiently and effectively exploit Big Data, advanced modeling, and real-time decisioning across channels and operations as an important capability that will ultimately distinguish those that thrive in uncertain and uneven markets from those that fumble.● Establish new competencies. Banks seeking to deploy new Big Data tools must have a strategy in place to ensure that the right skills are available to manage these deployments. Traditional IT shops typically dont have the capability to support Big Data tools, traditional reporting tools are just starting to plug into these new tools, and the new tools are also new for IT people in the bank. Bootstrapping Big Data solutions into an enterprise IT landscape will present some challenges. There are a number of different approaches to this problem. In the short and midterm, the best approach may be to look for Big Data capabilities through services arrangements from onshore and offshore service providers.Page 12 #FIN236979 ©2012 IDC Financial Insights
  • 13. Copyright NoticeCopyright 2012 IDC Financial Insights. Reproduction without writtenpermission is completely forbidden. External Publication of IDCFinancial Insights Information and Data: Any IDC Financial Insightsinformation that is to be used in advertising, press releases, orpromotional materials requires prior written approval from theappropriate IDC Financial Insights Vice President. A draft of theproposed document should accompany any such request. IDCFinancial Insights reserves the right to deny approval of external usagefor any reason.This document was reprinted by HP with permission from IDCFinancial Insights.©2012 IDC Financial Insights #FIN236979 Page 13