The Impact of Data Quality on GTM Work (Real Talk)

Nobody wants to talk about this. We'd rather discuss fancy AI tools, new outbound sequences, or the latest sales methodology. But here's what's actually killing your GTM motion: the data underneath everything else is garbage, and everyone on your team knows it but nobody wants to admit it.

A 2026 data quality survey found that 31% of revenue is now impacted by data quality issues. That's up from 26% just four years ago. The problem isn't getting better. It's getting worse, even as we throw more tools at it.

So let's have an honest conversation about what data quality actually does to your go-to-market execution, why most solutions fail, and what actually works.

The Uncomfortable Truth About Your CRM

Here's the scenario playing out at thousands of companies right now. Marketing generates leads. Sales works them. Deals close (sometimes). Data enters the CRM through a dozen different paths: form fills, imports, manual entry, integrations, purchased lists. Everyone assumes someone else is maintaining quality.

Nobody is.

The result? Your reps are spending roughly 27% of their time dealing with bad data. That translates to about 546 hours per year per rep, according to multiple industry studies. Not selling. Not building relationships. Hunting for accurate information, merging duplicates, and dialing wrong numbers.

Think about what that actually means. If you have ten SDRs, you're effectively paying for 2.7 full-time employees who spend their entire year doing data cleanup instead of prospecting. That's real money walking out the door.

And it compounds. B2B contact data decays somewhere between 22% and 70% annually. People change jobs. Companies get acquired. Phone numbers go stale. Email addresses bounce. The database you cleaned six months ago is already rotting.

Where Bad Data Really Hurts

The impact shows up everywhere, but some places hurt more than others.

Outbound goes nowhere. Your SDR sends 100 emails. Many bounce because the contacts left the company months ago. Another twenty go to generic "info@" addresses that nobody monitors. Ten more reach people who no longer have buying authority. Of the remaining thirty, maybe a few are actually relevant prospects. Your reps are fishing in a pond that's mostly empty, but the metrics make it look like a volume problem.

Routing breaks down. Leads come in and nobody can figure out where they belong. Is this an existing customer? A prospect already in conversation with sales? A duplicate of three other records? Without clean data, lead routing becomes a game of whack-a-mole. The State of Marketing Data 2025 report found that over 60% of teams say poor data disrupts lead handoffs and slows sales productivity.

Forecasting becomes fiction. When your CRM is full of duplicate opportunities, dead deals that never got closed out, and accounts assigned to reps who left the company, your pipeline reports mean nothing. Leadership makes decisions based on numbers that don't reflect reality. Resources get allocated to the wrong places. Targets get set based on inflated pipelines.

Personalization fails. Everyone talks about personalized outreach. But you can't personalize effectively when you don't know basic facts about your prospects. Is Sarah still the VP of Sales, or did she get promoted? Did the company raise a round, or was that their competitor? What technology do they actually use? Without accurate data, your "personalized" emails read as generic at best and embarrassing at worst.

Costs Nobody Calculates

Most companies think about bad data in terms of obvious waste: bounced emails, wrong numbers, wasted rep time. But there's a whole layer of damage that's harder to measure.

Sales and marketing blame each other. When conversion rates drop, sales says lead quality is terrible. Marketing says sales isn't following up properly. Both are partially right, and the root cause is often that nobody can trust the data enough to diagnose the actual problem. Teams that should be collaborating become adversarial because they're working from different versions of reality.

Good reps leave. High performers get frustrated when their tools work against them. If your best SDR spends half her time cleaning up data just to do her job, she'll eventually find somewhere that has their act together. The turnover costs far exceed what you'd spend fixing the underlying problem.

Opportunities slip through cracks. When you don't have visibility into account history, job changes, or trigger events, you miss moments that matter. A contact who championed your product at their old company moves to a new role. A prospect company gets funding. An existing customer's competitor just bought your solution. These signals exist, but they're invisible when your data foundation is broken.

Compliance risk grows. Between GDPR, CCPA, and evolving privacy regulations, having inaccurate or outdated contact information isn't just inefficient. It's potentially a legal liability. Trying to maintain compliance when you can't even trust your records is a nightmare for legal and ops teams alike.

Why Most "Solutions" Don't Solve Anything

Companies usually respond to data problems in predictable ways. None of them work particularly well.

The annual database cleanup. Once a year, someone exports the CRM, runs it through a cleaning service, and imports the results. For about two weeks, things feel better. Then the decay starts again, and within six months you're back where you started. It's like going to the gym once a year and expecting to stay fit.

Buying more data. When existing records are bad, the instinct is to purchase better ones. So you buy a list from a data provider. The new records come in, get mixed with your existing messy data, and soon you can't tell which information is trustworthy. Plus, those purchased records start decaying immediately.

Hiring someone to maintain it. Some companies assign a person (or a team) to data hygiene. This can help, but it's treating symptoms rather than causes. If your processes keep generating bad data, no amount of manual cleanup will keep pace. You need different systems, not just more labor.

Ignoring it entirely. The most common approach: pretend the problem doesn't exist. Reps find workarounds. Marketing builds parallel databases. Everyone develops their own source of truth, and organizational alignment becomes impossible.

How to fix GTM data quality

Fixing GTM data quality isn't about finding a magic bullet. It's about changing how data flows through your organization in the first place.

Start with the basics: prevention over cure. The cheapest bad record is the one that never enters your system. Build validation into every point where data comes in. Required fields. Format standardization. Duplicate detection at the point of entry. If someone tries to create a contact without an email address, don't let them. These small frictions prevent much larger problems downstream.

