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Credit Suisse: Multi-Domain Enterprise Reference Data
 

Credit Suisse: Multi-Domain Enterprise Reference Data

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

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

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    Credit Suisse: Multi-Domain Enterprise Reference Data Credit Suisse: Multi-Domain Enterprise Reference Data Presentation Transcript

    • Multi-Domain Enterprise Reference Data MDM & Data Governance Summit – New York 2012 16 October 2012
    • WWW.MDM.SUMMIT.COM 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
    • WWW.MDM.SUMMIT.COM Reference Data 3 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
    • WWW.MDM.SUMMIT.COM Current Challenges 4 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
    • WWW.MDM.SUMMIT.COM Vision: Multi-Domain Reference Data Strategy 5 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
    • WWW.MDM.SUMMIT.COM Vision: Future State 6 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
    • WWW.MDM.SUMMIT.COM RDH as a Shared Component Across Our Architecture 7 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
    • WWW.MDM.SUMMIT.COM Defining Our Data – Reference Data Terminology & Taxonomy 8 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
    • WWW.MDM.SUMMIT.COM Defining Our Data – Common Data Model 9 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.
    • WWW.MDM.SUMMIT.COM Control our Data - Governance for Reference Data Management 10 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.,)
    • WWW.MDM.SUMMIT.COM Share our Data - Target Technology 11 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
    • WWW.MDM.SUMMIT.COM Share our Data - Target Technology 12 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)
    • WWW.MDM.SUMMIT.COM Reference Data Onboarding Strategy (1 of 2) 13 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
    • WWW.MDM.SUMMIT.COM Reference Data Onboarding Strategy (2 of 2) 14 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
    • WWW.MDM.SUMMIT.COM Reference Data Adoption Strategy 15 • 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
    • WWW.MDM.SUMMIT.COM Reference Data Hub – Goals for 2012 16 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
    • WWW.MDM.SUMMIT.COM Lessons Learned 17 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
    • WWW.MDM.SUMMIT.COM 18 Q & A