Enterprise Data Integration Integration Solution Overview November 18, 2005

1. Enterprise Data Integration Integration Solution Overview November 18, 2005 Steven Fontana, IBM, Integration Specialist - Citigroup sfontana@us.ibm.com 631-804-8888 John Bekisz, IBM, Integration System Engineer

2. Financial Services Institutions Face Key Market Challenges Key FS Market Challenges Channel OptimizationMultiple touch points provide challenges in maximizing customer wallet and mind share Single View of Product and Customer Past CRM investments have not achieved the promise of wallet share due to silo LOB implementations Complex Business Infrastructure Legacy applications continue to put pressure on profits and prevent enterprise views of customers, products, trades, positions, etc Regulatory ComplianceGovernment mandates and industry standards require data infrastructures to be compliant with USA Patriot, Sarbanes, Basel 2, etc Market and Partner Networks Enhanced market infrastructures (e.g., SWIFT Net) provide new ways to optimize payments and settlements infrastructures

3. IBM Information Integration’s Capabilities Address These Challenges Key FS Market Challenges IBM Information Integration’s Capabilities Deliver updated portfolio, account and customer information to multiple channels to support marketing and customer service Channel Optimization Deliver a single version of the truth across multiple channels and business units Single View Accelerate consolidation of legacy sources to target applications to reduce support costs Complex Business Infrastructure Profile, cleanse and transform enterprise data to support Sarbanes, Basel 2, AML Regulatory Compliance Enhanced support for market infrastructures such as SWIFT Net and new connectivity standards such as SWIFT ML Market and Partner Networks

4. IBM Information Integration’s Customers are Proof Points IBM Information Integration’s FS Successes IBM Information Integration’s Capabilities Consolidated data across dozens of silo LOB’s ; branches, service centers at Top 10 Brokerage Firm to provide point of contact up sell capability Deliver updated portfolio, account and customer information to multiple channels to support marketing and customer service More than 20,000 users utilize single standard corporate utility to analyze customer profitability at JP Morgan Chase Deliver a single version of the truth across multiple channels and business units Accelerate consolidation of legacy sources to target applications to reduce support costs Consolidated multiple mortgage and lending systems to provide new CRM-based system for GMAC Mortgage Single common library of data integration routines deployed throughout 39 countries for Standard Chartered Bank-sets the stage for Basel II IRB Profile, cleanse and transform enterprise data to support Sarbanes, Basel 2, AML Enhanced support for market infrastructures such as SWIFT Net and new connectivity standards such as SWIFT XML Accelerated adoption of new SWIFT standards at Credit Suisse Group without back office changes

5. Enterprise Information Integration EII Web Integration • Poor data design and organization • Poor data management Enterprise Application Integration EAI • Deteriorating data quality • Changing business & technology • Inadequate data delivery Extract, Transform and Load ETL Technologies Introduced To Address The Problem Analytical Enterprise Data Integration Data Mart • Important and beneficial functionality • But no real solution to the key data problems PeopleSoft Data Mart SAP Consumer Portals Siebel Other sources Enterprise Data Warehouse Trading Partners Legacy data Extract/Transform/Load ETL Oracle Operational Data Store Electronic Marketplaces Will my data be siloed? Which Technology do I use? How Flexible Is My Design? Integrate the Parts Automation Reusability Common Meta Data Cleansing Profiling SOA

6. GROUP LEVEL COMPANYLEVEL METADATA REPOSITORY Application Consolidation, BI/DwH, ComplianceCustomer Challenges Big Gap Current State In Large Enterprises • Overlapping and redundant: • Data • Applications • Infrastructure (servers and storage) • No single, consolidated view of enterprise data • Hand coded data integration spaghetti • Supporting all of the above: • Consumes >40% of IT budget • Chokes flexibility and competitiveness Desired End State In Large Enterprises • Radical consolidation of: • Data • Applications • Infrastructure (servers and storage) • Run the business on a single, consolidated view of enterprise data (Master Reference Data) • Eliminate hand coded data integration spaghetti • Reduce costs radically while improving competitiveness

