Are FSIs leveraging reference data management to cut costs and manage risk?

Executive Summary
The reference data problem continues to confound the financial services industry. High levels of data siloization, multiple external vendors and disparate formats and lack of standards have led to poor quality reference data. This has stymied trade processing automation efforts on the one hand while also putting pressure on operating costs because of highly manual error rectification and excessive duplication. Further, the market uncertainty and new regulation such as the Patriot Act are driving the urgency to accurately identify and manage risks with respect to clients and different counterparties. Organizations spend up to 10% of their IT budget on data management and still find themselves fighting data quality problems. The business case for reference data management could not be more compelling.

The top 5 lessons from our reference data experience with several brokers, investment managers and custodians are as follows:

* A necessary and critical first step in reference data management is clean data. No amount of sophistication in your technology solution is going to make up for poor quality data. Continuing skepticism from the users about the integrity and quality of data will only result in a failed implementation. Get the data right, first and foremost. * While it is theoretically correct to aim for enterprise wide integration, it is practically more feasible to build your reference data solution step by step, prioritizing business applications and data types with the highest business impact, while keeping your approach and architecture open and flexible for enterprise wide integration. * Prioritize securities data and client/counterparty data for downstream business applications such as clearing & settlement systems and exposure management systems where the business impact of reference data will be the highest and where cost efficiencies will be realized rapidly. * Use a combination of build and buy and outsource to maximize your gains. Pure off the shelf products need heavy customization to fit with your organization. ASPs remain a question mark on the critical data sensitivity issue. * While the core application is best built to your specific requirements, there are potentially large savings to be realized from outsourcing the data and exception management operations to an offshore facility. Organizations employ up to 200 FTEs for these operations and are looking to save up to $10-12mn by outsourcing data management.

Reference data, rightly managed, presents a significant savings opportunity while also ensuring that your organization is equipped to manage risk and comply with regulatory requirements. Its time for every broker, investment manager and custodian to put reference data on their list of strategic technology initiatives.

The Challenge
Reference data, strictly defined, is the static information on securities and entities. In practice, the problem is often expanded to include market data and operating data central to risk management and management reporting.

Exhibit 1: Types of reference data

Reference data is the life blood of the trade process and used through the trade processing cycle.

Exhibit 2: Usage of reference data across the trade process

Source: Capco

The financial services industry faces a fundamental yet complex problem in reference data management. Even while trade processing automation reaches new levels, inaccurate and inconsistent reference data remains the single largest stumbling block to internal STP. Equally significant for a profitability challenged industry, are the burgeoning costs of manually maintaining and updating data, error rectification and maintenance of duplicate and often redundant systems. Add to it, the risk of underestimated or unknown exposure to clients and counterparties.

From our experience with firms across the industry, several reasons have contributed to creating the reference data problem. The most significant of them is data siloization - the Tower Group Sept 2002 survey showed that firms have 37 systems on an average containing the same securities and counter party reference data with 8% of the firms having more than 150 systems. These are most often disparate systems, geographically distributed with no common formats or standards. Duplication of data across these disparate silos coupled with manual and inconsistent updates result in multiple representations for the same data elements making data quality a huge issue.

Adding to the problem are multiple external sources of market data again with little standardization between them - on an average data is purchased from more than 7 different vendors. Surprisingly this is an area where there is little automation - manual updates are still the norm. Eventually the dubious data quality leaves every downstream user application with no choice but to have its own data correction and maintenance operation leading to high manual operating costs.

In this scenario, while most firms are recognizing the need to act, they're caught wondering where to begin.

The Approach
Typically, questions faced by CIOs looking to start acting on reference data management are in the following areas:

Exhibit 3: CIO Questions
* Should I aim for an enterprise wide solution vs. a step by step approach?

* How do I prioritize reference data types and user applications and locations?

* How do I evaluate technology solutions from bespoke developers vs. off the shelf products vs. ASPs?

* How do I phase out my investment and expedite the savings and benefits?

From our experience with brokers and investment management firms, while it is theoretically correct to aim for enterprise wide integration, it is practically more feasible to get a buy in from one or more business units and build your solution step by step while keeping your approach and architecture open and flexible for enterprise wide integration. A very significant challenge firms face for enterprise wide initiatives is the need to centralize data administration. This is an organizational challenge with significant change implications for organizational structures, hierarchies and roles. This is best dealt with once minimum scale has been achieved and the benefits and returns are more visible across the organization.

We have found it useful to undertake the prioritization of reference data initiatives based on two factors - where is the business impact likely to be higher and where can I start realizing the cost efficiencies sooner.

Exhibit 4: Prioritize business applications

On prioritizing between types of reference data, we believe securities data is the foundation of the trade process and critical to all business applications. We did find however, that banks and securities firms in Europe are focusing on improving their exposure and risk management practices and therefore according high priority to client and counterparty data. This will rapidly become the norm in the US as well, given the urgency created by Patriot Act and "know your customer" regulation. Investment management firms under heavy scrutiny from plan sponsors are prioritizing portfolio and performance data for more effective client and management reporting.

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