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Data technology walkway

How data standards and global identifiers are changing the investment industry


July 2022

Data consistency remains a challenge when unlocking the value of identifiers, according to speakers at a recent webinar.

Data standards and global identifiers are the international language of capital markets but sometimes words still get lost in translation.

“People talk about things in different ways,” Nathaniel Dahm, Director, Content Strategy, Real-time Feeds, ICE Data Services, said. “And until that regulatory umbrella comes in, it can be a little bit of the Wild West on how people approach data problems and these inconsistencies can lead to bad trades and money lost.”

While data standards and identifiers are commonly used for accurate and timely regulatory reporting, it can power an organisation’s operational effectiveness, productivity and efficiency across many areas.

Dahm said a key issue for firms was to uniquely identify instruments using three common identifier schemes:

  • ANNA numbers such as ISIN, SEDOL (which is strongly embedded in the UK), and CUSIP (which is embedded in the US).
  • Exchange identifiers, which have their own unique naming schemes.
  • Vendor symbologies, such as the one that is used to power ICE’s Consolidated Feed.

Small firms tend to focus on one or two identifiers while larger global firms tend to use all three common identifier schemes.

Firms also have their own legacy systems and internal silos that add to the challenge of interpreting multiple standards, according to an audience poll.

What are the data management challenges of implementing data standards and identifiers at your organisation?

One speaker from a large global bank said regulation had helped drive adoption, such as MiFID II, which barred firms from trading derivatives if they didn’t use a Legal Entity Identifier (LEI). The LEI is now starting to be applied more widely across equities and fixed income securities, as well as the payments sector, but work remains to be done.

While the global bank uses AI-driven matching algorithms to cross reference bank records to vendor data, it still requires operational oversight to make decisions on identifiers that fall in grey areas.

Dahm said this often occurs after a corporate action such as a reverse stock split which may result in changes to the affected instrument’s identifiers.

“It's something that you can apply a lot of machine learning and automation to but you've always got to have that operational team behind it to do the data verification at the end of the lifecycle.”

A speaker from another bank said the business benefits of operational efficiency are usually what sells standardization, which was reflected in another audience poll.

Beyond compliance, what extent of business and operational benefits is your organisation gaining, or would it expect to gain, from implementing data standards and identifiers?

The global bank had successfully used counterparty reference data to overhaul a key operational process, using a matching algorithm to automatically search its database to confirm whether it already had a client record. It stops duplicate accounts being created and was a “game changer” for the bank’s onboarding.

Another executive from a bank said firms now had a deeper understanding about the value of data, which could reduce compliance costs and future proof the organization.

An audience poll suggested there is still scope for firms to exploit the value of data beyond compliance.

Beyond compliance, to what extent has your organisation implemented data standards and identifiers?

While standards continue to develop, the bank executive suggested an important future standard to watch was the Natural person identifier, which has many use cases, such as in payments.

The speakers suggested an optimal deployment of data standards should include:

  • A broad scope of identifiers.
  • Support for referential data fields to efficiently describe identifiers.
  • Efficient data management tools to enable automation.
  • Thorough documentation that includes definitions for identifiers and standards, as well as an ongoing focus on processes to ensure data quality.