Reference data is the static information describing assets and account entities.
Reference data includes information describing securities, accounts, brokers, custodians, currencies, exchanges, countries, and issuers. Reference data does not include real-time and historical market data such as prices, corporate action notices, and index information.

There is wide inconsistency in how the industry currently handles reference data. In many cases, the entry and maintenance of reference data is still a manual process. Silos of reference data are often maintained in stand-alone applications that exist across multiple products or geographical locations within a firm. Data is represented differently in each application or location, resulting in redundant and often inconsistent data throughout the organization.

Inconsistent and inaccurate reference data results in a number of problems:

• Major cause of failure to achieve STP.
• Cause of nearly 50% of exception trades.
• Increased operational risk and costs due to failed trades.
• Excessive cost due to duplication and reconciliation across multiple applications and locations.
• Costly manual intervention in the maintenance process.

Reference data management has become a top priority of the industry:

• 67% of Technology Council members consider it a top priority.
• Many firms are centralizing the data administration process on either a department or enterprise-wide basis.
• There are a number of industry and vendor-based initiatives focused on improving reference data management.

New reference data management systems are emerging from vendors:

• At least 20 vendors offer reference data management products.
• Most are very expensive, exceeding the cost of a top-tier portfolio management and accounting system.
• Two vendors provide reference data audit services that are useful for uncovering reference data problems prior to initiating a large project.

The industry wide requirements for improving reference data management are significant:

• The adoption of standards for securities and counterparty identifiers, data communication (XML, MDDL, etc.) and technology.
• Consensus on internal business practices and benchmarks for data quality.
• The separation of reference data from business logic.
• Implementation of strict controls and security measures to minimize data errors in down stream applications.

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