Data Convergence and Enabling IT Infrastructure for Real Time Decision Support

2. Data Convergence and Enabling IT Infrastructure for Real Time Decision Support Sybase Global Financial Solutions Center October 13, 2004

3. Agenda 8:30 Registration / Continental Breakfast 9:00 Introductions Sybase, Eric Johnson VP Financial Services 9:15 Data Convergence Issues in Financial Services: Steve Patalano An Architect’s View Director and Sr.Architect, Capital Markets, Bearing Point 10:00 Architecting a Real Time Solution Mike Kane, Sybase Carol Clarke, Informatica 10:30 Break 10:45 Basel II – Compliance Driven Data Needs Paul Lockyear Principal, Quadrant Risk Management 11:15 Solution Demonstration 11:45 Q&A and Panel Discussion Sybase, BearingPoint, Informatica, Quadrant Risk Management 12:00 Lunch

4. Welcome Eric Johnson VP of Financial Services Sybase

5. The Case for Data Convergence Steven Patalano Director and Senior Architect, Capital Markets BearingPoint

6. Data Convergence and Service-Oriented Architectures

7. Data ConvergenceSetting The Stage

8. FINANCIAL Reporting and Regulatory CUSTOMERS KYC and Corporate Actions COMPETITORS Offering Cleansed Industry Data Solutions Converged Data Infrastructure OPERATIONS Reduced redundant data maintenance INFRASTRUCTURE Dynamically Reducing redundant systems Where Is The Industry Today? • Fragile and unwieldy data infrastructure . . . • “Siloed” applications, stand-alone business units and un-integrated offshore operations creating “unmanageable” data complexity • Rapid proliferation of unstructured content, such as spreadsheets • Information portals disconnected from the back office • Ill-defined and/or redundant governance and accountability • . . . will be further stressed by regulatory and market pressures • Market rebounds create opportunities for organic growth (e.g., customer cross-sell and up-sell) • Shareholders and regulatory bodies alike demanding greater information transparency (e.g., APRA standards, Sarbanes-Oxley and Basel II) • Information increasingly reconsidered as a strategic asset at higher levels of the organization

9. What Is Data Convergence? True convergence focuses on data, processes and technology across application domains • Differentiate between integration that focuses on connectivity at the edge and convergence that focuses on the core • The greatest opportunities for convergence exist within the CRM, Risk Management, Financial Management and IT Security domains Data convergence represents the first key component of a convergence strategy • Creation of single truths and reuse of existing data across systems • Development of new, converged data models • Facility for unstructured content and previously not captured attributes, such as identity credentials and entitlements • Inclusion of new data transformations such as security protection (e.g., encryption) and integrity validation (e.g., digital signature)

10. Why Is It Realistic Now? In spite of the traditional preconceptions and continuing challenges, data convergence can and should be realistically addressed today. Challenges • Earlier efforts to aggregate and consolidate data and information have caused the perception of failure due to significant cost and complexity • IT spending has been reduced significantly, partly as a result of the post Web era hangover • There exists no clear ownership of enterprise data • It is difficult to justify a Return on Investment at the Line of Business level for Enterprise data initiatives • Many feel that Business Process Outsourcing represents a potential “silver bullet” Realities • Creation of a Business growth platform is essential as the market improves • Immediate concern from Executives related to financial transparency and intense regulatory pressure • Additional Board of Director mandates • Evolution of data technologies (e.g. Data Services Layer / EII / High Performance analytic engines) combined with the maturing of technology processes and architectures • The realization that simplified data infrastructure substantially reduces cost, potentially more than the voice/data infrastructure layer

11. Who Is Interested? CIO • CIO’s are faced with both sides of the business; needs for growth and expansion and cost justification for each IT project. • Institutions are spending millions each year on IT but feel they have reached the limits that enable them to contain costs yet enable large-scale acquisitions. CFO • In the post Sarbanes-Oxley environment where CFOs are asked to sign off on financial statements, the quality of data and the systems that produce that data are being scrutinized now more than ever before. • Growth can only come with efficient architectures and synergistic investments in technology. CRO • Risk compliance in financial institutions has become more complicated by a number of regulations such as Basel II accord and Sarbanes-Oxley. • A “siloed” approach to compliance is no longer valid, significant savings can be found in the pooling of initiatives around risk. COO • In an environment where COO’s are being asked to grow revenues with less manpower than ever before, new regulations are getting in their way of being effective. • Privacy policies, and opt out policies are destroying pre existing databases and making it hard to cross sell and up sell existing customers.

