As you might imagine, we get quite a few questions through the subscriber Help Desk about the underlying data people are using to perform analyses and inform pricing decisions. One of the more common refrains goes something like this:
“Isn’t using dirty pricing data worse than having no data at all?”
Of course, there are no absolutes and the safest consultant-speak answer would be, “well…it depends.” In the past, we’ve repeatedly illustrated how the “dirty data” excuse doesn’t actually hold water in most cases and how you can in fact glean a lot of valuable insight from relatively dirty data.
That being said, however, there are situations where dirty data can actually be worse than having no data at all. Not because the data is incomplete or it’s a little messy. But because it actively pushes people toward the wrong conclusions, the wrong priorities, or the wrong behaviors…all with the “air of precision” and the false sense of confidence that numbers tend to engender.
And false confidence is far more dangerous than uncertainty.
A good example can be found in margin composition and price realization analysis. If your transaction data is incomplete, inconsistent, or poorly attributed, your margin and realization reports don’t just become “a little off.” They become structurally misleading. They systematically overstate performance in some areas, understate leakage in others, and create an illusion about where your pricing is working and where it’s not.
That illusion then works its way into pricing strategy adjustments, list price updates, discount policy changes, and executive expectations. Decisions get made as though the numbers are solid when…in reality…they’re molded out of sand. And as a result, the organization falls further and further out of alignment with the best strategic opportunities.
In this situation, having no analysis at all would at least force people to acknowledge the uncertainty and manage it thoughtfully. Dirty analyses, on the other hand, can hide huge uncertainties and inaccuracies behind an impressive-but-flimsy façade of charts, graphs, and numbers.
Another example comes from price segmentation and willingness-to-pay analysis. If customer, deal, and product data is riddled with missing attributes, misclassified transactions, inconsistent cost allocations, and poor competitive tagging, then your segmentation models don’t simply become fuzzy…they become biased.
You start to “see” pricing power where none really exists. You conclude that certain segments are more price sensitive than they actually are. You label certain product lines as commoditized or margin-poor when the real issue is discounting behavior, cost misallocation, or channel mix differences.
The big problem is that these biases and distortions don’t stay confined to analytics. They end up getting operationalized. Price bands get narrowed or widened. Sales guidance and targeting changes. Marketing and product investments shift away from some segments and toward others. And now the business is actively optimizing against a warped picture of pricing power and response.
At that point, dirty data isn’t just unhelpful…it’s actively steering decisions in the wrong direction.
A third example presents itself when data is used to evaluate people rather than pricing mechanisms. Pricing teams are often asked to use data to assess sales discipline, policy compliance, or negotiation effectiveness. But when the underlying data is incomplete, selectively entered, or politically “managed,” the analysis quietly shifts from measuring behavior to assessing gamesmanship.
Reps that are disciplined about following pricing guidance may get dinged because they lose deals their managers think are winnable. Managers who stretch or game the system help their teams look better than they actually are. Over time, the data becomes less a reflection of pricing performance and more a reflection of incentives.
At that point, the data is no longer a diagnostic tool…it’s a behavioral distortion field.
None of this means that dirty data is inherently bad or unusable. Again, in most cases imperfect data can still be extremely valuable for identifying directional trends, relative differences, and broad patterns…as long as everyone involved understands its limitations and treats it accordingly.
When dirty data is treated as precise, complete, and authoritative when it really isn’t, that’s when it stops being a decision aid and starts being a decision liability.
So…the goal for pricing teams is not to demand pristine and perfect data before doing anything useful. That’s unrealistic and unnecessary. No, the goal is to understand where the data is flimsy, where it’s biased, where it’s incomplete, and where it’s being operationalized in ways that could cause harm.
If you know that your contribution data is noisy, you can use it cautiously. If you know that your segmentation model is strong in some areas and weak in others, you can avoid over-optimizing against it. And if you know that your performance metrics are gameable, you can design them more thoughtfully.
Dirty data becomes dangerous not because it’s dirty, but because people forget that it’s dirty!
Used with humility, imperfect pricing data can still help you ask better questions, test smarter hypotheses, and view your pricing performance more clearly than you otherwise could. But used uncritically, it can quietly convince you that you understand your marketplace far better than you actually do.
And in B2B pricing, that particular mistake tends to be far more damaging than simply acknowledging that the data isn’t perfect and that a grain of salt…or even two…might be warranted.
Working With "Bad" Pricing Data
In B2B, there are so many moving parts that always having a perfectly accurate and complete dataset just isn't very realistic. So we've got to figure out how to do the best we can with the data we've got.
Using Pricing Analysis to Drive More Growth
Pricing analysis capabilities are usually only focused on pricing. But pricing isn't the only thing the analytical processes and underlying data can be used for. In this on-demand webinar, you will learn how answering other powerful questions can increase your impact and internal profile.
Delivering Data to Decision Makers
Providing data to decision-makers is a core responsibility for most pricing teams. But getting it right is a significant challenge. In this on-demand session, learn how leading teams are making their efforts in this area more effective.
