What could a closer look at your market data operations reveal?
Market data teams know their world inside out. They know their vendors, contracts, budgets and users, and they understand the constant challenge of keeping costs, usage and licensing under control.
But being close to something can also make it harder to see the bigger picture.
When so much time is spent keeping things moving, there isn't always the space – or the specialist resources – to stand back and ask the types of deeper questions an objective advisor would ask:
Are we managing demand, or simply reporting on spend after it happens?
Do our processes still make sense?
Does our data give us the insight we need to negotiate effectively?
Are our people spending their time where they can add the most value?
And are we set up to handle where market data management is heading next?
For Nadine Scott, Chief Customer Strategy Officer and Head of Advisory Services at TRG Screen, that's where the real opportunity lies: moving beyond understanding what is happening today to being able to influence what happens next.
“You can have a team that is extremely good at keeping everything running. The question is how much capacity they have left to shape demand, challenge the business, manage vendors strategically and create value.”
So, for this month's Market Data Matters, we brought together five TRG Screen experts to explore what can happen when you take that broader view.
What emerges can challenge some fairly fundamental assumptions: that you know the true scale of what you're managing; that having more information puts you in control; that the problem you can see is the problem you need to fix; or that optimization is principally about spending less.
But challenge assumptions and there are opportunities, too – better commercial decisions, stronger relationships with the business, more strategic use of specialist expertise and firmer foundations for automation and AI.
Sometimes, seeing what needs to change starts with looking differently at what you already have.
It sounds basic. But getting a complete picture of everything that sits within market data isn't always straightforward.
Content and technology overlap. Different firms categorize vendors differently. Products get added over time. Responsibilities expand. And what started as a clearly defined market data function can gradually become responsible for a much broader collection of services.
That can create some sizeable surprises.
Sara Crowe - Principal, Advisory Services recalls one organization that believed it was spending around $60 million a year on market data. Once the full picture was analyzed, the figure was closer to $100 million.
“Market data doesn't necessarily fall into a bucket with clean lines and defined specifications. There are nuances that span both content and technology, so firms can be confused as to which vendors are actually classified as market data.”
A gap of that scale isn't simply an accounting issue. It changes the understanding of what the team is managing, the resources required to manage it and where the biggest risks and opportunities might sit.
And spend isn't the only thing that can be hiding in plain sight.
Another common assumption is that once an organization has contracted and paid for data, it can use that data as it chooses.
Market data is licensed intellectual property. The rights attached to it can be highly specific, covering how it can be displayed, redistributed, incorporated into derived data or used within applications.
The complication is that how data is used can change over time.
Users change roles but keep subscriptions. Applications start consuming feeds for new purposes. Data moves between business lines. Internal tools create new outputs from licensed inputs. Unless someone is joining those dots, usage can gradually move away from what was originally agreed.
And the gap may only become visible when a vendor starts asking questions.
Understanding actual use is therefore about much more than avoiding compliance exposure. It gives the market data team a clearer picture of what the organization is consuming, what it genuinely needs and where changes could be made.
As Bill Noorlander, Principal, Advisory Services at TRG Screen, explains:
“While cost savings is generally at the forefront, it is not just about negotiating lower fees. You can achieve the same with different access levels, changes in use models and paying the right attention to actual use.”
That distinction is important. A lower unit price isn't necessarily the best commercial outcome if the underlying access or usage model is wrong. Sometimes the bigger opportunity comes from changing how the organization consumes the data in the first place.
The more clearly contracts, users, usage and rights can be connected, the stronger the foundation for the decisions that follow.
Most market data teams have access to more information than ever.
That should make decision-making easier.
But knowing what is happening doesn't automatically mean you can influence it.
You can know what a vendor costs without knowing whether the business genuinely needs everything it buys. You can know who has a service without understanding how critical it is to their role. You can know a contract is increasing significantly without having a credible alternative when renewal arrives.
