® IBM Software Group © 2005 IBM Corporation Steven Fontana, IBM, Integration Specialist - Citigroup [email protected] 631-804-8888 John Bekisz,IBM, Integration

®

IBM Software Group

© 2005 IBM Corporation

Steven Fontana, IBM, Integration Specialist - Citigroup [email protected] 631-804-8888

John Bekisz, IBM, Integration System Engineer

Enterprise Data Integration

Integration Solution Overview

November 18, 2005

IBM Software Group | WebSphere software

2

Financial Services Institutions Face Key Market Challenges

Channel Optimization Multiple 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

Key FS Market Challenges


3

Key FS Market Challenges

IBM Information Integration’s Capabilities Address These Challenges

Channel Optimization

Single View

Complex Business Infrastructure

Regulatory Compliance

Market and Partner Networks

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

Enhanced support for market infrastructuressuch as SWIFT Net and new connectivitystandards such as SWIFT ML

Profile, cleanse and transform enterprise data to support Sarbanes, Basel 2, AML

IBM Information Integration’s Capabilities

Deliver updated portfolio, account and customer information to multiple channels to support marketing and customer service


4

IBM Information Integration’s FS Successes

Consolidated data across dozens of silo LOB’s ; branches, service centers at Top 10 Brokerage Firm to provide point of contact up sell capability

More than 20,000 users utilize single standard corporate utility to analyze customer profitability at JP Morgan Chase

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

Accelerated adoption of new SWIFT standards at Credit Suisse Group without back office changes

IBM Information Integration’s Customers are Proof Points

Deliver a single version of the truth across multiple channels and business units

Accelerate consolidation of legacy sourcesto target applications to reduce support costs

Enhanced support for market infrastructuressuch as SWIFT Net and new connectivitystandards such as SWIFT XML

Profile, cleanse and transform enterprise data to support Sarbanes, Basel 2, AML

IBM Information Integration’s Capabilities

Deliver updated portfolio, account and customer information to multiple channels to support marketing and customer service


5

Enterprise Data

Warehouse

Technologies Introduced To Address The Problem

Oracle

Othersources

SAP

PeopleSoft

Legacydata

Siebel

TradingPartners

ElectronicMarketplaces

DataMart

DataMart

Operational Data Store

ConsumerPortals

Analytical

Important and beneficial functionality But no real solution to the key data

problems

Extract/Transform/LoadETL

Enterprise Application Integration EAI

Web Integration

Enterprise Information Integration EII

Poor data design and organization

Poor data management

Deteriorating data quality

Changing business & technology

Inadequate data delivery

Extract, Transform and Load ETL

Enterprise Data Enterprise Data IntegrationIntegration

Integrate the Parts Common Meta Data Automation

Which Technology do I use? Will my data be siloed?

Profiling

How Flexible Is My Design?

SOACleansing

Reusability


6

Application Consolidation, BI/DwH, ComplianceCustomer Challenges

Payroll3

Payroll1

Payroll2Fin 3Fin 2

Payroll3

Payroll1

Payroll2Fin 3Fin 2

FinancialFinancial RisksRisks

Customers I

Customers I

CustomersII

CustomersIISUPPORTSUPPORTSUPPORTSUPPORT

PRODUCTIONPRODUCTIONPRODUCTIONPRODUCTION

DISTRIBUTIONDISTRIBUTIONDISTRIBUTIONDISTRIBUTION

REFERENCESREFERENCESREFERENCESREFERENCES

GROUP LEVEL

COMPANY LEVEL

METADATA REPOSITORY

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

Big Gap


7

Payroll3

Payroll1

Payroll2Fin 3Fin 2

Payroll3

Payroll1

Payroll2Fin 3Fin 2

FinancialFinancial RisksRisks

Customers I

Customers I

CustomersII

CustomersIISUPPORTSUPPORTSUPPORTSUPPORT

PRODUCTIONPRODUCTIONPRODUCTIONPRODUCTION

DISTRIBUTIONDISTRIBUTIONDISTRIBUTIONDISTRIBUTION

REFERENCESREFERENCESREFERENCESREFERENCES

GROUP LEVEL

COMPANY LEVEL

METADATA REPOSITORY

Current State MetricsData 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

Big Gap

Desired End State MetricsData Creating consolidated view of enterprise data will save the US Bank $30 million in

storage costs on 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

Customer Challenges


8

Enterprise Information Integration Platform

CRMERPSCM

RDBMSLegacy

EAI/ Messaging

Web servicesXML/EDI

Data Warehouse

ANY SOURCE ANY TARGET

CRMERPSCMBusiness IntelligenceRDBMSEAI/ MessagingWeb servicesXML/EDIData Warehouse

