what needs real-time distribution and what does not; should workflows be introduced; how will success be measured – data accuracy, speed of account creation, access to information, budget control? What is ‘major data’ (counterparties, confirmation details, standing settlement instructions, organisational structure) and should be centralised and distributed; what is ‘minor’ or ‘supporting information’ which should not. Information that seldom changes or is specific to individual systems will probably be in the latter category. Many buy-side firms are among those facing major challenges. They have traditionally obtained their prices from counterparties, typically the investment banks, but with
trust having been eroded, they have needed to develop an independent pricing mechanism. Taking prices at face-value is no longer acceptable, but few have established independent pricing and valuation teams.
The situation has become ever more complex. For instance, the proliferation of trading venues, in part as a result of MiFID, means these need to be properly identified to ensure correct routing and pricing. MiFID has also brought other identification needs, such as whether an instrument is liquid or not. Entity data, often used in other parts of the organisation, now needs to be combined with instrument data, including for customer protection and audit trails. However, high speed market data is markedly different to slower speed feeds. One ray of hope amidst this daunting data challenge is that work done for one discipline is likely to be useful for others. When Rand Merchant Bank set about putting in place its new finance and risk architecture for its domestic and international operations, its data challenge had been lessened by work that had previously been done for KYC. There had already been a major project to clean up the bank’s data, including the move to a universal customer master. ‘So there was not that much dirty data,’ said the bank’s COO, financial services, Dirk Snyman.
Rand Merchant Bank
Speed has not traditionally been associated with reference data but there is now a need for much greater responsiveness to understand credit, market and pricing risk. The traditional method when wanting to understand an exposure was to run reports across trading and position-keeping systems. ‘Unless you are a Goldman Sachs, this tends to be a complex and difficult process,’ observed Larry Scott, president at Ness Technologies. This company has an offering called Financial Data Enterprise, a collection and aggregation engine with a data distribution mechanism. Somewhat ironically, one area that has often been neglected from a data perspective is derivatives. Data projects have tended to start with the high volume, commoditised sides of the business, such as equities and fixed income. Over time, banks have moved as well towards securities master repositories into which they have plugged their data sources and relevant applications, usually driven by a desire for improved efficiency. The derivatives operation has tended to be isolated from such efforts, with the dynamics of relatively low volumes and high value meaning that data issues were not considered and it was felt that trading and risk applications could handle them. However, there are often multiple systems and there are fragmented market infrastructures as well. The
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