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Best practise in data management


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Best practise in data management

  1. 1. Best Practices in Data ManagementMeeting the Goal of an Enterprise Risk Management Platform WHITE PAPER
  2. 2. SAS White PaperTable of ContentsExecutive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . .A Unified Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3The Metadata Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Auditing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6A Common Security Framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7A Common Set of Native Access Engines . . . . . . . . . . . . . . . . . . . . . . 8Data Quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Planning for a Data Management System . . . . . . . . . . . . . . . . . . . . . . 9Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13The content provider for this paper was Antonis Miniotis, Risk Product Manager,SAS Global Risk Practice.
  3. 3. Best Practices in Data ManagementExecutive SummaryEver since enterprise risk management (ERM) systems first came to the forefront in themid-1990s, financial institutions worldwide have been grappling with ERM principlesand their viable implementation. In general, an ERM system should capture and analyzeinformation across all lines of business on multiple topics (e.g., exposures, limits onproduct lines and business units, etc.), provide analytic capabilities (e.g., concentrationanalysis, stress testing, VaR calculations, etc.) and produce reports for a variety ofinternal and external constituents. It is now increasingly clear that data should be treatedas an asset; not doing so can negatively affect a firm’s bottom line. For more informationon this topic, see the SAS white paper Data as a Board-Level Issue: Effective RiskManagement and Its Dependence on Accurate, High-Quality Data.With constant performance pressures and changing regulatory demands, banks arebeing forced to collect more data and dig deeper into their databases in order to refinetheir analyses. As a result, banks are taking a closer look at their data managementprocesses across the enterprise, from the back office to the front end.Data management is often cited as one of the major challenges that banks face whenimplementing risk management systems. Data is very much the lifeline of banks,andyet they still struggle to sufficiently analyze growing volumes of available data.According to a 2008 report from the Economist Intelligence Unit (EIU),1 lack of relevantdata was seen as hampering financial services firms’ approaches to risk management.Nearly one-half (44 percent) of executives questioned in that survey considered qualityof information to be one of the three major challenges in implementing enterprise riskmanagement. Those assertions are as true today as they were in 2008, and we knowthat the need for a comprehensive data infrastructure increases with the complexityof the risks and the size of the organization. A 2011 EIU survey on enterprise riskmanagement confirmed that the data issue remains high on the agenda as a barrierto effective risk management.Figure 1: The main challenges in risk.21 Economist Intelligence Unit. The Bigger Picture: Enterprise Risk Management in Financial Services Organizations. September 2008. Economist Intelligence Unit. Too Good to Fail? New Challenges for Risk Management in Financial Services. 1 June 2011.
  4. 4. SAS White PaperIndeed, data management is – and will remain – a critical component of an ERMsystem, and a bank’s approach to data management should be holistic and unified,as data silos stymie the flow of information and prevent employees and managers fromseeing a complete picture. For example, when a financial institution defines its risk profileand associated risk limits at the organizational, business and product levels, the valuederived from those limits depends on whether they can be accessed seamlessly bycorporate, retail investment banking business units and corporate risk management,which monitors adherence to the institution’s risk appetite (see the SAS white paperThe Art of Balancing Risk and Reward: The Role of the Board in Setting, Implementingand Monitoring Risk Appetite3). This example, however, isn’t just about the efficientand uninhibited flow of data among the various operational lines of business (LOBs),customer-facing desks (e.g., capital market front-office trading rooms and retailbanking branch offices) and corporate risk management. Rather, the focus is on theassurance that:• The data is structured according to a specific set of business rules.• Data quality has been evaluated and reported on against these rules.• Data from disparate sources is grouped together in a form that suits analysts The key ERM issue for many and information consumers. banks is to get enriched data• Different users have the same view of the same type of data (e.g., a single view in a single place so they can of the customer, standardized exposure representation, companywide performance reports based on specific measures such as RAROC, etc.). actually report on it.For these assurances to be possible, an institution’s data management platform mustbe scalable, open and capable of supporting the needs of multiple risk managementfunctions (e.g., market risk, operational risk, credit risk, etc.) and applications (e.g.,exposure monitoring, VaR calculation, loss-event collection, RAROC reporting, reportingon adherence to KPIs, etc.), and should have the following characteristics:• A unified data model that is both flexible and extensible in order to meet the evolving business needs of a vibrant financial organization.• A common metadata framework based on an open industry standard.• A common security framework.• A common set of native access engines to the various possible source systems (transaction systems, limits management systems, netting systems, etc.).• Common data quality capabilities.This paper will discuss each component both separately and together in order todemonstrate the benefits of the structure as a whole.3 SAS Institute. The Art of Balancing Risk and Reward: The Role of the Board in Setting, Implementing and Monitoring Risk Appetite. December 2011.