Parallel Execution

Meta Data Management

DISCOVERDISCOVER

Discover data content and structure

Profile StageAudit Stage

PREPAREPREPARE

Standardize, match, and

correct data

TRANSFORMTRANSFORM

Transform, enrich, and deliver data

DataStageDataStageTX

Federated

UnderstandUnderstand ReconcileReconcile DeliverDeliver

Quality Stage

Service Oriented Architecture

On-Demand and Event Driven Services

Discovery Meta Data SemanticsStandardization Enhancement

Cleansing

Transformation

AuditingLogicDelivery


9

COE

Project #1

Billing SystemConsolidation

MasterReference

Data

Project #2

GlobalData Warehouse

Project #3

General LedgerConsolidation

Project #4

Single View of Customer

(Siebel)

1. Implement First Project

ArchitectureMethodologySoftware PlatformMentoring and best practices

2. Start implementing COE and MRD on first project

3. Cost savings on first project fund COE and downstream projects.

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

5. Build Self Sufficiency

Making The Transition


11

Financial Lending Institution


12

Our Capabilities in Financial Services

TransactionalTransactional OperationalOperational AnalyticalAnalytical

Subsidiaries/LOB Units

Financial Institution

Customers

Standards Adoption (SWIFT Net Migration & FiXML)

Single View Across Product,Customer, Portfolio

11

11 5533

22

22Master Data Management forReference & market Data

Core Banking & Legacy Application Consolidation

Channel Optimization 66

66Financial Services

Value Chain

IBMCapabilities

Environments

44Risk Management & Regulatory Compliance

Market and Partner Network

22

Subsidiaries/LOB Units

Branches/LOB Units

44

55

33

66


13

Complex messages; difficulty in adding new messages or supporting new versions

Many industry protocols, transports and data formats. Flexibility is key

Need for integration with back-end systems hosting data in complex formats

Need for 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.

Standards-Based Trading Pain Points IBM Information Integration Value Customer ExamplesCredit Suisse Group

Deutsche Bank

KAS Bank

Fidelity Investments

Bank of New York

11

Standards Adoption

IBM PACK for SWIFT

Back-end Systems

LogicalMessageFormat

IBMDataStage TX

Clients

CPGCounter-parties

CPGServiceProviders

SWIFTNet


14

Credit Suisse Group

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

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

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, providing complete visibility into transactions and messages

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


15

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

Single View Integration Pain Points IBM Information Integration Value Customer Example

Top Ten Brokerage Firm

Freddie Mac

Wells Fargo

Mrs. M. Talber

Global Custodians

Brokerage Firm

Institutional / Individual Accounts

Institutional / Individual Accounts

Funds Managers

Trading Accounts

Buy and Sell side allocation

Security masters, SSI’s

Reference DataReference Data

DTC and CUSIPS, etc Single View and Mappingto Industry Data Pools

Master Data Management22


16

Freddie Mac

Freddie Mac | A stockholder-owned corporation established by Congress in 1970 to

support home ownership and rental housing

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.

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.

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



17

33

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

Legacy Application Pain Points IBM Information Integration Value Customer Examples

GMAC Mortgage

Nordea

Lloyds Bank

Target Staging

IBM Information Integration Platform

TargetEnvironmentLegacy

R/3

R/3

R/3

R/3

Initial Staging

Define Relations

Standardize

Cleanse

De-Dupe

Map

Core Banking and Legacy Application Consolidation

Legacy

Legacy

Legacy


18

Nordea

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.

IBM DataStage™ and SAP R/3 PACK prepares data for the initial load into R/3.

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


Nordea | Largest financial services group in Scandinavia with EUR 252 billion in total assets, 9.7 million personal and 1 million corporate customers


19

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

Risk Management Pain Points IBM Information Integration ValueCustomer Examples

Ny Kredt

NASDR

Standard Chartered

AIG

Risk Management & Compliance

Meta Stage and DBMS

Extr

acti

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, V

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Data Integration

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Data

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Data Integration

Pro

file

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Da

taS

tag

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Result

Data

Result

Data

Result

Data

Result

Data

Data Marts

LossData store

FinancialData store

HistoricData store

EnterpriseData store

Core Banking Products

Credit

Collaterals

Customer

Loss Data

Data sourceTransformation& calculation

Rating, PD, LGD,CCF Models

BII OperationalRisk Engine

BII CreditRisk Engine

Interest RateRisk Engine

Internal CreditRisk Engine

Market Data

Reporting

Analysis

Disclosure

Management

Data

Meta Stage and DBMS

Extr

acti

on

, V

alid

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Data Integration

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Data

Qu

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Result

Data

Result

Data

Result

Data

Result

Data

Data Marts

Result

Data

Result

Data

Result

Data

Result

Data

Result

Data

Result

Data

Result

Data

Result

Data

Data Marts

LossData store

FinancialData store

HistoricData store


LossData store

LossData store

FinancialData store

HistoricData store


Core Banking Products

Credit

Collaterals

Customer

Loss Data

Data sourceTransformation& calculation

Rating, PD, LGD,CCF Models

BII OperationalRisk Engine

BII CreditRisk Engine

Interest RateRisk Engine

Internal CreditRisk Engine

Market Data

Reporting

Analysis

Disclosure

Management

Data

44


20

Standard Chartered

Standard Chartered | World- leading emerging markets bank with over 500 offices in more than 50

countries

Wholesale Bank Basel II Credit Risk Project goal 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 Solution leverages 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

