Market data management in 2026: engage with the business

At every market data conference, artificial intelligence is now the dominant topic. Whether it is DKF, FIMA or FISD the conversation revolves around automation, machine learning and intelligent tooling. And rightly so. AI takes over many time-consuming and administrative tasks, from pulling start and end dates out of contracts to identifying delivery channels and extracting information from internal systems or external sources. But that creates a new question. What will you do with the time that frees up? Where should your focus lie in 2026 when it comes to market data management?

We believe the answer lies in the tasks that have received too little attention in recent years: speaking with the business, understanding how market data is actually used, and making better-informed decisions.

MDS at Work and BIQH have worked together in the market data space for many years. In this opinion piece, we want to highlight something that gets far too little attention. This is not about tools or cost control. It is about something more fundamental.

It is about the interaction between market data management and end users.

Market data management and the business need to talk more often

Many challenges arise from a lack of communication between market data, procurement and the business. Not because the business does not know what it is doing, but because costs, alternatives and contract terms are rarely shared and discussed.

In some organisations, demand managers liaise closely with end users and play an important role in this communication, though this carries the risk that market data managers are not involved. In others, the role is missing entirely. Regardless of the setup, regular interaction with users provides insight into workflows, priorities and actual market data usage. This forms the basis for better-informed decisions. That is why, in the rest of this blog, we explore reasons to engage with the business more often.

Spreadsheets show costs, but not how market data works in practice

Many organisations still depend on spreadsheets to track allocations, where they even exist, along with costs and usage. That might give you a basic overview, but it lacks real context. You can see who is being charged for what, but not why it is being used, whether it is still necessary, or how well it fits into the workflow.

This often means licences and delivery channels remain in place long after they have stopped serving any real purpose. They continue to incur costs, even though no one remembers why they were set up that way to begin with.

Statistics can be helpful, but without the story behind them, they only tell part of the picture. That story rarely comes from the data itself. It comes from end users. True insight only comes when you understand how people are actually using market data and why they use it that way.

Value for money requires an understanding of the day-to-day workflow

Which market data vendor provides the right level of products and services? Which dataset delivers the best quality? When is it worth paying for premium quality, and when is a lower-cost alternative sufficient?

Because costs and alternatives are rarely discussed with users, the following situations are all too common:

• costly realtime market data being used where end-of-day would be perfectly sufficient
• high-cost terminals kept running for a single task
• legacy decisions that have never been reviewed
• a cheaper market data vendor ultimately delivering less value than a slightly more expensive one

Many of these decisions have simply developed over time and are not being challenged. For example, in their day-to-day work, MDS regularly comes across legacy data feed subscriptions that could either be removed or replaced with alternative data sources. This has the potential to reduce cost and manage exposure.

True value for money comes when market data managers understand what information is genuinely needed, why it is needed, and what level of quality supports that use.

Sometimes that means going with the more expensive option because it genuinely adds value. At other times, it means choosing a cheaper alternative because it has little to no impact on the business.

Conversations reveal inefficiencies you will not find anywhere else

In effective market data management, you come across patterns that never show up in systems but become immediately clear when you speak with users. Examples include:

• duplication of third-party data, where 70 percent of the instruments provided are identical
• delivery channels that are still being charged for, despite no longer being used
• licences that are no longer being utilised
• applications pulling the same market data multiple times when once would do

As long as the market data spaghetti is not untangled, these inefficiencies will persist. Duplicate data purchasing will continue to be a structural issue. A short conversation with users often provides more insight than weeks of managing spreadsheets.

A well-organised market data function works as a single unit

A strong market data function is built on close collaboration between all roles involved. Market data vendor management, procurement, administration and demand managers need to share information and work from a common understanding. For example, financial institutions may appoint Single Points of Contact (SPOCs) who meet regularly to embed collaboration across the organisation.

Without alignment, renewals are missed, contracts are automatically extended, requirements go unchallenged, and gaps may appear in compliance and allocation. With strong collaboration, you gain control through visibility into usage, value and risk. This results in stronger negotiations and more informed internal decision-making.

True strategic choices can only be made by understanding not just what is being used, but also why it is needed.

Start small: small steps deliver immediate results

You do not need to build mature market data management all at once. Small steps are often more effective and produce immediate results.
Examples include:

• holding interview-style meetings with key users
• conducting targeted reviews and challenges of specific licences
• applying profiling to understand how market data terminals and other services are used across teams
• testing an initial feed waterfalling/cascading strategy

In practice, small steps can deliver value straight away. Success helps build the momentum needed to optimise on a much wider scale.

Conclusion

Artificial intelligence makes market data work more efficient and more transparent. Instead of digesting hard-to-read contract paperwork, you can now ask questions and compare contracts using AI. But real value emerges when market data managers regularly engage with the business and understand how market data supports daily operations. By combining human interaction with the speed and structure that AI provides, you create a market data function that is ready for 2026: efficient, strategic and focused on real value.

Add to that a market data platform that brings flexibility and enables vendor independence, like the one offered by BIQH. Or bring in specialist support to help interpret and contextualise what you see, as provided by firms such as MDS at Work. With that in place, 2026 could be a very strong year for market data management teams.

AI creates the space. The value comes when you engage with the business.