12. What Is Driving The Data Convergence Timeline? GLBA 2000 GLBA Calif.SB386Security breach July 2003 Basel IIData Collection (op.loss) 2003 Basel IIReporting 2006 Calif.SB336 BSLDC BSLImpl. Opt-out Do-Not-Call Oct.2003 Sarbanes-Oxley Certif. & disclosure 2003 Sarbanes-OxleyControls & Reporting April 2005 SOXReq’s DNC SOXImpl. Patriot AML,CIP Oct.2003 HMDAReporting March 2004 HMDAData collection 2003 HMDAImpl. HMDADC Patriot Oct’ 06 Oct’ 03 Apr’ 04 Apr’ 05 Oct’ 04 Oct’ 05 Apr’ 06 Shared data and definitions: Customer, product and org hierarchies, HH, account, instrument, customer activities, operating losses, loan origination) Interactions:Finance-Compliance-Risk-Credit-Marketing-Sales-Service (see graphical view of interactions on separate slides) Shared capabilities: data acquisition and consolidation, Identity management, model development, rules engine Common requirements (Convergence) Data quality: Consistency, integrity, quality, auditability, tracking Data linkages: Customer linkages: HH, dups, counterparty, connected borrowers, guarantors, signatories Data access: real-time access and updates, virtual data access for trial modeling and tactical data collection Operational integration: real-time alerts / triggers, rules deployment and execution

13. Is It Just About Technology?

14. Governance Key Takeaways • A better understanding of the importance of data at all levels of the organization • Ideas to accomplish data convergence initiatives within existing governance structures • Practical organizational enhancements that could lead to positive results • Incentive models Key Questions • Who owns the problem? • Who funds the solution? And how? • Who is accountable? • What is the role of the Board of Directors? • Is the role of the CIO changing? Should it? • What is the role of “corporate” versus the line of business? • What is the appropriate governance structure?

15. Technical Key Takeaways • Awareness of new capabilities of standard data oriented tools • Understanding of emerging data focused architectural components and associated products • Architectural alternatives for dealing with data • A view on how new data architectures fit into the overall enterprise architecture Key Questions • This is a decades old challenge. What has changed? • Has the role of the Data Warehouse changed? Should it? • What are EII and DSL? Why do I care? • What technologies are emerging? • What are my alternatives for architecting data?

16. Transformation Key Takeaways • Investment synergies • Use existing initiatives to drive toward data convergence • Portfolio optimization and prioritization concepts • New process demands and process improvement options Key Questions • How is investment leverage achieved? • How do I prioritize existing and planned initiatives? • How can the infrastructure be updated while minimizing business disruption? • Do new process demands and process improvement options exist? • Will outsourcing solve the problem? • What are the possible first steps?

17. Data ConvergenceTechnical Issues

18. What Are The Key Discussion Areas? Clearly many technical aspects will need be discussed in the context of a data convergence initiative, but at the outset the key areas to address must include the following • Architecture • What architectural alternatives should the organization discuss? • What are the architectural drivers that must be considered? • What are the different patterns available? • Content & Structure • What are the types of information content we have in the organization and how might we categorize it? • What is the structure of this content? How well does the organization support these structures? • Flow • How does data currently flow through the enterprise? Should the flow change? • What are the real-time requirements and implications of this flow?

19. How do I Tie Architecture to ROI? The evolution of business drivers and the emergence of new perspectives on technology have increased the importance of architecture in the determination of return of investment Market Growth Client/ Market Trends Regulatory/Industry Initiatives Product Silos Process/ Information Requirements Industry Internal/External Factors Architecture Blueprint Business Processes Rationalization Business Service Based Technology Architecture Underwriting Approval Customer Contact Loan Sourcing • Common Business Services Across Different Products • Leverage Best of Breed Current Application and Component Inventory • Application of Evolving Technology Standards • Channel Management • Cost Reduction • Automation • Consistent customer experience Value Proposition Cost Reduction Revenue Enhancement Risk, Efficiency & Control ROI A business case and ROI approach prioritizes potential initiatives to optimize investment funding

20. What Is A Services-Based Architecture? A services-based architecture will improve the characteristics of both the business and technology environment and provide a solid foundation for data convergence Current State Future State Business Technology

