White Paper Reference Data Management in Banking ENABLEIT LLC, Namir Hamid

Transcription

1 White Paper Reference Data Management in Banking ENABLEIT LLC, Namir Hamid Oct 2011

2 Table Of Contents EXECUTIVE SUMMARY... 3 Overview... 3 Background... 3 CONSOLIDATION OF MARKET AND COUNTERPARTY DATA... 4 Market Data Integration... 4 Counterparty Data Integration... 4 Challenge 1: Buy or Build... 5 Challenge 2: Cleansing... 6 Challenge 3: Distribution... 6 Leveraging external vendors for reference data... 6 Page 2 of 7

3 Executive Summary Overview Reference Data Management at this time is being recognized as being one of the critical aspects of a corporate financial entity as consisting of a set of distinct categories. Different categories of data have their own unique characteristics. If these characteristics are not properly understood, practitioners will only be able to use generic approaches that are unlikely to deliver the desired results and may even be doomed to failure from the start. Projects that approach reference data as just "data" and fail to address its unique needs are likely to encounter problems. This is particularly true if such projects experience scope creep that leads them into a very different realm of reference data management. Data can be separated into a number of different categories, arranged as layers. Perhaps the most important category is the transaction activity data. This represents the transactions that operational systems are designed to automate. It is the traditional focus of IT, including things such as positions, portfolio and trades. Next comes, transaction audit data, which is data that tracks the progress of a trade transaction, such as trader name and time of execution. Above transaction activity data is enterprise structure data. This is the data that represents the structure of the enterprise, particularly for reporting business activity by responsibility. It includes things such as organizational structure and charts of accounts. Enterprise structure data is often a problem because when it changes it becomes difficult to perform historical reporting. (For example, when a unit splits into two, each responsible for a distinct set of products, how do we compare their current product performance to their performance prior to the split?) Enterprise structure data is a subject in its own right, which, alas, there is not enough space to discuss here. Background In recent years, the twin pressures of achieving regulatory compliance and straight through processing (STP) have highlighted substantial redundancy and duplication of effort in the area of reference data management. Many buy-side and sell-side firms are uncomfortably aware of both the time and cost they devote to purchasing, cleansing, and distributing reference data, as well as the risks that arise when these tasks are not performed effectively, among them failed trades and lost revenue opportunities. Firms are now questioning whether or not there is sufficient value and adequate technology support to consolidate all their market data management and counterparty systems on a single platform. As a best practice market data and counterparty data should be acquired as a utility and mapped to reference data through cross-referencing. But a robust reference data management platform must underpin such efforts if they are to succeed. Recognizing that there is little competitive differentiation to be derived from managing reference data, many firms are turning to third-party specialist organizations to reduce unnecessary costs and operational risks while improving data quality. Best-practice reference data management may involve leveraging third-party specialist organizations, but only when the purchase, cleaning, and distribution risk points in the process have been considered. Firms first need to understand the gap that exists between their current reference data management processes and newer, best-practice approaches. Page 3 of 7

4 Consolidation of market and counterparty data The reference data management problem is shared by both the buy-side and the sell-side. Each has to worry about securities reference data to settle trades reliably and to provide fair valuations. The sell-side firms are now beginning to consider market data and counterparty data as part of the larger reference data problem. Firms that had invested in market data management and counterparty systems that were separate from the securities reference data management platforms are now questioning whether or not there is sufficient value and adequate technology support to consolidate all these systems on a single platform. Let us examine the issues with integration of market data management and counterparty systems: Market Data Integration Trading firms have for a long time been concerned with sourcing accurate market data on a real time basis with absolute minimum latency in order to feed algorithmic trading engines. Latencies are measured in microseconds and volumes in millions of messages per second, which is very different to the volume and speed characteristics of traditional reference data management. Only the largest Tier 1 firms can afford the luxury of storing their own tick histories, but for the majority, there are cost effective industry utility services such as Tickdata and Reuters DataScope Tick History, which provide clean tick history on demand. These can be used for proof of best execution and algorithmic back testing. There are providers of reference data management platforms who can integrate market data into a business process when provided as a feed to distribute, but cannot manage it in a real-time scenario. Firms should closely examine whether the additional costs of the more complex platforms and the cost of the actual tick data storage provide sufficient benefits when compared with less expensive securities reference data management platforms and tick data history utilities. Counterparty Data Integration Many firms have invested heavily in counterparty data management. However, through a spate of recent acquisitions, organizational, geographic, and functional silos have developed resulting in multiple databases in different formats. Fragmentation can go as far as having different counterparty databases even within a group, depending on the instruments concerned. Counterparty data is often viewed by firms as 'proprietary' and relatively static. In fact, it is neither. For one thing, it is either right or wrong and the 'right' data can be reliably sourced as a utility, for example, from Avox or Dun & Bradstreet. For another, around 20% of it changes from year to year as a result of M&A and corporate restructuring activities. For a firm, there is undoubtedly a benefit from integrating counterparty data in one place. Now that industry utilities exist where clean data can be reliably sourced, it is no longer a proprietary advantage. The advantage would stem from the uniform usage of that data source throughout the firm. In both cases, our view is that for most firms, market data and counterparty data should be acquired as a utility and mapped to reference data through cross-referencing. But a robust reference data management platform must underpin such efforts if they are to succeed. Page 4 of 7