  5. 5. Best Practices in Data ManagementA Unified Data ModelThe lifeline of any system that is designed to derive valuable business information anddraw pertinent conclusions from processed data is the data itself, and that most certainlyis true for banks. In fact, the structure of that data is of critical importance to the viabilityof an ERM system.Figure 2: Enterprise risk management architecture.A data model that caters to the needs of an ERM system should not only include all theelements of ERM – e.g., exposures, mitigants and their relationships with exposures,counterparty and customer types, financial instruments, events and losses, marketand credit risk factors, etc. – but should also be capable of extending these elementsaccording to an organization’s particular needs. That is because a data model is anevolving entity that should be modified to reflect the changes that take place in theelements and business issues that affect the ERM platform.The data model should include tools that allow the business to analyze gaps and/orchange requirements against the evolving needs of ERM. These tools should enablethe business to analyze the impact of both changing existing items in the data model oradding new ones to it, either upstream or downstream in the ETL flows that use an entitythat has been earmarked for a change.The data model should consistently represent different types of data – e.g., sourcesystem codes, dates, regulatory exposure types, counterparties, etc. As such, the datamodel should include a thorough data dictionary that includes the rules and formats forevery type of field stored in the data model. 3
  6. 6. SAS White PaperIt should allow for the management of fast or slowly changing dimensions without anyloss of history where appropriate. For example, the probability of default (PD) rating ofa counterparty is a slowly changing attribute, and storing changes to this attribute isimportant in order for comparison calculations to be run and ratings migrations to beprocessed. An example of a fast changing attribute are the margin requirements on afutures transaction, which are evaluated every day.It is best to codesign the ETL flows that will populate the data tables of the ERMplatform together with the data model in order to ensure coordination and integrationof what is processed and how. Therefore, the data model is best defined in a stratifiedmanner, where there are two layers of data structure.A global ERM data model that incorporates data items for all sources of risk should beat the highest level. This global data hub would feed the data to the respective sublevelsof the ERM system. For example, information on counterparties and exposures would A data model is an evolvingbe stored for all sources of risk; however, within each subunit of the ERM system (i.e., entity that should be modifiedmarket risk, credit risk, etc.), these pieces of information would be processed differently to reflect the changes thatbecause they address different forms of risk. take place in the elements andTo reiterate, the design should follow a stratified structure, with a global data hub that business issues that affect thefacilitates the population and interchange of data among the various subunits of the ERM platform.ERM system (i.e., the market, credit and operational risk units). Such a design enablesyou to fix the ETL flows that populate the data marts that pertain to each subunit, soyou only have to worry about bringing the data into the global data hub. Once the datais in there, you can quite easily and transparently move it to any respective data mart(e.g., the credit risk data mart). In doing so, you isolate the issue of managing yourERM data from the issue of making changes in data that take place at the source-levelsystem because you only need to address these changes at the initial stage – that is, inpopulating the global data hub.The Metadata FrameworkMetadata – information about the data itself – is usually classified as one of thefollowing types:• Physical metadata, which describes the structures and properties of the data (e.g., field descriptions for a DBMS table).• Business metadata, which describes the physical data using representations that are familiar to the information users (e.g., in physical data, the client age could be defined as “Age_Client;” however, for reporting purposes, this field would be described in the metadata as “Current Age of Client.” The latter is the terminology that business users understand, as it directly represents the way they identify parts of their business.).• Application metadata, which describes the properties of an object that was created using an application (e.g., an internal ratings model).4