21. Common Access Portal Role Assignment and Security Client & Producer Relationship Management Business Processes Enterprise Integration and Work Flow Platform Third Party Distributors Other Manufacturers Service Associates Constituents Business Partners Retail Agents Clients Telephone Inter n Governments Municipals TPA Bonds OTC OTC Cash OTC ETD Cash ETD Cash ETD Main Frame FEPI Screen Scraping: All Financial Transactions Northern Trust Pen Front Office AFP Delaware OCD GPAS Middle Office IVR Ft. Wayne Financial Transaction Middleware Vantage 12.5 Back Office ISC COBOL: Email APS PenFacts Risk Management Groupnet Individual Distribution EDI COM IDMS Others Life 70 Accounting Mail Queue ADC Lock Box Reporting FID Product Factory Term Conversion Claim Handling New Business Marketing Support Sales Support Customer Service Producer Management AMG Business Components ESA Utilities Harvested & Wrapped Traditional Platforms COBOL: Legacy Surround COBOL: View Manager Legacy Access Middleware Source Data & Reporting Outputs Advice Workflow Case Management Vantage LifePro AOS New Business Constituent/ Party Claims Money Handler Compensation Underwriting … Client Database Transaction Database Internet Middleware COBOL: EDI COBOL 21 Lock Box Middleware Database Content Mgr Lotus Notes What Is The End-State Goal? The role and goal of architecture is to simplify technology: reduce costs through improved acquisition, licensing and operational performance and avoid the linear addition of resources and capital expenditures as business volumes and data needs grow Current State Bonds Governments Municipals Cash OTC ETD Cash OTC ETD Cash OTC ETD Future State Front Office Middle Office Back Office Risk Management Accounting Reporting

22. A Conceptual Services-Based Architecture

23. How Do I Relate The Architecture To The Data? Given a “services-based” architecture, it follows that underlying design of the architecture needs to “serve” the business and the information the business demands. The initial steps to ensuring this cohesion are to • Develop Models and Define Semantics • Models describe the business data from a conceptual viewpoint • and are independent of realizations by actual systems. • A data model: comprises a UML class model (or ERD) of the main data items (the business entities) and their relationships. • A type dictionary: A formal definition of data types, their ranges and other content constraints • A data dictionary: A superset of business attributes, including descriptions of their meaning (semantics), standardized formatting (syntax), and universal constraints. • Models may be hierarchically organized • Enterprise Data Models • LOB Data Models (or other segmentation) • Application (System / subsystem) Specific Data Models • Include / Exclude Specific Data Domains • Customer, Account, Product, Etc • Understand Usage and Life Cycle • Creation • Modification • Retirement / Archival • Creators / Sources / Consumer • Transportation and transformation models • Owners (content and models) • Access Mechanisms

24. What Distribution Patterns Must The Architecture Support? Identify the architectural patterns for data access best suited to support the data services • Shared: data stored in a single physical repository • “Golden copy:” data stored in a single centralized repository as well as within individual applications • “Data fusion:” data stored in multiple, distributed repositories, service hides the distribution for all use case

25. How Do I Bring It Back Together? The appropriate patterns to support the data requirements are incorporated into the architectural framework. This process helps ensure • Traceability of the architecture to the information needs to the business requirements to the overarching value proposition and ROI • Clarity in terms of information flow and reconciliation needs • Flexibility in that the appropriate pattern enables reuse • Extensibility of the architecture to support new business initiatives

26. Design Principles for Shared Services • Eliminate redundancy • Minimize cross-service dependencies • Adapt interface standards • Establish clear ownership and governance on a per service basis • Understand service requirements in detail – invest in service interface specifications • Maintain consistent entity and field semantics across services (non conflicting data models) • Understand existing and anticipate potential usage patterns • Leverage existing assets

27. Data ConvergenceTransformational Issues

28. The problem can be solved! You can get there from anywhere You can achieve significant investment synergies You can bring it down to a manageable set of phased projects You can also become compliant – in a phased manner

29. You can get there from anywhere Start Linkages Others Global FS CRM Credit Risk Finance, Opt-Out, GLBA Top Bank DWH Consolidation AML, Basel, Customer DWH X-LOB, Expanded CRM Leading CC Multi-AML Basel, Customer DWH X-LOB, etc Customer DWH and SOX (EPM) Basel, Privacy X-LOB, DWH Consolidation, Decommissioning Multi-Line Retail Campaign Management, Rules Engine Privacy, Opt-Out, DNC Credit Risk, etc Regional Bank

