The Hidden Cost of Unmeasured Data Spend

Even mature pharmaceutical organizations often make data procurement and renewal decisions without enough evidence.

The problem isn’t that they lack data. Quite the opposite. Prescription data, claims data, HCP intelligence, formulary data, market access information, and syndicated datasets have become deeply embedded in commercial decision-making. They help organizations understand markets, prioritize territories, measure brand performance, and identify new opportunities. The problem starts when you want to find out which of these investments continue to deliver enough value to justify their cost.

For many organizations, that question is surprisingly difficult to answer. Usage data may be incomplete. Data lineage may be difficult to trace. The connection between a dataset and a business decision may be indirect. And when the evidence is inconclusive, business teams naturally fall back on statements like “this data is critical to my market.”

That makes renewal the safer choice. Renew first, ask questions later. The invoice is visible. The cost of making that decision without enough evidence is not.

Over time, that uncertainty can mean retaining low-value or redundant sources, carrying overlapping data coverage, spending time debating renewals, and missing opportunities to redirect budget toward higher-impact investments. The hidden cost of commercial data spend isn’t just what you pay for the data. It’s what happens when you can’t confidently decide what to keep, what to renegotiate, and what to let go.

The invoice is only the visible cost

When organizations discuss commercial data spend, the conversation usually begins with contracts and subscriptions.

• How much are we paying for syndicated data?
• Which vendor agreements are due for renewal?
• Can procurement negotiate better pricing?

These are important questions, but they only address the easy part. The portion of the investment that’s most clearly visible. The more difficult question is whether the organization understands what that investment is actually delivering. So in addition to the ones above, you should also ask:

• Which datasets actively support commercial decisions?
• Which ones are heavily used across multiple teams?
• Which have become redundant as the commercial landscape has evolved?
• Which could be replaced with existing internal capabilities or alternative sources?

Surprisingly for some, these questions aren’t that easy to deal with, and often there are no clear answers. That uncertainty becomes expensive quickly. Even when you organization buys mostly the right data, it can struggle to continuously evaluate whether existing investments remain the right ones.

The hidden cost is uncertainty

One of the biggest misconceptions about commercial data spend is that the objective should be to reduce it. In our experience, that misses the point. The goal is not to spend less on data. The goal is to spend more effectively and with greater confidence.

Some organizations will discover opportunities to eliminate redundant data sources. Others may conclude they should expand investments in datasets that consistently support high-value commercial decisions.

Both outcomes represent success because both are based on evidence rather than assumptions. The real cost emerges when uncertainty becomes part of every renewal discussion.

Without reliable visibility, procurement teams hesitate to challenge existing subscriptions. Commercial teams naturally defend data they believe is important. Analytics teams struggle to quantify business impact beyond anecdotal success stories. And leadership is left making investment decisions with incomplete information. This may lead to situations where a company buys datasets from different vendors only to discover significant redundancy. Identifying duplication seems like the easiest part of the process, but I’m convinced that slightly less extreme variants of that scenario are still occurring at many life sciences organizations.

And the cost is not simply financial. It’s slower decision-making, internal debate, duplicated investments, and missed opportunities to redirect budget toward higher-impact initiatives.

Why this problem persists

If the challenge is so common, why hasn’t it already been solved? First of all, it’s not that simple. Commercial data creates value in ways that are difficult to observe directly. A syndicated prescription dataset does not create revenue on its own. Neither does a market access database or an HCP intelligence platform.

• These assets support decisions.
• They influence territory planning.
• They improve forecasting.

In general, they help commercial teams identify opportunities, measure performance, and understand changing market dynamics. By the time business value appears, it has been shaped by multiple people, processes, analytical models, and business decisions. Connecting those outcomes back to an individual data investment is rarely straightforward.

As a result, organizations often rely on three imperfect sources of evidence. The first is usage data. Modern data platforms can often show who accessed a dataset, how frequently dashboards are used, or how often queries are executed. This information is valuable, but it only answers part of the question. Usage is not the same as value.

The second is technical lineage. Organizations may understand where data originates and how it flows through reports and analytical applications. Again, this provides useful context, but it doesn’t explain whether those reports actually influence commercial decisions.

The third is business feedback. This is where many renewal discussions ultimately end up.

• A brand team explains that a dataset is critical.
• A commercial leader insists a particular source cannot be removed.
• An analytics team argues that certain reports are heavily relied upon.

Sometimes these statements are absolutely correct. Sometimes they reflect historical assumptions rather than current reality. The challenge is that subjective opinions, however well intentioned, rarely provide enough evidence to support multimillion-dollar investment decisions.

Why organizations keep renewing

From the outside, it can be tempting to ask why organizations continue renewing subscriptions they cannot fully evaluate. The answer is usually quite rational. Removing a commercial data source carries risk.

If a dataset genuinely supports important decisions, cancelling it could disrupt reporting, planning, forecasting, or commercial execution. Very few leaders want to be responsible for removing something that later proves essential. Renewing feels safer, especially when you’re dealing with the uncertainty I mentioned above.

Over time, that behavior creates something you could describe as portfolio drift. New datasets are added to solve emerging business challenges. Existing subscriptions remain because nobody has enough confidence to recommend removing them. Different business units acquire overlapping capabilities. Historical investments continue long after the original business need has changed. All that happens because uncertainty naturally encourages retention over optimization.

Good decisions don’t require perfect measurement

One misconception we frequently encounter is the belief that organizations need to calculate the precise financial ROI of every dataset before making investment decisions. In practice, that level of precision is rarely achievable. Nor is it necessary. Practically speaking, all commercial leaders need are reliable directional signals.

• Can we see whether a dataset is actively used?
• Do we understand which reports and analytical products depend on it?
• Can we identify the business processes it supports?
• Is adoption growing, stable, or declining?
• Does it duplicate capabilities already available elsewhere?

These questions collectively create enough evidence to support better decisions without the need for surgical precision. Instead of relying on assumptions, organizations can begin making informed choices about whether to invest further, sustain existing investments, renegotiate contracts, or gradually divest from lower-value assets. That shift is far more valuable than pursuing perfect attribution that may never be possible.

Treat data spend like an investment portfolio

Perhaps the biggest mindset shift is recognizing that commercial data should not be managed as a collection of individual subscriptions. It should be managed as an investment portfolio. And every portfolio evolves. Some investments increase in importance, while others gradually lose relevance. New opportunities emerge. Business priorities can change.

Organizations benefit from periodically asking whether their portfolio still reflects the decisions they are trying to support today, rather than the decisions they were making three or five years ago. This perspective moves the conversation beyond procurement.

All of a sudden, it becomes a question of portfolio management, where the objective is not simply controlling costs but continuously improving the relationship between data investments and business outcomes.

From spending on data to investing in value

As commercial organizations continue expanding their investments in analytics and AI, understanding the value of underlying data assets will become increasingly important. Not every dataset needs a perfectly calculated ROI. But every significant investment deserves enough visibility to support confident decisions.

That means understanding how data is used, where it contributes to commercial outcomes, and whether it continues to justify the resources invested in it. At C&F, we call this Return on Data.

Our approach helps organizations connect data investments with usage, adoption, business context, and measurable outcomes, creating the directional signals needed to make better investment decisions.

The goal is clear: Build a commercial data portfolio that consistently delivers more value.