5 Benefits of efficient reference data management The considerations argue strongly for the concept of a 'Golden Copy' of reference data to serve everyone in the company. In risk terms, the benefits of a more centralized strategy for the management of reference data include: Reduced trade failures Reduced costs related to manual rekeying of data and duplication of efforts Reduced operational risk Reduced number of missed trading opportunities. Best-practice reference data management may involve outsourcing, but only when the following challenges in the process have been considered: Challenge 1: Buy or Build The analyst firm, Burton-Taylor International Consulting, contends that organizations worldwide spent just over $23 billion last year in financial information and analysis provided by a wide range of specialist providers including Thomson Reuters (TRI), Bloomberg, Interactive Data Corporation (IDC), FactSet, and SIX Telekurs. In the case of reference data, it is common to buy multiple sets from multiple vendors, according to the needs and preferences of individual employees and teams within the organization. There is often good reason for this. Each financial data vendor has its strengths and weaknesses, so in the case of a particular instrument category, (for example, fixed income), there may be three or four suitable vendors. Internal politics may also come into play, with disputes over the 'best' (that is, most trustworthy) provider from which to buy reference data for a particular instrument category. For most organizations there is no single data vendor that has the full range of data coverage or depth of attribute types needed to satisfy all requirements nor is there a clear policy for combining that data into a single, meaningful view. This results in organizations spending money on duplicate, non-optimized data. They are not even sure of what they are buying, or where the data is being used. In some cases, reference data being purchased may not even be used at all, such as when a particular department decides to take data feeds from another source. Contract managers responsible for purchasing the data, however, may be reluctant to turn off feeds that they believe may be redundant because they are not sure if other departments in the organization are using the data feeds. Best-practice reference data management circumvents these purchase point risks by using advanced tools to track and analyze what data sets are being purchased and where they are being used, tracking the data path from contributor to consumer, and then monitoring by user and data element. Smart companies also turn to trusted independent partners to help them determine what data sources are right for the organization and its employees. Page 5 of 7

6 Challenge 2: Cleansing Reference data generally is non-optimized at the time of purchase and hence it needs to be cleansed in order to identify inconsistencies and faults. If reference data is held in multiple silos rather than centrally, it is likely to be cleansed multiple times. There is also the increasing impact of corporate actions on reference data to consider. The volume of corporate actions has ramped up significantly over the past year, yet when it comes to normalizing reference data to reflect the necessary changes they invoke, it has to be carried out across multiple silos. Some corporate actions are relatively easy to handle, such as splits and dividends. Others, like complex rights issues, require highly labor-intensive intervention from a specialist who understands what is being offered by the corporate action in question. In addition, corporate actions tend to be unexpected and brought into effect quickly, giving those responsible for reference data cleansing little time to respond. As a best-practice for reference data management, use automated tools that cleanse data by monitoring incoming data and checking for relationships and expected patterns. When exceptions occur, manual intervention may be required but smart companies use skilled staff in low-cost offshore locations to do this. Challenge 3: Distribution Once reference data has been bought and cleansed, it needs to be fed to the individual systems that consume it. That requires an in-depth understanding of what data sets and fields each consuming system requires, when the data is needed and in what format it is expected. Those requirements will vary hugely, according to the function the system performs, for example, NAV reporting, compliance reporting, collateral pricing, and so on. As a best-practice for reference data management, use ETL (extraction, transformation and loading) tools to subset and reformat the data contained in the 'Golden Copy', and send it to each consuming system in a way that it can be understood and used. When a request for a new feed is submitted, skilled personal are on hand to decide which fields from which data sets are required to build it. Leveraging external vendors for reference data Many firms have now realized that there is little competitive advantage to be gained from managing publicly available, highly commoditized reference data in-house. Increasingly, firms are turning to third-party specialist organizations, not only to manage reference data on their behalf, but also to re-architect data systems in such a way that they are outsourcing ready. Using a combination of best-of-breed tools and skilled resources, the third-party specialist will normalize, cleanse, validate, and aggregate multi-source content to achieve a single, standardized 'Golden Copy' of reference data which is fed back to the client as a managed service. We also need to consider the efficiency gains associated with higher levels of data quality. Reduced trade failures, reduced manual interventions, and reduced data rekeying would lower the operational risk. Our view is that there has never been a better time for financial firms to leverage third-party specialist organizations to manage commoditized reference data. A number of firms have now emerged that offer the competence and experience to handle reference data on your behalf. It is time for firms to consider whether they should continue to employ in-house staff to manage reference data in some of the world's most highcost financial centers or to entrust it to a provider that offers a more economically appealing alternative. The risks associated with reference data now are an immediate concern and require immediate action. Bestpractice reference data management is critical to current performance and a prerequisite for achieving further growth and efficiency. Page 6 of 7

7 References: Reference Data Management Advisory Committee Reference Data Management best practices July Latest trends in data banks the BankingTech Aug 2011 Software for Information Technology Page 7 of 7