Presentation by Credit Suisse at the MDM & Data Governance Summit New York, October 2012

1. Multi-Domain Enterprise Reference Data
MDM & Data Governance Summit – New York 2012
16 October 2012

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Credit Suisse Overview
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Credit Suisse provides companies, institutional clients and high-net-worth
private clients worldwide, as well as retail clients in Switzerland, with
advisory services, comprehensive solutions, and excellent products.
• Active in over 50 countries
• 48,000 + Employees
• Pre tax income: CHF 3.2 Billion (2011)
Organized into:
• Private Banking
• Investment Banking
• Asset Management

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Reference Data
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Reference Data
Any foundational data that provides the basis to generate, structure, categorize, or describe
business transactions; and is the basis to view, monitor, analyze and report on these
transactions.
Examples
• Client, Counterparty
• Chart of Accounts
• Booking Codes
• Product
• Legal Entity
• Organization
• Currency
• Calendar
Market Data
While Market Data can be considered a sub-type of Reference Data, it is treated separately
because of its unique low-latency (real time) requirements.
Why is Reference
Data Important?
Reference Data is a core asset of the bank which should be managed and governed in a
systematic fashion. Reference Data impacts most aspects of the banks operations. When
reference data is not used consistently, with commonly understood semantics and sources, it
will lead to multiple points of entry/updates resulting in manual fixes and downstream errors.
Business Imperatives Technology Imperatives
• Take ownership of data and its quality
• Provide information by adding context to data
• Ensure consistent usage across business processes
• Eliminate manual fixes and workarounds
• Meet regulatory requirements
• Transform data into information asset
• Reduce number of point to point interfaces
• Increase re-use using managed interfaces
• Reduce complexity by eliminating complex data flows
• Enable Business to view information instead of data by
providing appropriate tools and technology
• Support Operational Independence
• Provide Multi Entity Capabilities

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Current Challenges
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Reference Data
Challenges
• Inconsistent views of reference data used by different applications lead to incorrect &
inconsistent business metrics & reports.
• Multiple sources for a single reference data class (e.g. Counterparty) lead to confusion,
inconsistent representations of reference data.
• Poor understanding of reference data sources leads to multiple systems acting as
reference data enrichment and distribution points, increasing complexity and decreasing
consistency.
• Lack of governance for reference data means no clear ownership and no consistent
quality control processes for many reference data classes.
• Complex data flows and poorly understood data dependencies
Examples
• Different versions of Book codes used within Risk and Finance
• Different Legal Entity hierarchies (out of synch when changes are made)
• Different MIS hierarchies (over 500 versions currently stored)
Reference Data Interfaces Legacy Interfaces to/from Risk and Finance
• PeopleSoft GL is a large provider of reference data
today
• It provides 740 reference data feeds, including
• GL Accounts
• Consolidation Accounts
• Book
• Org Structures, and others

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Vision: Multi-Domain Reference Data Strategy
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Vision
To implement a multi-domain reference data management capability that provides
consistent, validated, well-formed and well-governed reference data, for all reference data
domains (classes) owned and managed by Back Office IT.1
Business Value
• Providing accurate, consistent reference data will reduce reporting and analysis errors
caused by incorrect reference data, and will reduce the overall cost of managing and
governing reference data.
IT Architecture
Value
• Significant reduction in the number and complexity of reference data interfaces, and
simplification of application logic as all reference data management functions are
centralized in a reference data hub.
1.Excludes Product and Client reference data
Common Data Model
Ensure a common understanding of our
data and how it should be used. Introduce
a framework to organize our complex data
landscape
Define our data
Central Platform
Central Governance
Make the right data easily accessible
at the right time
Central data governance ensuring clear
ownership and correct usage of the
shared data across the divisions
Control our data
Share our data
Objectives

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Vision: Future State
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Future State
High re-usability of data
objects
Use of “true” MDM tools for
reference data lifecycle
management
Reduced investment in
personalized engineered
hardware solutions
Transparent routing and
entitlement
Consistent semantics
Consistent data management
framework
Business Impact
Eliminate Interpretation Risk
High levels of automation
supporting authoring,
stewardship, governance
Consistent user adoption
Lower cost; lower innovation
threshold
Increased data quality
Integrated data
Flexible IT investment

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RDH as a Shared Component Across Our Architecture
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Reduce Complexity & Improve Efficiency through use of common technology components across
organizational domains.
Risk
Finance
Corporate Services
Data Warehousing
RDH
• Addresses data quality,
data standards
• Eliminates “resellers” of
reference data
• Offers a single version of
the truth
• Centralizes reference
data functions for lower
cost of ownership