  7. 7. Best Practices in Data ManagementGiven that metadata is information about the underlying data and applicationobjects, the importance of metadata in the context of an ERM system is clear. All theapplications that constitute an ERM system must have access to the complete pictureof a data item so it can be processed as required and correctly presented in the nextapplication in the flow.Without this very fundamental capability, there would be breaks in the flow, whichwould require the translation and transport of data from one application to the next. Forexample, if there were one common set of metadata, the economic capital calculationengine would read and process the same data that a performance managementapplication would later use to report on KPIs and KRIs. Without common metadata, oneapplication may process the data one way, while another may read the data differently.As another example, take the use of metadata on a ratings modeling and risk-weightedassets (RWA) process. A quantitative analyst would define an internal ratings modelusing the relevant application, which would produce metadata about the model inputsand outputs, the model PD categories, etc. Where both applications used a commonmetadata framework based on open industry standards, such as the commonwarehouse metamodel of the OMG, the ratings model metadata would be accessibleto the engine that performs RWA calculations, reducing the risk of errors in thecalculations.A common metadata framework should include both a structure and dictionary ofproperties for each object. The structure should adhere to rules governing groups ofmetadata objects, parent-child relationships, inheritance and data integrity:• Groups of metadata objects: A group of metadata could be a set of metadata structures that describes a DBMS table.• Parent-child relationships: A parent-child relationship is the relationship that exists between a supertype and a subordinate. For example, a data field is a child of its parent, the DBMS table.• Inheritance: Inheritance defines how a child can carry the properties of the parent object, including the access rights. For example, a set of access rights could be Enterprise risk intelligence identified for a DBMS table (the parent object). The metadata framework should data needed to drive an ERM include rules that specify how a DBMS table column (the child) could inherit – if at platform goes beyond that all – the access rights from the parent. maintained in a typical data• Data integrity: Similar to the data integrity of a DBMS schema, data integrity in terms of metadata pertains to all those rules that control the values associated warehouse. with a metadata item and how an item relates to other metadata items in the same group or in a parent-child relationship. For example, the metadata for identifying application users could stipulate that each metadata identity is associated with a single first and last name. 5
  8. 8. SAS White PaperIn addition to identifying the metadata framework’s structure and the rules forstoring information within it, you must also identify the mechanism for exchangingthis metadata information among applications. An open metadata frameworkrequires the implementation of an open industry standard – such as XML – as theinterchange mechanism. This facilitates the exchange of information not only amongthe components of the ERM platform, but also among other parts of the business andexternal regulatory bodies.AuditingA common metadata framework serves another important purpose – auditing. Internal A data management platformauditors and external regulatory authorities require banks to provide detailed information with an underlying metadataon the flow of data, calculation results and reports throughout the business flows. Forexample, to qualify for IRB in either market risk or credit risk, a bank must demonstrate framework that stores metadatain a coherent way how the data flows from the data sources to the analytical for all data, application objectsapplications, and how the quantitative models process this information and generate and reports is vital to an ERMthe results. system.Regulators want to see how model parameters are managed, how risk factordistributions are modeled, how models are tested/validated, and how they areused in daily business (e.g., how a point-in-time ratings model uses obligor-specificinformation to assign obligors to risk categories and how unstressed PDs are measuredand modeled). That is why it is vital to have a data management platform with anunderlying metadata framework that stores metadata for all data, application objectsand reports.Metadata is stored in repositories like any other type of data and in structures that areconducive to fast querying and avoidance of unnecessary duplication. The structureshould be able to associate repositories and enable the easy, transparent exchange andsharing of information among repositories.To facilitate the sharing of metadata, it makes good business sense to allow theinheritance of metadata definitions from one repository to another (or more) in order toavoid duplication. Deduplication and sharing enable administrators to better managethe metadata by allowing them to collaborate with the business users to identify a setof global metadata that any application or user of ERM data must have. For example,users that manage the ETL flows would need to have access to the servers wherethe input data would be processed. These server definitions could form a set of globalmetadata that the metadata repositories of the various subgroups of ERM (e.g., marketrisk, credit risk, operational risk) would inherit.6