30. $60MM+ (Current implementation and support cost trajectory in a line-of-business) $125MM (Current implementation trajectory across lines-of-businesses) $15MM (Additional capabilities to support “convergence”) $15MM (Additional capabilities to support “convergence”) $25MM (Projects delivered via the roadmap) $70MM (Projects delivered via the roadmap) CURRENT APPROACH CONVERGED APPROACH CURRENT APPROACH CONVERGED APPROACH What Are The Potential Investment Synergies? When an holistic approach is taken clients are able to see huge cost/investment-savings potential -- over 30% implementation cost savings were identified in two client cases

31. Can I Define a Manageable Set of Projects? Planned and Active Projects Growth/CRM Customer profitability Campaign Management Enterprise BI Risk Management Credit Model Funds transfer Counterparty credit risk Fraud Reporting tool upgrade Finance Enterprise planning system Performance reporting EPM Management reporting integration Treasury Tax systems Operational Reporting System Balance Scorecard Customer Portfolio Finance Infrastructure Upgrade Compliance Regulatory enhancements(Basel, SOX, GLBA, etc.) HMDA 2004 US Patriot Act, AML/SARSIn total over 100 initiatives Projects Rationalized To Convergence Vision Planned and Active Key DW Projects AML/fraud detection Basel II Credit Risk Basel II Operating Risk EPM DW rationalize LOB-specific DSS Campaign management Credit decisioning model LOB productivity mart Basel & EPM Data Convergence Operational Integration DW infrastructure AML/fraud detection Basel II Credit Risk Basel II Operating Risk EPM DW rationalize LOB-specific DSS Campaign management Credit decisioning LOB marts Sarbanes-Oxley enhancements Focus on projects relevant to building DW End-State Vision Rationalized approach to implementing projects across domains

32. Case Study – An Approach Operational Systems Heterogeneous Environment Near Real-Time Analytics Reporting Near Real Time Data Repository Harvesting/Staging Engine Operational Data Store (ODS) DB2,VSAM & IMS, Adabas Application Quadrant Risk Management ASE Application Informatica PowerExchange PowerCenter Microsoft SQL Server Application Sybase Real-Time Data Services Message Bus (e.g. TIBCO, MQ) Oracle Application Real Time Sales Force Automation Customer Relationship Management Real Time Fraud Detection

33. Architecting a Real-time Solution Carol Clarke Director, Business Development Informatica

34. Informatica Overview • Founded (1993) • Nasdaq: INFA (1999) • Over 800 employees worldwide Corporate • Data Integration Data Access • Dashboarding & Reporting Metadata Management Products • Over 2,000 companies worldwide • 83 of the Fortune 100 and over 80% of Dow Jones Customers • Over 300 sales, marketing and implementation partners including: IBM BearingPoint Sybase Siebel i2 Accenture Partners

35. Applications Audit Universal Data Services Unifies Interaction with Data Enterprise Systems Business Pressures Compliance Unified Customer View Strategic Sourcing Operational Efficiencies Mainframe Flat Files Other

36. Broad Partner Ecosystem

37. Conceptual Architecture Operational Systems Heterogeneous Environment Near Real-Time Analytics Reporting Near Real Time Data Repository Harvesting/Staging Engine Operational Data Store (ODS) DB2,VSAM & IMS, Adabas Application Quadrant Risk Management ASE Application Informatica PowerExchange PowerCenter Microsoft SQL Server Application Sybase Real-Time Data Services Message Bus (e.g. TIBCO, MQ) Oracle Application Real Time Sales Force Automation Customer Relationship Management Real Time Fraud Detection

38. UDS—Enabling Shared Data Services

39. Web Services Heterogeneous Grid Profiling and Cleansing Team-based development Security Leverage existing IT investments Performance Quantum Trusted data Enables outsourcing Lowers Privacy Exposure Key Technology Business Value Informatica PowerCenter Integrate Data. Adapt to Change

40. Complex Source Access Patented Changed Data Capture Real-time, Change and Batch Options Service-Oriented Architecture Codeless, Visual SQL Driven Enterprise Scalability Access and deliver data in “On-Demand” Extends value of existing investments Accelerate project lifecycles Reduce solution development and maintenance costs Key Technology Business Value Informatica PowerExchange Unlock Complex Data. On Demand.