As Nadine explains, the question is whether the information available was designed to support the decision you're trying to make.
“More information isn't the same as information built for a purpose. Data assembled to settle invoices will answer questions about invoices very well. Asking it to support a negotiation, a sourcing decision, or a conversation with the business about demand is asking it to do something it wasn't shaped around.”
That's an important distinction.
Being able to report accurately on what has already happened is valuable. But moving beyond that means using information to shape what happens next: challenging demand before another service is ordered, identifying alternatives, anticipating vendor changes and going into negotiations knowing what the organization is genuinely prepared to do differently.
There is another assumption worth challenging: that market data procurement can be managed in much the same way as any other cost category.
Traditional sourcing disciplines have an important role. But market data brings an unusual combination of contracts, users, applications, entitlements, usage rights and business dependency.
Dan Kennedy, Head of Sales North America at TRG Screen, sees firms encounter the limitations of a purely sourcing-led approach when they reach the negotiating table:
“People tend to look at it as a sourcing problem primarily, because that's how you would handle every other cost category. But the deeper value is in understanding the connectivity between contracts, invoices, users, user behavior and what they're allowed to use.”
And that's an important point. Getting the information organized doesn't, by itself, create value. You need to understand what it is telling you, decide what strategy to apply, act on it and then determine whether that action actually delivered the intended result.
It's a loop that Dan believes firms don't always close. Better information creates the potential for better decisions; expertise and execution are what turn that potential into an outcome.
A team may know that a proposed price increase looks unreasonable. That doesn't necessarily mean it has the leverage to change it.
As Ian Pilbeam, TRG Screen Industry Strategist, puts it:
“Information does not equal leverage in many cases. The threat of vendor displacement, if it's even possible, is a more solid basis for a negotiation.”
And that leverage can't suddenly be created a week before renewal.
It comes from understanding business dependency, knowing what alternatives exist, keeping ahead of vendor strategies and giving the organization enough time to act.
The useful question isn't simply what are we paying?
It's what could we realistically do differently?
This is where taking a broader view becomes particularly useful.
Imagine the instruction from management: reduce market data spend.
The obvious response might be to renegotiate contracts or identify unused services. But what if the reasons behind the rising spend sit much deeper in the process?
New requests may be approved without checking whether equivalent content already exists. High-cost services may remain with users whose roles have changed. Contracts may renew because there hasn't been time to revalidate the requirement. Or vendor pricing changes may only receive serious attention when the renewal is already looming.
As Sara puts it:
“There is almost always a downstream issue that supports the symptoms identified by management. Management is focused on lowering costs and avoiding extreme budget increases, but the underlying causes are many.”
The same applies when the apparent problem is technology.
A firm might conclude that a system isn't delivering enough value when the real issue lies in the quality of the underlying data, how the process has been designed, who owns it or how consistently the technology is being used.
Looking across the different pieces together – people, processes, systems and data – can reveal that the thing causing the pain isn't necessarily the thing everyone has been trying to fix.
And that matters because treating the symptom may produce a short-term improvement without changing what caused it. The more useful question is not simply “how do we fix this?” but “why is this happening in the first place?”
Once you start making those connections, the opportunity becomes much broader than finding something to cancel.
Take cost allocation.
Dan gives the example of a hedge fund with ten portfolio managers. The firm buys market data centrally and allocates the cost equally between them. On paper, dividing the bill by ten looks perfectly reasonable.
But what if one portfolio manager consumes significantly less? What if another relies on particularly expensive datasets? Or what if different teams derive very different commercial value from what they're consuming?
Suddenly, the allocation becomes much harder to defend.
Connecting costs with users and consumption creates the opportunity to allocate them more intelligently – and gives the business much greater transparency over the cost of its own decisions.
“If you offer that transparency to the business, behavior may change. That's where you can get a benefit across savings – but it's not simply about cutting cost,” comments Dan.
And sometimes the opportunity is the exact opposite of cost reduction.