“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


21


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

Single View Integration Pain Points IBM Information Integration ValueCustomer Example

JP Morgan Chase

Wells Fargo

Edward Jones

Mrs. M. Talber

John & Molly Talber

Molly Talber

Customer AccountsCustomer Accounts

M Talber

Depository Accounts

Credit and Lending

Investment Accounts

Product RecordsProduct Records

Mortgage, etcSingle View and Mappingto Industry Data Pools

Single View55


22

JP Morgan Chase

JPMorgan Chase | A leading global financial services company

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.

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



23

Channel Optimization

AuthoritativeDatabase

Source Systems:

•Kiosks•ATM’s•Call Centers•Internet •DDA Core Banking•Branches

Customer Inf File

IBM Information Integration Suite

No single understanding of customers or brands

Lack of a comprehensive view of data across systems


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

Channel Pain Points IBM Information Integration Value Customer Examples

Top Ten Brokerage Firm

New York Life

66


24

New York Life Insurance | Largest mutual life insurance company in the United States

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 visibilityby providing 10+ staff with adhoc reporting to completecustomer information Reduced IT costs by $130kannually by eliminating manualreporting Provided agents with 7x24detailed customer and policydata through secure web site


New York Life Insurance

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.

®

IBM Software Group


Websphere Data Integration Suite Technical Product Overview

John Bekisz


26

Delivering accurate, consistent, timely, and coherent business information

WebSphere Information Integration Solutions

Integrated Metadata

Integrated Metadata

StandardAPIs

Service-oriented Scheduled Event-driven

Connect to Data and Content

Find Federate Place Publish

Analyze Model Cleanse TransformInformationIntegrationServices

RTI

DataStageDataStage TX

MetaStage

QualityStage

ProfileStageAuditStage


27

The Ascential/IBM Data Integration Solution

Parallel Execution Engine

DISCOVERDISCOVER

Discover data

content and

structure

PREPAREPREPARE

Standardize, match, and correct data

TRANSFORM and DELIVERTRANSFORM and DELIVER

Transform, enrich, and deliver data



QualityStageQualityStage DataStageDataStage TX

DataStageDataStage TX

Meta Data Management

Service-Oriented Architecture

Real-Time Integration Services

Enterprise Connectivity

and Event Management

• 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


28

Data Transformation: 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

DataStageDataStage

Inputs

Transform

Quality

Output

DataStage Server

Multiple Jobs in Parallel


29

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

Tran

sfor

mTr

ansf

orm


30

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

Tran

sfor

mTr

ansf

orm


31

DataStage Architecture

High Degree of Reuse and version control at each level

We keep you in the tool

Transforms and Routines

Shared Container

Job

Job Sequencer

Tables


32

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

Tran

sfor

mTr

ansf

orm


33

Easy to Use GUI


34

Work as you think


35

Extracting from DB/2 into Hash Files


36

More Robust Example


37

Transformation


38

Example: MQ and QualityStage Integration


39



40



41



42

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.


43

Single Point of Control


44

Full job control


45

Produce and Consume Web Services

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

DataStage job


46

Real-time Integration (RTI) Services

RTI Services connection to Enterprise Applications, Portals, and Business Process Integration

“Always on”

DataStage Server 1

“Always on”

DataStage Server 2DataStage Server N

PlatformLoad

Balancing

Logging

AuthorizationEnterprise Java Beans

Authentication

Auditing

Java Messaging Service

Web Service Client

RTIAgent

Web Service Client

JMS Client

Java Application

“Always on”

RTIAgent

RTIAgent

RTI Input QualityStage

RTI Console

Ascential RTI Server – J2EE Environment

Enterprise Integration Suite

Transformer RTI Output

®

IBM Software Group


Thank You John Bekiszmobile: [email protected]


55

One Integrated Solution

Three Points of Integration GUI Server Metadata

Documents

® IBM Software Group © 2005 IBM Corporation Steven Fontana, IBM, Integration Specialist - Citigroup [email protected] 631-804-8888 John Bekisz,IBM, Integration