Reference Data Management Improved But Still Accounts For Two In Three Trade Fails, Says TowerGroup

The global securities industry has made significant progress on improving their management of reference data over the past three years
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The global securities industry has made significant progress on improving their management of reference data over the past three years, but reference data shortcomings still account for getting on for 60% of trade fails. Or so say consultants Tower Group.

TowerGroup’s 2005 Reference Data Survey – an update of its widely-cited 2002 Reference Data Survey – has found that reference data and data management projects are now rated as a top or high priority for financial institutions across both North America and Continental Europe and the UK.

“The majority of firms reported that they have already acquired funding for data management projects or have budgeted for them in 2006,” says Matt Nelson, the analyst in the TowerGroup Investment Management practice who headed the study and analysis. “Driving these projects is the industry’s collective concern over the risk associated with errant or inconsistent data, the desire to reduce manual processing, and the desire of firms to centralize data administration. Given that these pressures won’t subside and the frenetic pace of global regulation, Tower Group anticipates seeing a continued high level of focus and spending on data management projects through 2008.”

The survey was distributed by TowerGroup in late 2005, among financial institutions in both North America and Continental Europe and the UK. Respondents to the survey included fund managers, including five of the top 10 global managers with over US$11 trillion in assets under management; banks, including five of the top 10 global custodians with over US$26 trillion in assets under custody; and broker-dealers, including four of the top 10 global broker-dealers with over US$250 billion in capital employed.

Highlights from the survey include:

– While failed trade rates have declined below 10% on average, inaccurate or inconsistent reference data and poor data management processes continue to play a significant role in failed trades, accounting for nearly 60% of all failed trades.

– Institutions’ cost expectations for reference data management projects have decreased from US$3.5 million in 2002 to US$3.1 million in 2005, with an expectation that results will be realized from these projects in roughly one-and-a-half years. The decrease in cost expectations is a result of the growing maturity of data management software solutions and the decrease in the cost of content.

– Institutions continue to consume significant amounts of vendor data content and on average, expect to keep this amount consistent or to increase it in the future. This is in stark contrast to 2002 when institutions were keen to decrease their vendor content under budget pressure.

– A key variable in the reference data marketplace is the confusion surrounding data standards. No clear leader emerged as the consensus choice for an internal data standard in the survey, and a significant percentage of participants indicated that they are pursuing proprietary data models.

Nelson noted that another indicator of change is an increasing interest in managed reference data solutions (MRDS), the fully outsourced reference data management model.

“Early adopters have agreed to major MRDS deals, leading other institutions to begin assessing their own needs with an open mind toward fully outsourcing their reference data management,” says Nelson. “While many unanswered questions remain about the details of this model, TowerGroup expects that more early adopters will wade into the MRDS waters, and that outcomes will be favourable from operational and economic perspectives. This will ultimately drive further activity around outsourcing.

Reference data is the information underlying a financial trade that defines the customer, the instrument, the nature of the deal and how settlement should occur. The effective management of reference data is essential to automating the entire lifecycle of a financial trade and supporting the large number of data-intensive, downstream applications.

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