Reference Data Utilities -the only way forward Central Data Utility (CDU) Joe Turso May 13, 2014 © Euroclear SA/NV and SmartStream Technologies Company Confidential 1

Exploring commonality  What is common in data management?  For smooth and efficient functioning, many parts of the financial services industry rely on pre- and post-trade infrastructure  Market infrastructure aims to:  Mitigate risks  Mutualize costs  Increase operational efficiency  Post-crisis expanding scope financial markets infrastructure; recognition of data management as vital infrastructure and recognition of need for standards  How can we move data management over to a similar model? 4

A need for effective data management Data Costs Data Processing Costs -Direct -Indirect Pre Financial Services Industry Cost (Bn USD ) Research Sources: - Inside Reference Data - Burton-Taylor

Drivers for a new approach Changing data management drivers 1.Regulatory burden 2.Cost reduction 3.Operational control 4.Standardisation 5.Technology and Outsourcing Data management value chain  Timely decision support  Best execution and cost effective clearing  Transaction breaks and repairs  Regulatory requirements for data integrity  Compliance, risk and financial reporting  Enable legacy infrastructure efficiencies

Parameters of a data management business case Risk & Financial Reporting Downstream Remediation Data Operations Direct Processing

Principals of effective data management Multiple orientations to a single version of the truth Standard should be implemented upstream Promote a common data definition Data is a corporate asset Enable business links and controls Data traceability

Data management landscape Instrument Life Cycle Market Event Monitoring New Instrument Setup Instrument Events:  Corporate Actions Events  Factoring Behaviours Identification, Relationships and Hierarchies Trade Life Cycle Order Management Trade Execution Risk Management P&L Computation Settlement Expiry Cycles Data Management Processes Change MonitoringTrigger Validation Model Validation Enrichment & Supplementation Data Assets Data Governance Organisation Information Owners Information Stewards Information Consumers Sources Licences Repositories Licences Masters Operations Reports Data Change Policy

Standardization and flexibility in a Data Utility engagement 4 Multi-tenanted utility driven Data Management platform Hardware, IT Infrastructure and BCP Data feed Handlers & Normalisation Logic Data Model; Data Types, Asset Classes and Dependencies Quality Driven Rules Libraries Cross Referencing, Data Dependencies & Linking Level 1: Exception Management Level 2: Root Cause Investigation Level 3: Research & Analysis Client Rules, Logic & Hierarchies Client Symbology Referencing Internal Data Flows Client Histories, Storage & Audit Standards Technical Consumptions & Integrations Operational Shift Development Shift Technical Lift/Hosting Client Instrument Setup Processes Client Level 1, 2, 3 Support Mutualising Standards Proprietary Processes Outsourced Resources Utility Client Processing Industry & Utility Processing Variable Level  Client defined integrations & XRef  Individual & Unique flows supported by its purpose built architecture and Global Operations team Custom Level Resources to support on-site activities and infrastructure Utility Level  Best practices driven  Integrated technology, logic & operations  Client defined sourcing  Evolving processing capability to stay ahead of:  New regulations  Market changes Quality and Timeliness Driven SLA Managed Service SLA Transition Optimisation

Accelerate delivery through a Data Utility Customer Requirements Take-on Transition and Transformation Customer Requirements Unknown Elapsed Time CDU Capability CDU Delivery Immediate potential for best practice driven quality and timeliness improvements Unknown Deployed/ Managed Service/ Outsource Option to build delivery Build out causes unnecessary delivery risk  Platform build  No operational best practices  No integrated operations Time to market lag

The road ahead  What is different today?  Different competing approaches  Scope and potential limitations  What is next? 4