7. GROUP LEVEL COMPANYLEVEL METADATA REPOSITORY Customer Challenges Big Gap Current State Metrics Data • Major US Bank has tens of terabytes of redundant and overlapping data following acquisitions. Applications • A major global chemicals company is running 12 instances of SAP and has no consolidated view of the business. • A major global telco was operating three order systems and had no consolidated view of orders. Infrastructure • Global logistics supplier needs to consolidate: • 18 data centers to 3-4 data centers • 1500 applications to 200 applications • 2600 servers to 1600 servers Hand coded data integration • Major US Bank - 3,000 people hand coding • Canadian Bank – 5,000 people hand coding Desired End State Metrics Data • Creating consolidated view of enterprise data will save the US Bank $30 million in storage costson one project. Applications • Consolidation to 1 global SAP instance will reduce operating costs by $40 million annually. • Creating a consolidated view of orders led to the capture of $200 million in revenue that was previously lost. Infrastructure • Restructuring and consolidation will increase the logistics supplier’s operating profit by at least € 1 billion annually by 2005. Hand coded data integration • US Bank – 50% productivity gain would save $150 million annually • Canadian Bank – 50% productivity gain would save $250 million annually

8. Standardization Enhancement Transformation Discovery Delivery Semantics Meta Data Cleansing Logic Auditing On-Demand and Event Driven Services Service Oriented Architecture ANY SOURCE ANY TARGET DISCOVER TRANSFORM PREPARE CRM ERP SCM RDBMS Legacy EAI/ Messaging Web services XML/EDI Data Warehouse CRM ERP SCM Business Intelligence RDBMS EAI/ Messaging Web services XML/EDI Data Warehouse Discover data content and structure Standardize, match, and correct data Transform, enrich, and deliver data Deliver Understand Reconcile DataStage DataStageTX Federated Profile Stage Audit Stage Quality Stage Parallel Execution Meta Data Management Enterprise Information Integration Platform

9. Making The Transition 3. Cost savings on first project fund COE and downstream projects. 5. Build Self Sufficiency Master Reference Data • Architecture • Methodology • Software Platform • Mentoring and best practices COE 2. Start implementing COE and MRD on first project 1. Implement First Project 4. Start implementing downstream projects • Leverage capabilities developed previously • trained staff • software templates, software components, business rules, etc. • Continue to build COE and Master Reference Data Global Data Warehouse General Ledger Consolidation Single View of Customer (Siebel) Billing System Consolidation Project #3 Project #4 Project #2 Project #1

10. Financial Lending Institution

11. Our Capabilities in Financial Services 2 1 Market and Partner Network 5 Financial Institution 3 Financial Services Value Chain 6 4 6 Subsidiaries/LOB Units Branches/LOB Units Customers Subsidiaries/LOB Units 2 Environments Transactional Operational Analytical 1 Standards Adoption (SWIFT Net Migration & FiXML) 3 Core Banking & Legacy Application Consolidation 5 Single View Across Product, Customer, Portfolio IBMCapabilities Channel Optimization 2 Master Data Management for Reference & market Data 4 Risk Management & Regulatory Compliance 6

12. Standards Adoption 1 IBM PACK for SWIFT Clients Logical Message Format SWIFTNet CPG Counter- parties IBM DataStage TX CPG Service Providers Back-end Systems Customer Examples Standards-Based Trading Pain Points IBM Information Integration Value • Complex messages; difficulty in adding new messages or supporting new versions • Many industry protocols, transports and data formats. Flexibility is key • Needfor integration with back-end systems hosting data in complex formats • Needfor quality data to have quality partner interaction • Support for SWIFT, EDI and other industry standards for partner trading, with message data normalized for streamlined updates • Broad range of back-end connectivity options supported by powerful data transformation and connectivity via SAA • Data matching and standardization, limiting errors and delivering consistency • Data connectivity, transformation and quality – in one integrated platform. • Credit Suisse Group • Deutsche Bank • KAS Bank • Fidelity Investments • Bank of New York

13. Credit Suisse Group Credit Suisse Group | World-leading financial services company, advising clients in all aspects of finance, around the world, around the clock. 360° Finance Problem Solution Result Accelerated and simplified adoption of new messages (ISO 15022) without overhaul of back-office systems, with normalized message data across the organization. Simplified management and monitoring, providingcomplete visibility into transactions and messages Implemented IBM DataStage™ TX and IBM™ PACK for SWIFT with Logical Message Format for complete SWIFT integration and support for all SWIFTNet services. LMF shields back end systems from periodic message format changes Heightened ROI and competitive pressures required the automation of the process flows to reduce overall settlement times without access to incremental internal resources. Needed to convert to ISO 15022 messages from 7775 format without impacting multiple back office systems

