About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

Will SLA’s be Re-Evaluated After Tumultuous Times Highlight Response Issues?

Subscribe to our newsletter

Service level agreements were a key topic in this morning’s roundtable discussions at FIMA 2008, with one data manager at a Tier 1 financial institution suggesting that many SLA’s are now likely to be revisited in order to achieve better responses from their data suppliers after the current market conditions highlighted the need for faster answers to questions from the vendors.

SLAs between data vendors and their financial institution clients can become elaborate, but the more elaborate they get, the more it will cost to support, said a major vendor representative. When agreeing SLAs for offshored services, it is also essential to look at other factors such as time zones and turn around times on queries. But what is essential in crafting an SLA, is to focus on the key points of service that you would like to achieve, rather than trying to cover everything.

While vendors will not provide any guarantees on the accuracy of the data itself for a number of reasons, what they do provide is guarantees on the level of service they provide, in areas such as reacting to exceptions. So there is a certain level of responsiveness that is required – such as a response within an hour for up to 20 requests in the hour – to satisfy the SLA agreement.

The vendor/client SLA is usually a subset of SLAs that the client has with its own clients, said a buy side data manager in the discussion. When he is evaluating data products, the criteria are cost, coverage and service, with service receiving the largest weighting. But this is then pushed back by his company’s executives who put more emphasis on cost and coverage. So it’s necessary to find a balance between them among suppliers.

Interestingly, the major vendor said that analysing metrics over a long period of time, like 24 months to see which vendor is right or wrong on a piece of data, the average is between 48.5% to 51.5%. In other words, all vendors have a similar level of errors averaged out across market segments, sources or processes.

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: How to organise, integrate and structure data for successful AI

Artificial intelligence (AI) is increasingly being rolled out across financial institutions, being put to work in applications that are transforming everything from back-office data management to front-office trading platforms. The potential for AI to bring further cost-savings and operational gains are limited only by the imaginations of individual organisations. What they all require to achieve...

BLOG

Innovative Systems Wins Best Data Solution for Regulatory Compliance Award at A-Team Group’s DMI USA Awards 2025

Innovative Systems has won the award for Best Data Solution for Regulatory Compliance for its FinScan Enhance solution in the Data Management Insight USA Awards 2025. The awards recognise established providers and innovative newcomers who offer solutions that are providing leading data management solutions, services and consultancy to capital markets participants across Europe. Winners are selected...

EVENT

Buy AND Build: The Future of Capital Markets Technology

Buy AND Build: The Future of Capital Markets Technology London examines the latest changes and innovations in trading technology and explores how technology is being deployed to create an edge in sell side and buy side capital markets financial institutions.

GUIDE

Regulatory Data Handbook 2025 – Thirteenth Edition

Welcome to the thirteenth edition of A-Team Group’s Regulatory Data Handbook, a unique and practical guide to capital markets regulation, regulatory change, and the data and data management requirements of compliance across Europe, the UK, US and Asia-Pacific. This year’s edition lands at a moment of accelerating regulatory divergence and intensifying data focused supervision. Inside,...