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Chinese New Year Dinner

Chinese New Year Dinner. Reference Data Management – presented by Andrew MacArthur of RBC Capital Markets. Upper Canada Overseas Section February 20 th 2013. 1. AGENDA. Welcome Address by Chair Talk by Andrew MacArthur of RBC Capital Markets Dinner. 2.

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Chinese New Year Dinner

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  1. Chinese New Year Dinner Reference Data Management – presented by Andrew MacArthur of RBC Capital Markets Upper Canada Overseas Section February 20th 2013 1

  2. AGENDA • Welcome Address by Chair • Talk by Andrew MacArthur of RBC Capital Markets • Dinner 2

  3. Reference Data ManagementAndrew MacArthurDirector, Strategic Data Services, RBC Capital Markets 3

  4. What Is Reference Data • Some qualities of reference data… • once created, relatively slowly changing • referenced from many places • often referenced via a commonly known unique identifier • errors have impacts across multiple areas • Some examples… • clients • securities • trading books

  5. What Isn’t Reference Data • The dividing line can be thin, but qualities may be: • 1 instance per commercial transaction undertaken • joins to things considered reference data rather than vice-versa • will often not change once created (clumsy trader errors aside!) • Examples being: • trades • telephone calls • call reports for sales staff • cashflow information • financial statementsz

  6. Why Is Managing It Hard • The unique identifiers can be inconsistent – if they exist • Securities have many identifiers – RIC, Ticker, ISIN, CUSIP • Clients are more limited – BIC, LEI, BB ID • Clients and Issuers are inconsistently treated • Identifying and applying updates • Many data sets have established industry services – Bloomberg, Thompson Reuters, S&P, Moody's • They represent issuer, but don't expand to all legal entities • Processing a change is subjective. Name change vs take over • There is no automated solution. Specialist roles are currently unavoidable. These people are as common as unicorns

  7. What Happens When It Goes Wrong • External visibility • Interactions with clients may fail. The impact could vary from a minor reputation issue to a significant monetary loss • Potential risk around regulatory reporting (US Person) • Internal challenges • Many data sets have established industry services – Bloomberg, Thompson Reuters, S&P, Moody's • They represent issuer, but don't expand to all legal entities • Processing a change is subjective. Name change vs take over • There is no automated solution. Specialist roles are currently unavoidable. These people are as common as unicorns • Change initiatives become very difficult

  8. Technical Strategies • Golden Source approach • Create a single platform, typically comprised of data loaders, data repository, GUI and data distribution components. • The data repository model should represent the union of all reference data required for • How to implement the strategy? • Data model – how to model current state vs target state • Back population – what are existing trusted sources and how can you measure this • As an interim state, consider data aggretation / golden copy • Before distribution, you can use the golden source/copy as a control point for enterprise data quality

  9. Business Strategies • Data ownership vs data stewardship • Create a data steward. They are responsible for the process of managing the data and identifying owners • Data owners are responsible for the data content. This may be as small as a single field vs a whole data class • How to make the organisation realise the benefits • Identify the existing pain points • Inefficient data management. Duel keying (not a spelling error) • Knock on effects. Capital provision, liquidity, trading limits • Be realistic in what you can achieve • Don't over sell what you can deliver. Very few reference data strategies can be achieved unilaterally

  10. Order To Chaos • Imagine an organisation where… • Every piece of important data is only created or modified only once • Where there is always an obvious and trusted source for the data you require • There is a well defined process for extending the data sources when new process require this, with associated integration and testing platforms • No one argues about what “right” looks like • Strategic change is based on a solid foundation of well defined, easily identifiable data

  11. Questions • There must be some!

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