41. Real-time Real-time captured changes moved in near real-time • ChangeCaptured changes, accumulated for SQL extract at periodic intervals • BatchHigh speed bulk data movement and Materialization to startcapturing changes PowerExchange Integrated “Latency” Options

42. World-class Customers in Every Industry Financial Services and Insurance Telecommunications High Tech and Manufacturing Pharmaceutical Transportation, Services,and Retail Public Sector and Federal Government

43. Architecting a Real-time Solution with RTDS & IQ Mike Kane Senior Architect, Financial Services Sybase

44. Conceptual Architecture Operational Systems Heterogeneous Environment Near Real-Time Analytics Reporting Near Real Time Data Repository Harvesting/Staging Engine Operational Data Store (ODS) DB2,VSAM & IMS, Adabas Application Quadrant Risk Management ASE Application Informatica PowerExchange PowerCenter Microsoft SQL Server Application Sybase Real-Time Data Services Message Bus (e.g. TIBCO, MQ) Oracle Application Real Time Sales Force Automation Customer Relationship Management Real Time Fraud Detection

45. Company Overview Credit Limit Risk Management Fixed Income Customer Master Message Producer Equities TIBCO JMS Commodities Message Master Polling Securities Master • Requirement: • Automatically prevent trading once a customer credit limit is breached • Prevent buy side systems from trading when a buy limit is exceeded • Current Solution: • Poll the message database periodically for credit limits thresholds. • Poll periodically the message master to check if buy-limit exceeded. • Risks: • Trading continues even when a limit (credit or buy-limit) is breached because of the non-real time nature of polling • Message master system heavily impacted due to frequent polling requests for changes.

46. Company Overview Credit Limit Risk Management Fixed Income Customer Master User changes Account changes Equities Credit changes TIBCO JMS RTDS Enabled Sybase 12.5.2 Commodities Proactive Messages In real-time Security changes Securities Master • Benefits: • Trading systems notified in real-time & proactively of limit breaches as and when they happen • Minimal impact on source systems • Eliminates custom polling application • Simplifies architecture • RTDS Solution: • Account changes are pushed out proactively in real-time • Security changes are pushed out proactively in real-time

47. Conceptual Architecture Operational Systems Heterogeneous Environment Near Real-Time Analytics Reporting Near Real Time Data Repository Harvesting/Staging Engine Operational Data Store (ODS) DB2,VSAM & IMS, Adabas Application Quadrant Risk Management ASE Application Informatica PowerExchange PowerCenter Microsoft SQL Server Application Sybase Real-Time Data Services Message Bus (e.g. TIBCO, MQ) Oracle Application Real Time Sales Force Automation Customer Relationship Management Real Time Fraud Detection

48. SybaseIQ - Best of Breed – for Large Scale Implementations • Speed • Lightning Fast Ad-hoc Access • High Performance Data Access • Lower TCO • Data Compression Versus Data Explosion • Low maintenance cost • Very competitive pricing • Scalability • Linear User Scalability 100s and 1,000s of Users • From Gigabytes to Terabytes of Data • Incredible Flexibility • Any Query - any Time • Ad-Hoc - Any Schema • Any Server Configuration • Easy to Learn, Standard Language P A R T N E R S U C C E S S Enterprise Analytics Infrastructure

49. Company Overview Gartner Categories for Rating DWs Query Complexity Amount of Detailed Data • comScore – 40 TB input data (IQ-M: 16 TB) • NC Dept. Health and Human Services - 2 TB input data (IQ-M:1.5 TB) • Nielsen Media Research - 12 TB Input data (IQ-M:12 TB) • IRS - 10+TB (10 years data) IQ-M: 10 TB • C Card Co. – 6 TB input data (IQ-M compresses 5 TB) (Non-Disclosure) • Korean Customs – 32 way joins in production • U.S. DOT – Bureau of Transportation Statistics - 18 way joins • IRS - 14 way joins • Bank of Montreal – 10 way joins Complexity of Data Model Numbers of Concurrent Users • C Card Co. - 1400 concurrent users • NC Dept. Health and Human Services - 1200 concurrent users • Nielsen Media Research -+200 concurrent users • U.S. DOT – Bureau of Transportation Statistics - 1300 tables • C Card Co. - 700+ tables and growing rapidly

50. Best Practice and Basel II Compliance Paul Lockyear Principal Quadrant Risk Management (International)