Before you launch the campaign. Before you build the scoring model. Before you redesign the funnel, reconfigure the tech stack, or hire the demand gen agency. Before any of that, answer one question:
Do you trust your data?
Not theoretically. Not “we have data.” Do you trust the numbers in your CRM enough to make a $500K decision based on them? If someone asked you how many active accounts are in your pipeline right now, could you give them a number you’d bet your job on?
In most PE-backed companies, the honest answer is no. And everything built on top of that “no” is a house of cards.
The Data Problem Nobody Wants to Own
Data infrastructure is the least glamorous problem in B2B marketing. Nobody gets promoted for cleaning the CRM. Nobody presents a “we fixed the data” slide at the board meeting. It’s invisible work that produces no visible output until you try to build something on top of it.
Which is why it doesn’t get done. Marketing leaders inherit systems with 200,000 records, half of which are duplicates, a third of which have incomplete information, and an unknown percentage of which are completely wrong. Contact data decays at 25–30% per year. Companies merge, people change roles, email addresses bounce, phone numbers disconnect.
But the dashboards still work. The charts still render. The funnel still shows numbers moving from stage to stage. And everyone looks at those charts and makes decisions as if the underlying data is trustworthy, because the alternative — admitting that the data is unreliable — means admitting that every decision made using that data is questionable.
Nobody wants to be the person who says “our numbers might be wrong.” Especially not in a PE-backed company where the numbers are all anyone talks about.
What Unreliable Data Actually Costs
The cost of bad data isn’t abstract. It’s concrete and measurable, even though nobody measures it:
Your lead scoring model is scoring based on data fields that are 40% inaccurate. Which means 40% of your lead scores are wrong. Which means sales is getting “high-priority” leads that aren’t actually high-priority, and missing leads that are. The entire system designed to prioritize sales effort is producing random output and nobody knows it because nobody audited the inputs.
Your attribution model says marketing sourced $3M in pipeline last quarter. But the attribution rules were set up by someone who left two years ago, the UTM parameters are inconsistent, and half the “marketing-sourced” deals were actually inbound referrals that happened to hit a tracked URL. You’re reporting a number to the board that you can’t defend if anyone asks how it was calculated.
Your ICP definition says you target companies with 500–5,000 employees in financial services and healthcare. But when you pull the actual closed-won data, your best customers are 200-person technology companies that don’t match a single criterion. Your targeting and your reality have diverged, and the data to surface that divergence exists but has never been analyzed.
Every one of these is a strategic decision being made on broken information. And the cumulative cost is immeasurable: wrong accounts targeted, wrong leads prioritized, wrong stories told to the board, wrong conclusions drawn about what’s working and what isn’t.
Start Here
The most productive thing a marketing leader in a PE-backed company can do in their first 90 days is something that will never appear in a board presentation: audit the data.
Not a full data cleansing project. Not a six-month CRM overhaul. Just a clear-eyed assessment of what’s trustworthy and what isn’t. Which fields are populated accurately. Which reports can be relied on. Which metrics are real and which are artifacts of bad data masquerading as insight.
This isn’t exciting work. It won’t produce a bump in this quarter’s pipeline chart. But it will produce something more valuable: a foundation of truth that every subsequent decision can be built on.
Because here’s the thing about growth engines: they’re only as good as the data they run on. A perfectly designed demand system running on unreliable data will produce unreliable results. And in a PE-backed environment where every result gets scrutinized by people who know how to read numbers, unreliable results don’t just fail — they erode the credibility of everyone associated with them.
You cannot run a growth engine on unreliable data. And pretending you can is the most expensive decision a marketing leader makes.