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

Data Management Summit Innovation Showcase Presents The Investment Data Utility

Subscribe to our newsletter

Join us tomorrow at our London Data Management Summit to find out how to make data an opportunity rather than a risk, what still needs to be done to ensure Markets in Financial Instruments Directive II (MiFID II) compliance, how to make the most of alternative data and next generation analytics, the latest technologies adding a new dimension to data management – and more!

Looking at emerging technologies and their vendors, the Summit will include a data innovation showcase. We caught up with Robin Strong, founder and CEO of The Investment Data Utility, ahead of the event to find out how his company can help resolve some of today’s data management challenges.

Q: What data management problems do financial institutions have that you believe you can solve?

A: Unknown, probably poor, data quality across the business, the inability to measure data quality versus peer groups, and incorrect data that is not spotted until it’s too late – failed trades, skewed risk reports, compliance breaches and so on.

Q: Why do financial institutions have these problems?

Typically, because data comes from disparate sources and is processed by different systems in different ways. Even a well-produced ‘gold copy’ lacks any meaningful comparison point to assess its quality.

Q: How do you solve these problems?

A: I have developed crowdsourcing, collaborative technology that allows data to be compared across institutions, generating an industry benchmark that identifies incorrect data before it is used by the business.

Q: What technology do you use?

A set of proprietary algorithms implemented using low-cost standard software tools linking to a central processing cloud.

Q: How does your solution fit into a financial institutions architecture and data flows?

The beauty of this model is that it does not impinge on existing toolsets, workflows and governance processes. It can be added as an additional layer in the architecture at low cost, resulting in very high ROI.

Q: Which emerging technologies do you see as having the most potential to improve data management and why?

I am a big believer in industry standards, yet so many firms reinvent the wheel with proprietary tools to store, reconcile and govern data. Industry collaboration is key and unless operational costs are reduced, new entrants will establish lower overhead models to undercut the competition.

Subscribe to our newsletter

Related content

WEBINAR

Upcoming Webinar: Optimising cloud, marketplaces & managed data services

Date: 30 June 2026 Time: 10:00am ET / 3:00pm London / 4:00pm CET Duration: 50 minutes Financial institutions are under mounting pressure to rethink how they source, manage and distribute market data. Rising data volumes, multi-cloud adoption and the operational demands of regulations such as DORA are exposing the limits of legacy infrastructure, and driving...

BLOG

New Issue IQ and Boltzbit Partner to Slash Bond Issuance Data Processing Time by 74%

New Issue IQ, the solutions vendor dedicated to modernising primary bond markets, has announced a strategic partnership with deeptech AI company Boltzbit, to optimise the processing of new bond deal information. The collaboration reportedly delivers a processing-time improvement of approximately 74% by automating workflows that have traditionally been manual and fragmented. Through this integration, New...

EVENT

TradingTech Summit London

Now in its 15th year the TradingTech Summit London brings together the European trading technology capital markets industry and 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

AI in Capital Markets Handbook 2026

AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...