14. Master Data Management 2 Institutional / Individual Accounts Reference Data Trading Accounts Mrs. M. Talber Buy and Sell side allocation Global Custodians Security masters, SSI’s Brokerage Firm DTC and CUSIPS, etc Funds Managers Single View and Mappingto Industry Data Pools Single View Integration Pain Points IBM Information Integration Value Customer Example • Duplications, errors and manual overrides in transactional data received from sales channels • Duplicated and inconsistent data in corporate systems • No single understanding of customers • Not ready for Global Data Synchronization • De-duplication of security records stored in multiple formats/systems • Real time reconcilations against data received from custodians, buy side, sell side, street etc • Customizable business rules for matching • Investigate and understand data structures and formats • Top Ten Brokerage Firm • Freddie Mac • Wells Fargo

15. Freddie Mac Freddie Mac|A stockholder-owned corporation established by Congress in 1970 to support home ownership and rental housing Problem Solution Result Replacing hundreds of manually-coded integration programs with automated, metadata-driven parallel solution. Cut time required to update 1M+ transactions per data. Using metadata to document process for easier maintenance and extensibility. Unable to quickly assess the impact of millions of changes to their mortgage portfolio on a daily basis. This limited their ability to manage risk, extend loans and optimize margins by exploiting small rate differences between financial borrowing markets and lending rates. • Changes to mortgage portfolio will be visible via Freddie Mac’s enterprise data warehouse systems within 12 hours of occurring • Richer and timelier reporting environment • Greater opportunity to increase margins and expand lending

16. Core Banking and Legacy Application Consolidation 3 Legacy De-Dupe Legacy Cleanse Legacy TargetEnvironment Initial Staging Target Staging Define Relations Legacy R/3 Standardize R/3 Map R/3 R/3 IBM Information Integration Platform Customer Examples Legacy Application Pain Points IBM Information Integration Value • High maintenance costs and lack of data/process unity associated with running multiple instances of the same application • Unrealized value from mergers and acquisitions with multiple DDA and credit , lending systems • Keeping track of metadata during application transition and consolidations • Uncoordinated technical architecture • Rapidly profile and analyze data across corporate systems to prepare for migrations and consolidations • Migrate only data that is meaningful, active and de duplicated • Rapidly locate all institutional data in source systems and prepare to migrate to target applications • Conduct impact analysis on potential changes to metadata • GMAC Mortgage • Nordea • Lloyds Bank

17. Nordea Nordea | Largest financial services group in Scandinavia with EUR 252 billion in total assets, 9.7 million personal and 1 million corporate customers Problem Solution Result • Nordea is able to act as one operating unit, in support of having one brand • Cut IT budget by 25%, resulting in savings of $8M by 2003 IBM DataStage™ and SAP R/3 PACK prepares data for the initial load into R/3. Needed to support a sub ledger consolidation from 4 large retail banks. Auditing and guaranteed delivery are critical. Transactions arrive from complex flat files from many countries, and must be validated, mapped, reconciled and prepared before loading into R/3.

18. Risk Management & Compliance 4 Customer Examples IBM Information Integration Value Risk Management Pain Points • Three year period of measuring operational risk data to meet Basel II Accords has begun • US Patriot Act has extreme focus on Anti Money Laundering and Know Thy Customer- fines for non compliance are severe • Sarbanes Oxley and other Regulatory mandates place intense focus on data quality • Rapidly profile and analyze data across corporate systems to prepare for internal ratings based approach for Basel 2 • Data Quality Assessments that quickly identify gaps in required data for compliance in SOX, AML etc • Conduct impact analysis on potential changes to metadata and change data management • Ny Kredt • NASDR • Standard Chartered • AIG

