How to benchmark operational data quality in RevOps

In the world of RevOps, having technically clean data is like having a perfectly tuned race car engine – it’s great, but pretty useless if you can’t get that power to the wheels. That’s where operational data quality comes in. While technical quality ensures your data is trustworthy, operational quality determines whether you can actually use it to drive revenue.

Let’s dive into how to measure and benchmark operational data quality effectively. It’s trickier than measuring technical quality, but if you get it right, it’s the difference between having a showroom car and one that actually wins races.

What is operational data quality?

Before we start measuring anything, let’s be clear about operational quality, part of the three-tier data quality model that also includes technical but also strategic data quality. Operational quality isn’t just about having clean data; it’s about having data you can take timely action on. This means:

• Related datasets are properly linked (like having contacts correctly matched to accounts)
• Data is formatted correctly for the systems that need it
• Information is assigned to the right users at the right time
• Unnecessary data is hidden to reduce noise

Companies invest in data for two main reasons: to derive insights for decision-making and to enable and automate business processes. Even if your data is technically flawless, it can be operationally useless if end users can’t use it for necessary tasks and GTM decisions. Operational quality takes technically sound data and makes it actionable.

Direct vs. indirect metrics

Here’s where things get interesting. Unlike technical quality, which has fairly straightforward measurements, operational quality requires both direct and indirect metrics to get the full picture.

Direct metrics are the straightforward measurements you can pull directly from your systems, including:

• Percentage of person records linked to accounts
• Number of contacts properly linked to opportunities
• Distribution of accounts owned by each sales rep
• Percentage of records with conflicting ownership
• Number of person and account records with no linkage and no activity

Indirect metrics are the real-world impact measurements that tell you if your operational quality is actually delivering results:

• Time for sales to follow up on inbound leads
• Number of times a lead gets rerouted
• Time required to load a list from an event
• How long it takes to build a new report
• Time needed to build a campaign list

The hidden cost of poor operational quality

You might be wondering, “But how much does this really matter?” Let me paint you a picture. Imagine your marketing team just ran a killer campaign. The data is technically perfect – every field is filled out correctly, every email is valid. But because of poor operational quality:

• Half the leads are sitting unassigned because your routing rules can’t handle their job titles
• The ones that did get assigned went to the wrong territory because the address standardization is inconsistent
• Your sales team can’t find the engagement history because it’s not properly linked

Without operational quality, your campaign success is significantly diminished. When your data lacks proper linking and standardization, ops teams resort to taking matters into their own hands, remediating the data just enough to get their work done. This type of one-time effort is repeated over and over again across the entire organization, causing inefficiency at scale.

Benchmarking operational quality

Unlike technical quality, which focuses on individual record integrity, operational quality must be measured across key data relationships and processes. To effectively benchmark operational quality, you need to understand both what you’re measuring and how to measure it.

Understanding what to measure

Operational quality primarily involves making data linkages actionable. The core use cases you need to benchmark include:

• Linking a person to an account (lead-to-account matching)
• Linking accounts across systems
• Assigning leads and accounts to sales territories, sales teams, CRM users and queues
• Assigning sales teams and CRM users to sales territories
• Linking parent and child accounts
• Assigning contacts to buying centers/demand units
• Assigning contacts to opportunities and buying roles on the opportunity
• Assigning product entitlements to accounts
• Linking intent data to accounts
• Linking engagements to campaigns

Building your measurement framework

While technical quality metrics are fairly standard across companies, operational quality metrics need to be tailored to your organization’s specific needs. For each of the use cases above, your measurement framework should incorporate both the direct metrics that track data relationships and the indirect performance metrics that reveal operational impact.

When evaluating these metrics, remember that operational quality measurements are unique to each organization. Your benchmarking approach should:

• Establish baseline measurements for each key use case
• Track how metrics change over time compared to historical data
• Regularly review and adjust metric definitions if results seem misaligned with actual performance
• Balance coverage metrics (how much of your data meets quality standards) with performance metrics (how effectively that data drives processes)

The goal is not just to track numbers, but to understand how effectively your data enables your GTM processes. This requires ongoing monitoring and adjustment as your business grows and your processes evolve.

The bottom line

In RevOps, operational data quality determines whether you’re truly in the race or just idling in the pit. By systematically benchmarking your operational quality metrics, you can identify performance gaps and optimize your data systems before they impact your revenue performance.

Want to dive deeper into all three tiers of RevOps data quality? Check out our comprehensive guide that breaks down technical, operational, and strategic quality in detail.

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