  9. 9. Best Practices in Data ManagementA Common Security FrameworkThe metadata layer allows various personas or business user types to view a singleversion of the truth when it comes to available data, reports and application objects.However, every organization should streamline access rights to the data and itsderivatives (i.e., reports, performance diagrams, quantitative models, etc.) in order toavoid both intentional and erroneous mismanagement of these resources, which areamong the most important assets of an ERM system.As such, the data management platform should be managed centrally (rememberthat the elimination of isolated silos is one of the most important hallmarks of an ERMframework). It should also allow for auditing and reporting on access patterns, accessrights and the flow of data through ERM processes.To facilitate auditing and reporting, security should be metadata-driven. After all, auditingis a fundamental part of most regulatory initiatives, and regulators and internal auditorsalike want to see not only how data is processed, but also how it is accessed – in otherwords, who does what, when and how.Data management security should address the issues of authentication andauthorization. Authentication involves the acknowledgement of users as they attemptto log on to the data management platform. Typically, this is facilitated by an applicationserver or operating system authentication mechanisms, and via LDAP or Active Server. Every organization shouldThe security platform should be flexible enough to support these mechanisms, enabling streamline access rights to thethe business to leverage investments in existing security frameworks. data and its derivatives in orderThe concept of authorization is ERM-specific because it determines what set of to avoid both intentional andactions an authenticated user is allowed to perform on a particular data resource (orany other application resource, for that matter), commensurate with the particular erroneous mismanagement ofbanking environment. these resources.The security framework should accommodate various forms of access controls:• Direct access controls specify which users and/or groups are allowed to access a particular object (e.g., a DBMS table, a remote server, a quantitative model, an ETL flow, a KPI report, etc.) and in what way(s).• Direct access control templates are defined for a particular user and/or group and are associated with access to a particular resource, such as the ones described in the previous bullet point.• Inherited access controls determine access to a resource by the access controls for its parent. For example, access to an ETL flow could be determined by the access rights to the group of ETL flows that encompass this particular flow, or access to a DBMS table could be determined by the access rights definitions that were defined for its parent DBMS schema.The data management platform should generate ample user access logs. These logsshould be in a user-friendly format and available to internal and external auditors at will.In addition, such logs should be readily available to the ERM platform’s querying andreporting tools to enable the efficient preparation of security/access reports. 7
  10. 10. SAS White PaperA Common Set of Native Access EnginesIt is very important for an ERM data management system to include an array of nativeaccess engines to other data sources. Only then can an ERM system be truly globalin its scope and application. We stress the words “native access engine” in order todifferentiate these engines from those that are generic (e.g., ODBC). A native accessengine is designed to access and employ all the functionality of the particular DBMSoptimizer that needs to be accessed. That includes crucial functionality in indexprocessing, fast loads and writes, multithreaded partition capabilities (wherever available)and optimized SQL queries processed at the source itself. Native access becomes evenmore crucial when access to both the metadata of the ERP system and its underlyingdata structures are required.It is important for these engines to be flexible in the way they allow a user to interact withthe source data (which can be an RDBMS, ERP, legacy system, etc.). That means thatthey should enable the user to submit SQL scripts in the native form understood by theinput data source optimizer directly, or be capable of translating the user’s SQL to theform understood by the input source.Through better data quality, reports would improve – variances could be explained andcould increase revenue because they’d have much better information and more currentinfo for more timely decisions. An ERM data management system must include an array of native access engines toData Quality other data sources in order toData quality is paramount for any system that operates for the sole purpose of be truly global in its scope andproducing valuable business information. No financial organization can ever be surethat its economic and/or regulatory capital calculations are accurate and reliable if the application.supporting data is not cleansed and validated according to defined business rules.Furthermore, data quality must be an integral part of the process not only to ensure thefast, seamless processing of the data, but also to satisfy the auditing requirements ofregulators and independent auditors.Because of its importance, a detailed description of data quality is beyond the scopeof this paper. But at minimum, any ERM data management system should be able toperform these basic functions:• Data profiling.• Business rule creation and application for imputing missing data.• Business rule creation and application for standardizing and validating column values against a knowledge base.• Deduplication.8
