How to benchmark technical data quality in RevOps

In RevOps, data quality isn’t just an operational necessity—it’s the foundation of your entire go-to-market strategy. In the three-tier RevOps data quality model, the first and foundational tier is technical quality, which ensures your data is trustworthy. Without it, the proverbial “garbage in, garbage out” principle will undermine every downstream report, process, and decision.

This blog dives into how to benchmark technical data quality effectively. Let’s explore the key dimensions—completeness, accuracy, recency, and normalization—and how you can measure them.

What is technical data quality

Technical quality addresses whether your data is reliable and ready to support your RevOps processes. At its core, it evaluates:

• Completeness: are all essential fields populated?
• Accuracy: are the data entries correct?
• Recency: how current is your data?
• Normalization: is your data standardized and consistent?

Each of these elements plays a pivotal role in ensuring your data is “clean” enough to fuel actionable insights. Unfortunately, internal changes and external forces often lead to the accumulation of outdated, incorrect, incomplete, or poorly structured data—what we refer to as data debt. Technical data quality seeks to answer one fundamental question: Can you trust the data? Without trust, even the most sophisticated analytics or workflows are built on a shaky foundation.

Why benchmarking matters

Data with poor technical quality doesn’t just add inefficiencies—it introduces risk. Imagine a lead with an incomplete or incorrect address in a geographically based sales territory, languishing in a black hole because the sales rep who received it has no incentive to correct the error. Routing mistakes lead to missed opportunities or wasted time, frustrating your sales team and potentially losing revenue.

Small errors like this can cascade into significant problems, costing teams time, trust, and revenue, one of the hidden costs of poor data quality in RevOps. By systematically benchmarking technical quality, RevOps teams can quantify issues, identify gaps, and implement solutions to improve the integrity of their data.

How to measure the 4 dimensions of technical data quality

Benchmarking technical data quality involves more than just identifying issues. It requires systematically evaluating your data across measurable dimensions. These dimensions provide a framework for understanding where your data falls short and what steps are needed to make it trustworthy.

Each of the four dimensions—completeness, accuracy, recency, and normalization—plays a role in shaping the reliability of your data. Together, they ensure that your data isn’t just available but also actionable and aligned with your RevOps goals. Below, we break down each dimension and explain how to measure and improve it, along with action steps to get you started.

1. Completeness: are all essential fields filled?

To benchmark completeness:

• Define essential fields for your organization, such as name, company, address, industry codes, or revenue.
• Measure the percentage of records with all required fields populated.

Quick win: Use reporting tools to flag incomplete fields and prioritize updating critical records first.

2. Accuracy: are the entries correct?

Maintaining accuracy in RevOps data is notoriously challenging due to its dynamic nature. Here’s how to measure and improve:

• Use third-party providers like Dun & Bradstreet to verify data.
• Validate information during sales or customer interactions.
• Benchmark accuracy by sampling records for errors or inconsistencies.

Quick win: Compare your database to external sources to confirm company details, ensuring prospects are routed correctly.

3. Recency: is the data up-to-date?

High data decay rates in RevOps make recency critical. Benchmark by:

• Timestamping when records were last updated
• Tracking how many records were updated in the last 3, 6, or 12 months

Quick win: Set automated reminders for periodic updates or leverage tools that refresh key fields based on recent activity.

4. Normalization: is the data standardized?

Standardization ensures your data is user-friendly and operationally efficient.

• Create consistency by standardizing formats for fields like country names or phone numbers (e.g., “United States” vs. “US”).
• Measure normalization by the percentage of records matching established standards.

Quick win: Run automated scripts to normalize common fields, such as converting phone numbers to international format.

Building trust with technical quality

Benchmarking technical data quality is the critical first step in aligning your RevOps processes with reliable, scalable data. Tools like RevOps Data Automation platforms are invaluable for automating enrichment from multiple data vendors, validating and correcting errors in real time, and creating standardization workflows for consistent formats. These platforms reduce repetitive manual efforts, free up team resources, and ensure consistent results at scale.

However, technical quality is only the first step. The next two tiers of the data quality model–operational and strategic data quality–depend on getting this foundational layer right. Without trustworthy data, efforts to take action on your data or make strategic decisions are destined to fail.

Want to dive deeper into actionable strategies for data quality across all three tiers? Download the Authoritative guide to RevOps data quality to unlock the tools, metrics, and insights you need for RevOps success.

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