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Defining Our Data – Reference Data Terminology & Taxonomy
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Organizational
Structure
Entity
Agreement
Ledger
Economic
Resource
Party
Product/
Service
Subject AreaData Domain
Classification
Codes
Org Unit CS Division MIS Unit Department Regions
Legal Entity
Servicing
Entity
Jurisdiction
Client regulatory Approvals
Standard Settlement
Instructions
Legal
Contracts
Chart of
Accounts
Trading
Book Info
Premises
Counter-
party
Client
Financial
Market
Stock
Exchange
External
Bodies
WorkerVendor
Financial
Instrument
Product
Framework
End of Day
Prices
Corporate
Actions
Issue RestrictionsIndices
Formulas Valuations Currency Reference Rates
Reference Data Classes
Currency
Code
Country
Code
Calendar
Language
Code
Industry
Code
Time
Zones
Locales
Transaction
Types
Instrument Credit rating Credit Suisse Rating
Tax
Category
Master Data
Structural Data
Classification
Data
Organization
Enity (OE)
Terms & Conditions

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Defining Our Data – Common Data Model
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Business Glossary Business Object Models
Logical Data Models
Service Data Models
Business Glossary of target design
describing definition, usage,
ownership and data governance
aspects for reference data class
data elements.
Business Object Models
describing relationships and
dependencies.
Logical Data Models
To drive the development of the
Service Data Models.
Service Data Models
for distributing data as a SOA
service to consumers.

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Control our Data - Governance for Reference Data Management
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Our Approach
Approach
• Minimum Governance Model defined
• Sourcing
• Definition
• Management
• Distribution
• Data Quality
• If minimum governance is met, approved as a managed interface to Golden Source
Opportunistic
• Use every opportunity to push data governance
• Couple of serious issues related to data quality that was escalated to ExB. Used
this to setup a STC comprising of CFO, CIO and GC and a Governance Board of all
COO’s in Back Office
• Regulatory push to handle contract data as reference data. Used this to include IB
in the Data Governance Board
Focus on Value-Add
• Avoided the pitfall of trying to define organizations and roles (viewed as too academic)
• As long as Minimum Governance Model is implemented, it was good enough, thereby
avoiding lengthy discussions of who should be called what (Data Steward, Data Tsar,
Data Provider, Data Owner, Data Governance, Data Conference etc.,)

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Share our Data - Target Technology
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Orchestra Networks
EBX from Orchestra Networks selected as standard tool for managing Structural and
Classification reference data.
• Selected after Gartner vendor short list and RFP process completed Dec. 2011
• Approved by Architecture STC for Structural and Classification data
• Offers configuration-based tool with little to no coding required
• Provides robust support for data governance, with workflow that can be adapted to our
business operating model
• Also selected by Asset Management for their client and product MDM tool
Operational Pilot
• Operational pilot completed in April, 2012
• Gain detailed understanding of production footprint, configuration requirements, time to
market considerations, and integration with other CS tools and platforms.
Broader
Opportunity
• Opportunity exists to leverage this technology investment to support Master Data
management, addressing the challenges of PB and IB
• E.g. managing derivative contract content (IB contract life cycle management
initiative)
• IB Client Data Management program is evaluating Orchestra Networks and assessing
its suitability for their requirements

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Share our Data - Target Technology
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Analysis based on Product Risk and Vendor Risk. Product Risk is based on market success of the product
and the maturity of the market. Vendor Risk is based on the reputation and stability of the Vendor
High Risk • No market penetration
• Beta version
• E.g., Oracle Fusion Products
Product Risk
Low Risk • Stable product with very high market
penetration
• Mature market
• E.g., Oracle Database
Medium
Risk
• Stable product with medium market
penetration
• Growth mode
• E.g., Oracle Universal Content Management
High Risk • In conception stage. No Enterprise customers
• Not profitable. No cash flow
• Unknown in the market place
Vendor Risk
Low Risk • Stable company with high revenues and stable balance
sheet
• Well recognized in the market place
Medium
Risk
• Has multiple enterprise customers using the Vendor
• Is profitable with a positive cash flow/Risk of being
acquired
• Recognized by analysts/markets as viable alternative
Product Risk Profile is Medium
Orchestra Network’s EBX product was short listed #1 by Gartner
Vendor Risk Profile is Medium
Used in BNP Paribas and various other banks/industries
Mitigation Mitigation
• Vendor relationship with the competency center to help evolve
the product and future direction
• Ensure single code base is maintained across customers
• Provide references to other clients (already done with Citibank and ANZ)
to increase market share
• Provide visibility to vendor with speaking engagements at conferences
(currently being done)