Layer your data sources. Single data providers have coverage gaps. No matter which vendor you use, they don't have accurate information for everyone you care about. The solution is what people call waterfall enrichment: check your first provider, and if they don't have the data, automatically check a second, then a third. This approach can push match rates from 50-60% with a single source to 80-90% across multiple providers.

Automate enrichment, not just cleanup. Instead of manually researching prospects, set up systems that automatically enrich records when they enter your CRM and refresh them on a schedule. Platforms like Databar let you connect to dozens of data providers without managing separate subscriptions, then create enrichment workflows that run automatically whenever new records appear or existing ones need refreshing.

Build decay schedules into your processes. Different data types decay at different rates. Phone numbers go stale faster than company names. Email addresses decay faster than physical locations. Set up refresh schedules that match these realities. Maybe you reverify emails monthly, refresh firmographics quarterly, and update contact details whenever engagement signals suggest something changed.

Make quality everyone's problem. Data hygiene can't live exclusively with ops or marketing. Reps need to flag inaccurate records. Marketing needs to validate lead sources. Customer success needs to update information after conversations. Build lightweight feedback loops so issues get caught early, not six months later during an audit.

The Signals That Something's Wrong

How do you know if data quality is undermining your GTM motion? Watch for these patterns:

Your email bounce rates keep creeping up, even when you haven't changed targeting or messaging. Bounces above 5% suggest your contact data is decaying faster than you're refreshing it.

Sales consistently complains that "leads are bad" without being able to articulate exactly what's wrong. Often this means the leads themselves might be fine, but the data attached to them (job titles, company info, contact details) is incomplete or inaccurate.

Different teams give different answers to basic questions like "how many accounts are in our ICP" or "what's our average deal size." When the same query returns different numbers depending on who runs it and which system they use, you have a data consistency problem.

Reps spend significant portions of their day doing research outside the CRM. If your people are constantly Googling to find basic information that should be in their tools, the tools aren't working.

Account coverage metrics don't match reality. You think you have three contacts at a target account, but two left the company a year ago and the third is actually at a different company with a similar name.

What Good Looks Like

When data quality is healthy, you can see it in how teams operate.

Reps open the CRM and trust what they see. They don't need to verify information through external channels before acting on it. The record says the contact is VP of Operations, and that's accurate.

Lead routing happens automatically and correctly. New inbounds reach the right person within minutes, with context about the account already attached.

Pipeline reviews use actual numbers. Forecasts are based on real opportunities with accurate close dates and deal sizes. Leadership can make resource decisions confidently.

Outbound campaigns have predictable performance. When you target 1,000 accounts, most of them actually receive your message, and responses correlate with your targeting criteria.

Marketing attribution is trustworthy. You can see which campaigns sourced which opportunities because the data is clean enough to track properly.

Building Sustainable Data Health

The goal isn't perfect data. Perfect data doesn't exist. The goal is data that's good enough to support the decisions and actions your GTM motion requires.

That means understanding what "good enough" looks like for your specific use cases. For outbound prospecting, you need accurate contact information and recent-enough firmographics. For account-based marketing, you need complete account hierarchies and reliable technographic data. For customer success, you need up-to-date usage and engagement signals.

Prioritize accordingly. Not every record needs the same level of enrichment. Your top 500 target accounts probably warrant more attention than the 10,000 companies in your broader TAM list. Build tiered processes that allocate resources where they matter most.

Track the metrics that reveal quality trends. Monitor bounce rates, duplicate creation rates, records with missing required fields, time since last enrichment. Set thresholds that trigger action before problems become crises.

And accept that this is ongoing work. Data quality isn't a project you complete; it's a capability you build and maintain. The companies that get this right treat it as infrastructure, not a one-time initiative.

The Payoff

Clean data compounds just like bad data does, but in the opposite direction.

Organizations that maintain high data quality see 20% better campaign response rates. Close rates improve by roughly 15% within six months of getting data foundations in place. Conversion rates climb around 12% when marketing and sales work from accurate information.

More importantly, teams stop fighting with their tools and each other. Energy that went into working around bad data gets redirected toward actually selling and marketing. Morale improves because people feel effective rather than frustrated.

The companies that win aren't necessarily the ones with the biggest budgets or the most sophisticated strategies. Often, they're simply the ones whose data works well enough that their other investments actually pay off.

That's the real talk about data quality and GTM: it's not glamorous, it's not exciting, and nobody puts it on the homepage of their pitch deck. But it's the foundation everything else depends on. Get it wrong, and every other motion you build runs at a fraction of its potential. Get it right, and suddenly all those other investments start delivering what they promised.

FAQ

How fast does B2B contact data decay?

Studies show B2B contact data decays somewhere between 22% and 70% annually, depending on the industry and data type. Email addresses in particular have been decaying faster recently, with some estimates putting monthly email decay at around 3.6%. This means your database needs regular refreshing, not just occasional cleanup.

What's the most important data quality metric to track?

Bounce rates provide the most immediate signal of contact data health. If email bounces climb above 5%, you likely have a decay problem. Beyond that, track records missing required fields, duplicate creation rates, and time since last verification for key data points.

Should we hire someone to manage data quality?

It depends on your scale. Smaller teams can often manage with automated enrichment tools and clear processes. Larger organizations may benefit from dedicated RevOps or data ops resources. But adding people without fixing underlying processes usually fails. The goal should be systems that maintain themselves, with humans handling exceptions.

How do we get sales reps to actually update the CRM?

Make it easy and show them the value. If updating records is a multi-step process, reps won't do it. If they can flag an issue with one click, more will participate. Also share win stories where good data led to closed deals. When people see that data quality connects to their commission checks, behavior changes.