  11. 11. Best Practices in Data ManagementPlanning for a Data Management SystemAn ERM system should be able to process and integrate information from many sourcesthat fall into one of three main categories – front-, middle- and back-office systems.• Front-end systems – Includes investment banking trading systems used by Better data quality would traders to track positions and price and execute deals, and retail banking origination systems used by loan officers. improve reports, explain• Middle-office systems – Used by the ERM unit to measure and monitor risk variances and lead to increased exposure at various levels – from individual business units up to group level. LOBs revenue, as banks would have (retail, corporate, investment banking) are compared with risk concentration limits better, more current information (as defined against particular portfolio segments) and flagged as needed. for more timely decisions.• Back-office systems – Where trade settlements take place and information is passed to accounting for crediting and debiting, as required by each transaction.Even though these categories serve different purposes, there are overlapping activitiesthat dictate the need for overlapping data access. For example, traders need real-timeaccess to information on limits, and they must also perform some form of marginal orincremental VaR calculations to assess a position’s contribution to their portfolios. Forthe retail side, the single environment would support behavioral scoring, regulatoryreporting and risk management best practices (simulation-based economic capitalcalculations). From an ERM perspective, all three systems would interact (see Figure 3). Market Data • Bloomberg Current Data • Reuters Configuration Data Trading Systems • Fixed Income • FX • Equities • Derivatives Data Marts (e.g. Front Office Credit Risk etc.) Positions/Exposures ERM Global Repository, Limits Data Hub Default Data Risk Engine-Middle Office Internal/External Ratings Nettings, Collateral (physical, financial, guarantees etc.) Customer/ Counterparty data Back Office Operational Risk Reports Event-Loss Self- • Internal Assessment Data • Regulatory Report • Market RepositoryFigure 3: System interaction in an ERM environment. 9
  12. 12. SAS White PaperThe parameters that constitute configuration data are static throughout the process andare used as inputs in modeling, economic capital and/or regulatory capital calculations.In a regulatory environment, such data includes: supervisory haircuts, add-ons andCCFs, the confidence level and time horizon for VaR calculations, supervisory riskweights, etc.Figure 3 shows how the data management process moves data through thefollowing steps:1. Data requirements and gap analysis. Business users from various functions (e.g., quantitative analysts that model VaR, internal auditors that want to check and validate the process, state supervisors making regulatory demands to check the validity of the calculations, etc.) have identified their data needs. The data management team can then evaluate the gaps, if any, in the source systems.2. Mapping inputs to outputs. Once any gaps are identified and substitute data tables/fields have been agreed upon with the business users, the data management team can begin mapping inputs to outputs. In this example, the outputs are the tables found in the data model of the ERM data hub.3. ETL process construction. Next, the ETL designers begin constructing the ETL processes that will move data from the source systems to the ERM data hub. ETL flows must be tested, validated and put into production. Flows can then be moved to the scheduler. This is done on the basis of data dependencies. For example, to perform regulatory capital calculations, you must first load parameters (such as haircuts, add-ons, etc.) and the MtM positions of the portfolio. In this example, the start of regulatory capital calculation flows should be dependent on the completion of the other two flows. This, of course, implies the need to integrate checks and balances into the scheduler to ensure the execution of flows according to business specifications.4. Transfer of data to the data marts. Once the data is loaded on the ERM data hub, it can then be transferred to the data marts using predefined ETL flows. Because you designed the ERM data hub and data marts, you also have control over the structure of the ETL flows, making this part of the process easier.10