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Reference Data Onboarding Strategy (1 of 2)
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Ref Data Hub
Authoring
Management
Governance
Distribution
Data Stewards
Governance Body
Consuming
Apps
Consuming
Apps
Match/Merge
Authoring
Optional
Ref Data Hub
Authoring
Management
Governance
Distribution
Data
Stewards
Consuming
Apps
Consuming
Apps
IB & PB Ref Data Prgm
BO RDH Prgm
1.Multiple
Reference Data
Sources
(e.g. Client,
Product)
• Multiple sources for the same
reference data class require
(potentially sophisticated) Matching
(de-duplication) and Merging (attribute
survivorship) capability
• Authoring (creating of new instances)
remains with the sources
• Management and governance takes
place in the hub, with optional
feedback loop to the sources of record
• All consuming apps acquire from Ref
Data Hub
2.Authoring
External to RDH
(e.g. Currency,
Industry Codes)
• Ref Data Hub acts as golden source;
source of record is external to RDH
(can be external to CS)
• All authoring and management (e.g.
hierarchy maintenance) performed by
data stewards in source of record
• Ref data is loaded into Ref Data Hub
on a periodic basis
• Governance activities take place in Ref
Data Hub
• All consuming apps acquire from Ref
Data Hub

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Reference Data Onboarding Strategy (2 of 2)
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Ref Data Hub
Authoring
Management
Governance
Distribution
Consuming
Apps
Consuming
Apps
One-Time
Load
(Optional)
Ref Data Hub
Authoring
Management
Governance
Distribution
Data
Stewards
Governance Body
Consuming
Apps
Consuming
Apps
Ref Data Hub
Authoring
Management
Governance
Distribution
Data Stewards
Governance Body
3. Simple
Authoring in
RDH
(e.g. GL COA,
Calendar)
• Ref Data Hub acts as source of record
and golden source
• Optional initial data load from external
source
• All authoring and management (e.g.
hierarchy maintenance) performed by
data stewards in Ref Data Hub
• Governance activities take place in Ref
Data Hub
• All consuming apps acquire from Ref
Data Hub
4. Complex
Authoring in
RDH
(e.g. Book)
• Complex management processes (e.g.
complex workflows) require a two-step
onboarding process
• Initially, existing source of record is
used, and ref data is loaded into hub
for governance and distribution
• Later when sophisticated management
processes have been implemented in
Ref Data Hub, it becomes the source
of record, eliminating dependency on
external source.
• All consuming apps acquire from Ref
Data Hub

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Reference Data Adoption Strategy
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• The existing Golden Source
systems have a large number of
point-to-point interfaces
• The majority of consumers are
sourcing data from a non-golden
source system which leads to
reduced control over the quality
and timeliness of the delivered
reference data
• Our adoption strategy will first
focus on significantly reducing
existing point-to-point interfaces
and maintenance costs by
migrating inter-domain
consumers directly attached to
the Golden Sources
• As a second step, we are
planning to connect existing
Data Hubs to the RDH. This will
immediately provide high quality
and timely data to a large
number of consumers
CurrentState2012-2013Focus

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Reference Data Hub – Goals for 2012
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Initiative Data Classes Description
Corporate
Structural Data
• Worker
• Facilities
• Organization
• Reference data available in RDH
• 2012 focus is on adoption
• 84 consuming systems identified for initial
migration
Strategic Risk
Program
• Book • Reference data available in RHD
• 2012 focus is on adoption
Contract Lifecycle
Management
• Contract Data • Focus is onboarding and adoption
PB Platform
Renewal and MEC
• Language
• Calendar
• Regions
• Division
• Focus is onboarding and adoption
OnePPM • Project Portfolio
• Product Portfolio
• Focus is onboarding and adoption
OneGL • GL Chart of Accounts • Focus is onboarding and adoption
• Locale/Country
• State
• Currency
• Servicing Entity
2012 Goals
• A true horizontal service to provide/consume reference data across BO
IT, eliminating the need for disparate reference data hubs
• Standardized process for deploying Reference Data
• Align with major initiatives/functions to supply required reference data

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Lessons Learned
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Governance
Challenges
The challenges of implementing Data Governance
• Top Down
• Getting a dedicated data governance organization has been challenging
• No pushback on the idea but hard to decide who takes responsibility, how to fund
the central group and the business case
• Bottom’s Up
• Standard answer “Everything is working fine”
• Hard to get visibility into manual workaround and fixes being done and relating to
data quality issue
• The cynical response being data governance is hard and selecting a preferred
approach or standard often boils down to making a pragmatic decision between sub
optimal options
• The lack of data governance “maturity” complicated by the demand for “one bank data”
– clear data visibility and accountability between front office and back office
Application
Engineering
Challenges
Defining a clear roadmap for application design change
• Assessing the degree and appetite for change: migrating reference data as a function
of individual applications to leveraging a common component used across our sweet of
applications
• Developing “data adapters” to bridge strategic service data models to legacy point to
point interfaces to manage the risk associated with change
• Establishing the right metrics to measure progress and to drive the business case for
change
Summary
Never let a crisis go to waste
• Regulation is the new factor here – this is a genuine opportunity to change the way
reference data is sourced, managed and distributed

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Q & A