Multi-Domain Enterprise Reference Data

MDM & Data Governance Summit – New York 2012

16 October 2012

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Credit Suisse Overview

2

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

Ob

jective

s

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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 Area Data 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 Worker Vendor

Financial

Instrument

Product

Framework

End of Day

Prices

Corporate

Actions Issue Restrictions Indices

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

Matc

h/M

erg

e

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

Curr

en

t S

tate

2

01

2-2

013 F

ocu

s

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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