Benchmarking data is more than a set of numbers—it is the foundation for meaningful performance improvement. According to APQC’s Benchmarking Quick Reference Guide, benchmarking is an approach organizations use to measure their internal processes and look externally to identify, understand, and adapt practices from best-in-class organizations. In other words, benchmarking data provides the evidence organizations need to compare performance, uncover gaps, and identify proven practices that lead to better results.
At its core, benchmarking data connects internal performance to external standards. Organizations use it to improve profits and effectiveness, accelerate change, set stretch goals, and achieve breakthroughs. Rather than relying on assumptions, leaders can use benchmarking data to understand how their processes perform relative to peers and top performers across industries. This comparison creates urgency, reveals blind spots, and provides a roadmap for improvement.
However, not all benchmarking data is created equal. The quality, relevance, and credibility of the data determine whether it drives real insight—or confusion.
How to Determine What Is Good Benchmarking Data
Good benchmarking data is trusted, validated, and neutral.
First, trusted data comes from credible, established sources. Organizations often struggle with finding relevant, timely, and reliable metrics. Trusted benchmarking databases, such as APQC’s Open Standards Benchmarking® database, provide structured, consistently defined metrics that allow organizations to make meaningful comparisons. A common vocabulary—like APQC’s Process Classification Framework (PCF)®—also supports consistency across industries and geographies.
Second, validated data has been reviewed for accuracy and comparability. During the collection phase of benchmarking, teams are advised to validate the data and confirm that participating organizations truly represent high performance. Without validation, organizations risk basing decisions on outliers, misinterpretations, or poorly defined measures. Normalizing data—converting it into comparable units—is another key step to ensure apples-to-apples comparisons.
Finally, neutral data is unbiased and structured for objective comparison. Reliable benchmarking sources aggregate and blind data where appropriate, reducing the risk of skewed results or self-reported bias. Neutral data allows leaders to focus on performance gaps and enablers rather than anecdotal claims.
How to Determine What Benchmarking Data to Collect
Selecting the right data begins with clarity of purpose. In the planning phase of benchmarking, teams identify a pain point or broken process and clearly define the study’s scope, measures, and objectives. Benchmarking should focus on processes that have the greatest impact on business objectives and results.
Organizations should:
- Identify current performance measures.
- Define the performance gaps they want to close.
- Select KPIs aligned to strategic priorities.
- Determine which processes are most critical to improve.
The Process Classification Framework (PCF)® can help define work processes comprehensively and without redundancy, ensuring benchmarking efforts remain focused and manageable. By understanding both the process and its sub-processes, organizations can select meaningful metrics that reflect cost, cycle time, quality, risk, or customer satisfaction.
In short, benchmarking data is powerful when it is credible and purposeful. When organizations collect trusted, validated, and neutral data—and align it to strategic goals—they turn comparison into competitive advantage.