  13. 13. Best Practices in Data Management5. Analysis and modeling. Once the data is loaded into the data marts, analysts can begin analyzing and modeling the data. The risk engine should be flexible not only in terms of what models it allows you to implement (e.g., EC using credit migration or contingent claims; VaR calculations using delta-normal, historical, MC simulation, etc.), but also in permitting the storage and batch or real-time submission of the scripted version of the modeling work. Once the analysts have finalized their modeling work, these scripts can be loaded into the scheduler to enable the submission of the entire process, end-to-end or in parts, as required. The scheduling of an ETL flow depends on how frequently data in the source systems change. For example, a financial account used as collateral for a loan will incur changes almost daily in terms of its balance. Another example would be the calculation of undrawn amounts against facilities. Customer information, however, does not change daily, at least in terms of demographic information.6. Report and output data generation. Report and output data generation processes are necessary post-processes to the risk engine scripts. Here again, the data management function of the ERM system will play the crucial role of The data management defining the tables and columns of the report repository. The data management subsystem should system should cater to multinational organizations and be able to generate reports in various structures and languages. Therefore, the data management and accommodate the reporting reporting subsystems must be able to work jointly, sharing metadata and data. requirements of the various Reports can be published in a portal that authorized users can access. user types throughout theAs previously mentioned, the data management subsystem should accommodate organization.the reporting requirements of the various “personas” or user types throughout theorganization, as demonstrated in the table below. Scope of Frequency of Personas Reports Report Content Format Updates Upper Groupwide Group KPIs/ Focus on VaR Management KRIs, group strategic goals measures performance as defined daily; others against risk in company can be appetite, policies and weekly, aggregate on scenarios monthly or positions & VaR. or stress tests quarterly. against critical risk factors. Risk Groupwide, Group KPIs/ Aggregate Daily, Management business KRIs, group OLAP type intraday. units, banking performance presentation & trading against risk over various books appetite, hierarchies aggregate along with positions & VaR. detailed model validation, back-testing, scenarios or stress testing. Trading Desk Trading Trading desk Limits alerts, Real time, Staff books as per KPIs/KRIs, trading marginal or end of day. trading desk desk performance incremental against risk VaR, MtM of appetite, portfolios, aggregate hedging positions & VaR. information. 11
  14. 14. SAS White PaperThe data management system should maintain historical data in terms of the data usedby the risk engine and the reporting repository. Historical data is vital for back-testing,historical simulations and comparison of calculation/analysis results between differenttime frames.The data management system should generate ample logs and trace informationthroughout the flow of data from the point of extraction from the source systems all theway to report generation. This way, regulators and internal auditors can validate the pathof the data, and internal developers can use this log information to iteratively improveprocess capacity.In summary, the ERM data management system should be capable of addressing theflows of data to business needs in the real sense of the day, as illustrated in Figure 4. Positions Market Data Risk Factors/Correlations Expected Returns/KPIs MtM of Risk Accrual/Trading Positions/ Measurement/ Expectations Reporting Performance Optimization Balance Sheet Economic Values VaR, EC, etc. Portfolio OptimizationFigure 4: Flow of data to business needs.12
  15. 15. Best Practices in Data ManagementConclusionFinancial institutions are continuously seeking the ability to make better decisions– supported by data – faster. However, because most banks spend so much timegathering and cleaning data and creating reports, there’s little time left to explore datafor insights that can have a positive impact on the bottom line.That’s why a holistic, unified approach to data management – one that ensures asmooth flow of information throughout the organization – is a critical part of a true ERMsystem.Using SAS® for data management enables decision makers at all levels to see acomplete picture of enterprise risk. SAS Data Management supports the needs ofmultiple risk management functions and applications by providing:• A scalable, open platform.• A unified data model that is both flexible and extensible.• Common metadata and security frameworks.• A common set of native access engines.• Embedded data quality capabilities.SAS integrates quality control and automates the entire data management process –from data collection and aggregation to data validation and cleansing – to ensure dataaccuracy and consistency in a unified, quantitative risk management framework.Using SAS to manage risk at the enterprise level enables decision makers to gleankey insights from data and then combine those insights with information from otherfunctions (e.g., marketing, finance, HR) to facilitate true enterprisewide risk-basedperformance management. 13
  16. 16. About SASSAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market.Through innovative solutions, SAS helps customers at more than 55,000 sites improve performance and deliver value by making betterdecisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW ® For more information on .SAS® Business Analytics software and services, visit SAS Institute Inc. World Headquarters   +1 919 677 8000 To contact your local SAS office, please visit: SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright © 2012, SAS Institute Inc. All rights reserved.103003_S83731_0312