Market Data Matters: The February 2026 edition

Market data processes don’t usually fail in obvious ways.

They fail when everyday tasks take longer than they should. When questions that used to be easy to answer aren’t. When teams spend more time explaining and chasing than actually improving anything.

That pressure shows up as organizations grow either organically or through M&A activity. More users. More vendors. More products. More regulation. More internal change. As teams scale across offices, regions and time zones. Both volume and complexity increase, and the friction becomes harder to ignore.

From a technology and operating model perspective, the same four market data processes consistently show up as the first to struggle. Not because teams aren’t doing their jobs, but because the way the work is organized no longer matches how the business operates.

Entitlement management is often the first place things start to get off track.

In smaller or more stable environments, access is relatively predictable. People stay on the same desks for long periods. Role changes are infrequent. Vendor relationships are well understood. Manual updates are manageable, and if something looks wrong, someone usually spots it.

At scale, that breaks down.

People change roles more often. Coverage models evolve. New products are launched. Vendors are added. Regulatory requirements shift. Teams may also be operating across regions with different entitlement rules and vendor constraints.

What should be a simple update increasingly depends on information scattered across teams, systems and individuals.

Over time, entitlement models start to lag reality. Some users retain access they no longer need. Others don’t get access they should have. The gap between how people actually work and how access is configured widens.

That gap creates cost leakage and compliance risk. The process just can’t keep up with the rate of change.

If entitlement management defines what access someone should have, access provisioning determines how quickly they actually get it.

In many firms, provisioning still relies on approval chains, manual checks and a small number of people who understand how things really work. That can function when volumes are low and change is slow.

As scale increases, it doesn’t.

More joiners, movers and leavers. More applications. More entitlement changes. More dependencies between teams. Each step introduces delay; whether that’s waiting for approval, validation or simply for the right person to be available.

Geography amplifies the issue, but it isn’t the root cause. Even within a single region, higher volumes and more frequent change stretch processes that were originally designed to be handled ‘by hand’.

When access takes too long to provision, the impact is immediate. People can’t get the data they need. Teams look for shortcuts, and those shortcuts introduce risk, often unintentionally.

This isn’t a discipline problem. It’s a design problem. Processes that depend on seamless approvals and individual knowledge don’t scale as organizations grow and change faster.

Cost allocation often surfaces later, but it tends to create the most friction when it does.

As organizations scale, market data spend becomes shared across desks, products, regions and legal entities. Vendor contracts grow in size and complexity. Usage is spread across teams with different priorities and funding models.

Finance still needs clear, defensible answers.

Without reliable entitlement and usage data, allocation becomes approximate. Costs are spread using assumptions rather than evidence, and variations across periods are hard to explain.

That’s when questions start. Why is one desk paying for data it doesn’t believe it uses? Why can’t anyone clearly explain the change from last quarter to this one?

What should be a factual discussion turns into negotiation. Decisions slow down. Optimization becomes blunt, because no one fully trusts the inputs.

Again, the issue isn’t effort. It’s that cost allocation models built for simpler, slower organizations don’t hold up once usage and ownership are distributed across the business.

And finally, a bonus observation – whilst focusing day to day incremental demand it's easy to lose sight of big upcoming contractual events that can wreck a budget in one go. So, whilst diligence on the constant flow of individual demand for entitlement is essential, keeping a weather eye on renewals (or periodic reviews of perpetual contracts needs attention too.

As teams grow and change, it becomes frightfully easy to miss renewal deadlines, terminations and rightsizing opportunities. It’s not uncommon for us to see some very high-cost numbers where a team’s renewals process was not in order and contracts were renewed even though they were no longer needed. These can become very hard to explain to senior leadership. The key difference between the licensing side of the business and the entitlement side? With licensing, one mistake can cost hundreds of thousands of dollars, while on the entitlement side it’s more of a death by a thousand cuts.

This can be especially problematic in cases of M&A activity, where a team inherits a bunch of unfamiliar deals. The newly formed team often lacks the systematic or tribal history to remind them XYZ renewal is coming up. These cases highlight that effective market data management truly requires sweating the small, tactical stuff day to day while not losing sight of the bigger looming icebergs that could sink the whole budget in one event. It’s areas like this where your technology, especially if it’s powered by AI, can provide the crucial safety net to prevent such mishaps.

Across entitlement management, access provisioning and cost allocation, the underlying issue is the same.

These processes were designed for organizations where change was slower, structures were simpler, and people could rely on informal knowledge to keep things moving.

That doesn’t work at scale.

Manual handoffs, individual knowledge and spreadsheet-driven processes don’t stretch indefinitely. At a certain point, the volume and pace of change overwhelm them.

But scale doesn’t create these problems, it exposes them.

And as market data teams head deeper into 2026, the challenge for leaders isn’t whether these pressures will appear, but whether they recognize them early enough to address the root causes, rather than reacting once the friction becomes unavoidable.