Executive Guide · January 2026 · 8 min read

What CEOs Should Know About CRM Data Quality

Nobody starts a company to think about CRM data. You have product to build, customers to win, markets to capture. Data quality sounds like a problem for ops to handle.

But here's what most executives don't realize: your CRM data is probably significantly worse than anyone is telling you, and it's affecting revenue in ways that don't show up on dashboards.

The Problem Nobody Talks About

When your VP of Sales says leads are bad, the natural response is to look at marketing. When marketing says campaigns aren't performing, the response is to optimize creative or targeting. When the board asks why CAC is up, everyone points fingers.

What nobody talks about is the data underneath all of it.

The leads marketing generated might be fine. But 30% of the emails are invalid, so sales never reaches them. Or they're duplicates of existing contacts, so attribution is broken. Or the company size data is wrong, so they're routed to the wrong team and never followed up properly.

This isn't hypothetical. In most B2B companies we work with, 20-40% of CRM data has significant quality issues. Duplicates, invalid emails, outdated contacts, missing critical fields. And it's invisible until you look for it.

How Bad Data Affects the Business

Revenue Operations Break

Modern revenue operations depend on data flowing correctly through systems. Lead scoring, routing, nurturing, territory assignment, forecasting. All of it depends on accurate data.

When the data is bad, these systems don't work right. Leads get misrouted. Enterprise deals go to SMB reps. Hot prospects sit in queues because their score is wrong. The automation is perfect. The data feeding it isn't.

Sales Productivity Drops

Your reps are spending time they should be selling on data tasks instead. Researching prospects who've changed jobs. Figuring out if an account is already being worked. Manually correcting information before they can make a call.

Industry studies put this at 20-30% of selling time. For a sales team of any size, that's a massive productivity drag that directly impacts quota attainment.

Marketing Efficiency Suffers

Bad email data means bounced emails, which damages sender reputation, which affects deliverability for everyone. Bad segmentation data means campaigns reach the wrong people. Duplicates mean attribution is unreliable, so you can't tell what's actually working.

Marketing is optimizing campaigns while flying blind on the data underneath them.

Forecasts Become Unreliable

If duplicate records inflate your pipeline, your forecast is wrong. If contacts are outdated and deals are actually dead, your forecast is wrong. If company data is incorrect and some accounts are actually disqualified, your forecast is wrong.

You can have the best forecasting methodology in the world. It doesn't matter if the underlying data is garbage.

Why This Happens

Data quality degrades naturally. People change jobs (about 30% per year in some industries). Companies get acquired. Email addresses stop working. This happens whether or not anyone touches the data.

Meanwhile, data flows in from multiple sources with different quality levels. Web forms, list purchases, trade shows, manual entry, integrations. Each has its own issues. Nobody owns the whole picture.

And data quality is unsexy. It doesn't get executive attention until something breaks visibly. By then, years of decay have accumulated and the cleanup is a major project.

The Executive Questions to Ask

You don't need to become a data expert. But you should be asking these questions:

What percentage of our contact data is verified accurate?

Not "exists" but "verified." How many emails have been validated recently? How many contacts have we confirmed still work at those companies? If your team doesn't know, that's your first problem.

How many duplicate records exist in our CRM?

Duplicates inflate everything: contact counts, pipeline, TAM analysis. They also cause operational chaos. Get a number. If it's more than 5-10% of your database, you have work to do.

When was the last systematic data cleanup?

If the answer is "never" or "I don't know," your database has years of accumulated decay. If it was more than 12 months ago, significant degradation has happened since.

Who owns data quality?

In most companies, the answer is "nobody specifically." Marketing thinks it's ops. Ops thinks it's IT. IT thinks it's the business teams. Without clear ownership, nothing gets done.

What does data quality cost us?

This is the hardest question but the most important. What's the productivity loss? The wasted marketing spend? The deals that didn't close because of bad routing? Put a number on it, even if it's rough.

What Good Looks Like

Companies that take data quality seriously have:

• Clear ownership: Someone is accountable for data quality with actual authority to make changes
• Regular audits: Quarterly at minimum, with documented metrics tracked over time
• Quality gates: Data validation on inbound sources, not just cleanup after the fact
• Visible metrics: Data quality on a dashboard that leadership actually looks at
• Budget allocation: Resources dedicated to maintenance, not just one-time projects

This isn't complicated or expensive. It's just a matter of treating data as the operational asset it is, rather than a problem for someone else to handle.

The Investment Question

Data cleanup costs money, whether done internally or externally. The question is whether it's worth it.

Here's a simple calculation: if your sales team spends 25% of their time on data tasks, and you have $2M in total sales compensation, that's $500K of selling capacity lost to data problems. Even recovering a fraction of that makes cleanup worthwhile.

Add in the marketing efficiency gains, the improved forecast accuracy, and the deals you'll win because leads actually get worked properly, and the ROI case is usually straightforward.

What you can't afford is to keep ignoring it while the problem compounds.

The Conversation to Have

Next time you're reviewing revenue performance with your team, ask about data quality. Not as an accusation, but as a genuine question: how confident are we in the data we're using to make decisions?

The answers might surprise you. And if nobody knows, that tells you something too.