Why Data Quality Is the Bedrock of All RevOps Success
Revenue Operations is often tasked with delivering efficiency across the go-to-market engine. That efficiency usually translates into cleaner dashboards, better forecasting, and tighter handoffs between marketing, sales, and customer success. But what often gets overlooked, until it's too late, is the quality of the data underpinning all those processes.
If your Salesforce instance is a mess (and let’s be honest, whose isn’t!?), your scoring model is going to misfire. If your enrichment vendors are feeding you incomplete data, your segmentation won’t hold up. If your routing logic is based on malformed fields, your SDRs are going to waste their time chasing the wrong leads. Data quality isn’t just about keeping a clean database, it’s about enabling every part of your revenue engine to perform at its best.
Data Problems Compound Quietly
What’s tricky about data quality is that its consequences rarely announce themselves. You don’t get a Slack message that says, “Your conversion rates are down because 30% of your job titles are malformed.” Bad data stays silent. The creepy type of silence!
Reps complain about junk leads, but they can’t point to exactly why.
Attribution dashboards feel off, but no one can confirm where the gaps are.
SDRs spend more time triaging leads than engaging them.
The signs are there, but they’re buried under layers of assumptions and duct-taped automation. And before long, ops teams are stuck reacting to exceptions instead of scaling with intention.
Defining What Good Data Looks Like
Most teams say they want "clean data," but rarely define what that actually means. In practice, it’s less about perfection and more about fitness for use. If you're trying to build out an ideal customer profile, you need reliable firmographics. If you're scoring leads, you need consistent job titles and industry tags. And if you're running ABM, you better trust the domains and personas tied to your target accounts.
Good data means “CA,” “California,” and “Calif.” all normalize to a single, consistent value. Your routing rules and reports don’t break over formatting differences. Another example of good data is a clear, consistent set of rules of how you normalize data. Take for example that you may have a global sales organization. You have a team in Spain. One in the US. One in Saudi Arabia. So when you mention the United States you may have them entered into your system as: Estados Unidos, USA, and الولايات المتحدة الأمريكية. Having a clear seat of global and local normalization rules will help set up your CRM. What I’ve done in the past is use Openprise to define these rules ahead of time so whenever these items are entered in the CRM, the data would be normalized according to the business rules or predefined rules in your system.
So instead of shooting for some abstract ideal, define what good looks like by use case. You might care more about deduplication in your inbound flow, while enrichment matters more for outbound targeting. The key is to align your data quality efforts to your actual revenue workflows.
Data Decays Even If You Do Nothing
One of the harshest truths in RevOps is that your data gets worse every day, even if no one touches it. People change jobs. Companies pivot. Vendors merge. What was accurate six months ago is now a liability. The average tenure of a CRO was 18 months and now it’s 16 months. That means if you’re targeting a CRO, they’re likely out of that position just a year and a half later.
And if you're not proactively managing data decay, you're essentially flying blind. That lead you thought was a senior decision-maker? They left last quarter. That account you routed to your enterprise team? They’ve downsized to SMB.
The decay isn't just a minor nuisance, it's a core reason why go-to-market performance lags, especially in longer sales cycles. I highly suggest monitoring job changes as part of your Master Data Management strategy.
In instances like this I’ve used Openprise’s Champion Mover solution to track changes. Then enrich with your data provider to see when they moved. It’s the first few months where these prospects have a change mindset. Being top of mind reaching out to them could position your solution well if and when they do need to make an impact.
Cleansing Isn’t Glamorous, But It’s Necessary
Data cleansing gets a bad rap. It sounds like janitorial work. But in reality, it’s the maintenance that keeps your engine running.
At its core, cleansing means:
Removing obvious junk records ("asdf@asdf.com" is not a real lead)
Standardizing fields like state, country, and title
Fixing malformed domains, emails, or phone numbers
Filtering out test data and duplicates before they enter the system
This is the front line of defense. And if you don’t have it automated, you're playing whack-a-mole every time something breaks downstream.
Enrichment Isn’t One-and-Done
Too many teams treat enrichment like a checkbox. Buy a license from a data vendor, plug it in, and assume your data is now "complete." But no single provider can cover your entire ICP. Some are better at SMBs. Others skew toward enterprise. Some give you titles but not emails. Others nail technographics but miss phone numbers.
The more mature approach is to build and maintain a waterfall. Start with your preferred vendor, but if they return nothing, fall back to others. Prioritize based on match rates and field-level completeness. You don’t need 100% coverage, you need the right coverage for your strategy.
Platforms like Openprise do this well. They allow you to sequence enrichment vendors, clean the responses, and standardize the output, all without writing code or managing multiple contracts. It’s not sexy, but it works, and you only pay for the records you keep.
Segmentation and Scoring Are Only as Good as the Inputs
Here’s where it all ties together. You want to build a rock-solid scoring model? It better not rely on fields that are missing 40% of the time. Want to route leads by seniority or function? You need consistent job title parsing. Running an ABM motion? You can’t trust account tiering if your revenue fields are inaccurate.
Create your segments and then leverage your tools to filter the records correctly. This will allow seamless operations fit for purpose by section. This is a bit dated but I’ve often used this Openprise guide from back in 2015 to think through all of the rules I need to build out and have specific jobs execute by function.
In other words, all the high-leverage work you want to do in RevOps, scoring, routing, attribution, segmentation, depends on the foundation being stable. Otherwise, your elegant logic turns into a house of cards.
AI Adds Power, But Only If Your Data is Prepped
There’s a lot of hype about AI in RevOps. Some of it is warranted. But AI isn’t magic. If the data going in is a mess, the outputs will be equally bad, just faster.
That said, AI can be a force multiplier when layered on clean data. You can:
Detect champions who’ve changed jobs by scraping LinkedIn profiles
Extract intent signals from email bodies or meeting notes
Normalize international addresses and job titles
Used correctly, AI becomes a layer that augments your existing processes. But it should never be your first line of defense.
Data Quality Is a Process, Not a Project
This is the mistake I see most often. A company runs a data audit, brings in consultants to clean up their CRM, and then… walks away. Six months later, the same problems are back. That’s because data quality isn’t a one-time fix, it’s a habit.
You need systems that:
Prevent bad data from entering in the first place
Continuously monitor for decay
Automatically correct issues through rules and enrichment
Allow for human overrides when necessary
It’s not glamorous. But it’s how you scale without constantly firefighting.
If you want to build a high-performing RevOps function, you have to care about data quality. Not because it’s fun. Not because it earns you praise. But because it’s the only way to unlock reliable automation, clean handoffs, and predictable revenue outcomes.
You wouldn’t build a GTM engine on top of a wobbly chassis. Don’t build your systems on top of bad data. Fix the foundation. Everything else gets easier after that.