19. Standard Chartered Standard Chartered |World- leading emerging markets bank with over 500 offices in more than 50 countries “With IBM, Standard Chartered Bank will build one common library of data integration routines and deploy them throughout our company, a critical factor to ensuring that our risk data is all handled in accordance with company standards." -- Senior Project Manager, BASEL IS Problem Solution Result Wholesale Bank Basel II Credit Risk Projectgoal is to meet Basel II Capital Accord guidelines by 2006, utilizing new internal modeling approaches for capital calculations. Required strong risk management analytics, processes and disclosure. Needed consistent data management processes across operations, customers and supporting technology in more than 50 countries Centralized Basel II Data Integration Solutionleverages IBM Enterprise Integration Suite™ to deliver enterprise integration. Integration routines are built in IBM DataStage™ and deployed throughout Standard Chartered Bank in repeatable manner. Data is treated in a consistent manner, critical to Basel II compliance. • Delivers enterprise integration and data consistency necessary for Basel II • Supports groups across the Wholesale Bank (Group Risk Management, Finance and Special Asset Management teams) • Provides reliable information for management and regulatory reporting, portfolio management and front-line business users

20. Single View 5 Product Records Customer Accounts Depository Accounts Mrs. M. Talber Credit and Lending John & Molly Talber Investment Accounts Molly Talber Mortgage, etc M Talber Single View and Mappingto Industry Data Pools Single View Integration Pain Points Customer Example IBM Information Integration Value • Duplications, errors and manual overrides in transactional data received from sales channels • Duplicated and inconsistent data in corporate systems • No single understanding of customers • Not ready for Global Data Synchronization • De-duplication of customer records stored in multiple formats/systems • Real time account and customer information against data received from branches; call centers, Web etc • Customizable business rules for matching • JP Morgan Chase • Wells Fargo • Edward Jones

21. JP Morgan Chase JPMorgan Chase |A leading global financial services company Problem Solution Result • More than 20,000 internal customers now use a single corporate-standard customer profitability “utility” to analyze and make decisions that improve the overall profitability of the company • No one is allowed to comment or “spin” profitability without referring to this utility Needed single, authorized source for customer profitability reporting & analysis. Solution required highly automated integration process, straightforward change management, and ability to handle a diversity of data sources. Used IBM Information Integration solution to receive 300+ feeds from product systems worldwide, then transform and load into a data warehouse. 1TB+ of data is now updated every 48 hours with daily refreshes planned.

22. Source Systems: • Kiosks • ATM’s • Call Centers • Internet • DDA Core Banking • Branches Authoritative Database Customer Inf File Channel Optimization 6 IBM Information Integration Suite Customer Examples Channel Pain Points IBM Information Integration Value • No single understanding of customers or brands • Lack of a comprehensive view of data across systems • Duplications, errors and manual overrides in transactional data received from sales channels • Inconsistencies between data in different systems causing inaccurate information • Link multiple disparate sources of information through semantics-mapping and data matching • Standards-based interfaces to integration brokers • Maintain meta linking and matching between data sources • In-flight data enrichment • Access to a broad range of legacy sources • Top Ten Brokerage Firm • New York Life

23. New York Life Insurance New York Life Insurance |Largest mutual life insurance company in the United States Problem Solution Result Multi-tier solution with UNIX-based operational data store and enterprise data warehouse feeding marts for reporting, and 7x24 web-based access. Leveraging IBM DataStage™ to integrate legacy data into warehouse and IBM ProfileStage™ to better understand and access mainframe sources. Agents and HQ staff were unable to maximize customer profitability or pursue up-sell/cross-sell opportunities. Detailed customer and policy data residing in 15+ separate legacy mainframe policy systems with little to no documentation and poor data quality was unavailable to users and multiple channels. • Improved customer visibility by providing 10+ staff with ad hoc reporting to complete customer information • Reduced IT costs by $130k annually by eliminating manual reporting • Provided agents with 7x24 detailed customer and policy data through secure web site

24. Websphere Data Integration Suite Technical Product Overview John Bekisz

25. WebSphere Information Integration Solutions Connect to Data and Content Analyze ModelCleanse Transform Find Federate Place Publish Delivering accurate, consistent, timely, and coherent business information ProfileStageAuditStage RTI QualityStage Event-driven Standard APIs Service-oriented Scheduled Integrated Metadata Information Integration Services Integrated Metadata DataStageDataStage TX MetaStage