A closer look can reveal that the organization should actually be buying something it doesn't have because an important commercial or client requirement isn't being adequately supported. Equally, data already being paid for and used for one narrow purpose may have value elsewhere – supporting new analysis, products, client propositions or risk capabilities.
That challenges another common assumption: that good market data management is principally about taking cost out. Sometimes it is about getting more value from what you're already paying for – or recognizing where additional investment could create value.
Ian sees an opportunity for market data teams to play a much more proactive role here:
“Surfacing when new or changed datasets seem relevant to the business can give an organization competitive advantage if that knowledge can be turned into new product opportunities.”
That's a very different conversation from simply asking how much a feed costs.
It positions market data knowledge as something that can help the business make better decisions – and potentially create value – rather than simply control expenditure.
There is another source of value that can easily be overlooked: the expertise sitting inside the market data team itself.
Market data is a specialist discipline. Experienced professionals build up years of knowledge about suppliers, licensing, commercial models and how their business consumes data.
Yet many still spend significant amounts of time gathering information, reconciling records and handling repetitive administration.
That creates an opportunity cost which doesn't appear neatly on a market data report.
And there can be another unintended consequence. As firms reduce headcount, outsource activities or introduce more technology, they need to be careful that efficiency doesn't come at the expense of the institutional and domain knowledge needed to manage market data well.
Ian's perspective is that the value of experienced market data professionals increasingly lies in applying that knowledge – understanding vendor behavior, licensing nuance, business requirements and where genuine alternatives exist – rather than simply processing the work around it.
For Nadine, greater maturity isn't simply about making those activities more efficient. It's about creating the capacity for experienced people to focus on work where their knowledge and judgement can have greater impact.
That might mean managing strategic vendor relationships, working with the business on demand, assessing alternatives, interpreting licensing changes or identifying opportunities to get more from the data the organization already buys.
Technology and automation have an obvious role in freeing up that capacity. But simply automating what happens today isn't necessarily the answer.
As AI becomes more embedded in market data management, many of the issues we've discussed become harder to ignore.
If contract records are incomplete, AI doesn't know what's missing.
If inventory is unreliable, automation can act on unreliable information faster.
And if a manual process contains years of undocumented human judgement, automating it may reveal just how much interpretation and correction was happening behind the scenes.
Sara sums up the risk neatly:
“Automating a bad process doesn't fix it. It just makes bad processes faster.”
That doesn't make AI a threat to market data teams. Quite the opposite.
Dan sees data quality as one of the first weaknesses AI is likely to expose, but also one of the biggest opportunities. Once reliable foundations are in place, AI has the potential to enrich information, identify relationships and challenge processes that have evolved through habit rather than design.
Bill brings that back to the underlying objective:
“Volumes of data do not mean good data. You need a good understanding of what your objectives are for better commercial decisions.”
The opportunity, then, isn't simply to automate more.
It's to make sure the people, processes, systems and data underneath that automation are capable of supporting where the function wants to go.
Perhaps the biggest theme to emerge from our roundtable is that market data maturity isn't defined by how efficiently the day-to-day gets done.
That's the starting point.
The bigger opportunity comes from connecting the information and expertise already within the organization so teams can become more proactive: shaping demand rather than simply processing it; preparing for negotiations rather than reacting to them; understanding the relationship between cost, usage and value; and giving experienced market data professionals more capacity to work strategically with the business.
That brings us back to Nadine's starting point. The shift isn't simply from inefficient to efficient. It's from knowing what has already happened to being in a position to shape what happens next.
And getting there starts with understanding where you really are today – not where you assume you are.
That may uncover spend you didn't know about, licensing exposure you hadn't spotted, processes that no longer make sense or information that isn't giving you the answers you need.
But it can also reveal opportunities you weren't looking for.
Because the point of taking a closer look isn't simply to find what's wrong. It's to understand what needs to change – and where the market data function could be creating more value than it does today.