26. The Ascential/IBM Data Integration Solution • Open, Service-Oriented Architecture • Integrated Data Profiling & Data Quality • Advanced Data Transformation and Routing • Reusable Components & Rules • Unlimited Performance with Linear Scalability • Robust, Intelligent Adapters • Anytime, Anywhere Connectivity • Industry Standard Compliant (XML, EDI, JMS, JCA) • Industry-Ready Integration Solutions Service-Oriented Architecture Real-Time Integration Services and Event Management DISCOVER PREPARE TRANSFORM and DELIVER Discover data content and structure Standardize, match, and correct data Transform, enrich, and deliver data ProfileStageAuditStage QualityStage DataStageDataStage TX Parallel Execution Engine Meta Data Management Enterprise Connectivity

27. Data Transformation: DataStage DataStage Why DataStage? • Graphical, codeless design environment • Extensible transformation platform that leverages existing business logic • Built on the most scalable and adaptable processing engine • Enterprise-class platform that delivers proven ROI • Manages the evolution from development to deployment smoothly Business Benefits • Unsurpassed levels of productivity • Accurate, consistent information delivered on-time • Consistent rules applied across applications • Data and process is auditable DataStage Server Inputs Transform Quality Output Multiple Jobs in Parallel

28. Transform Complete Development Environment • Integrated ETL development workbench • Design, develop, view data • Debug, test, monitor & manage • Metadata driven ETL processing • Portability • Develop anywhere, Deploy anywhere • Client has access to multiple servers • Server maintains all connectivity • Extensible architecture • Many Plug-ins to other vendors products

29. Transform It’s in There • Pre-built Functions • Date/Time Conversions • Data Type Conversions • String Manipulations • Mathematical Formulas • Data Warehouse Functions • Surrogate Key Generation & Maintenance • Aggregation • Change Compare • Data Manipulations • Sorting, Merging, Joining, Filtering • FTP, HTTP, operating commands • Online Library available for Download

30. Transforms and Routines Tables Shared Container Job Job Sequencer DataStage Architecture We keep you in the tool High Degree of Reuse and version control at each level

31. Transform 80/20 Rule • 80% of Transformations are simple • Only Spend 20% of your time on these rules • String formatting, Date conversions, Look-ups • 20% of Transformations are complex • Spend 80% of your TIME on the 20% Complex • Too Much Data; Too Little Time • Data Scrubbing, householding, survivorships • Multiple Sources to Multiple Targets • Complex Business Rules which require business logic • Nested if/then/else; case; loops; arrays • EVERYBODY Does the 80% Easy Stuff Easily • ONLY Ascential Makes the 20% Complex look Easy

32. Easy to Use GUI

33. Work as you think

34. Extracting from DB/2 into Hash Files

35. More Robust Example

36. Transformation

37. Example: MQ and QualityStage Integration

38. Example: MQ and QualityStage Integration

39. Example: MQ and QualityStage Integration

40. Example: MQ and QualityStage Integration

41. High Level Design • Design the Extraction, Transformation and Load processes • Work as you think: • Move easily from white board to design • Integrated: • Test, debug, data viewer, run DS jobs and perform maintenance from a common graphical workbench.

42. Single Point of Control

43. Full job control

44. Produce and Consume Web Services DataStage job • Invokes Web services from within DataStage jobs • WSDL browse & import capabilities • Easily call WS operations from DataStage • Web services can be sources, targets, or transformations • Use WS PACK to invoke a Web service from a DataStage job • Use RTI to package a DataStage job as a Web service

45. Ascential RTI Server – J2EE Environment Web Service Client RTI Console Web Service Client Auditing Platform Load Balancing Authentication JMS Client Java Messaging Service Authorization Java Application Enterprise Java Beans Logging RTI Agent RTI Agent RTI Agent DataStage Server 1 DataStage Server N DataStage Server 2 “Always on” “Always on” Transformer RTI Output RTI Input QualityStage “Always on” Enterprise Integration Suite Real-time Integration (RTI) Services RTI Services connection to Enterprise Applications, Portals, and Business Process Integration

46. Thank You John Bekiszmobile: 212.920.0566jbekisz@us.ibm.com

47. One Integrated Solution • Three Points of Integration • GUI • Server • Metadata