Benchmark Gaps: Staying Market-Driven When Comp Data Is Missing

Organizations today strive to be market-driven in their compensation strategies, aligning pay structures with external benchmarks to attract and retain talent. However, a common challenge arises when there isn’t enough reliable compensation data for a particular job, level, location or industry. Situations may arise, such as:

“We have Professional (P) level 3 data but not P2.”
“The P4 data is lower than the P3 data.”
“We like to benchmark against the life sciences industry, but there’s not much data in France.”

This data gap can put HR and its compensation professionals in a bind, caught between wanting to be data-driven and needing to make informed pay decisions. This article explores practical, evidence-based strategies for managing situations where compensation data is missing, while still ensuring decisions remain fair, consistent and justifiable.

Why Compensation Data Might Be Missing

These types of gaps typically occur in two forms:

Insufficient market data. Even when a role is part of the benchmarking database, data might not be available in a specific market or scope cut due to small sample sizes or low participation.
New or niche roles. Emerging roles in technology, life sciences or specialized business functions may not yet have established reference points in your benchmarking data. Titles like “AI ethicist” or “sustainability strategist” may not show up in major surveys.

Strategies to Fill the Gap

When faced with missing data, compensation professionals should lean on logic, structure and internal equity to craft solutions. Here are some strategies that are generally effective in navigating these types of situations:

1. Estimate from Adjacent Levels

When level-specific data is missing, look at the same role in other levels (above and below). Use existing level progressions to interpolate or extrapolate a likely pay range.

Example: If you have compensation data for levels P1 and P3 in France but are missing P2, refer to the same role in the United States. Calculate the midpoint progression between P1, P2 and P3 in the U.S., and then apply that progression to estimate the P2 midpoint in France.
Pro tip: Review the progression between levels to ensure alignment with internal progression logic. If there are big jumps due to skill premiums, adjust accordingly.

2. Use Broader Scope Parameters

If you are using a narrow scope and encounter missing or limited data, broaden your parameters to increase market insight. In such circumstances, expand the data search horizontally to help approximate compensation expectations.

Examples:
- Industry: If you can’t find data in software services, expand the search across all industries or to a closely aligned sector (e.g., information technology).
- Revenue scope: If you are unable to find compensation data for companies of a specific revenue size (e.g., less than $50 million), use an all-revenue cut.
Pro tip: Be mindful of key variances. Industries like tech typically offer higher pay, while nonprofits tend to pay less. Additionally, organization size can significantly impact salary levels, particularly for leadership roles, so adjustments may be needed to reflect organizational context.

3. Use “Roll-Up” or Similar Functional Matches

When exact job matches don’t exist, identify a role with similar outputs, responsibilities and scope. Some surveys include additional job matches in a “roll-up” format, combining data for several similar jobs at a given level and then aggregating to provide a broader, more comprehensive sample. Use this as a base when job-specific cuts are missing.

Example: If “analytics engineer” is missing, consider data from a similar “data engineering” role or a broader “data science” roll-up match.
Pro tip: As with roll-ups, beware of hidden value differences that you reasonably suspect may be tied to certain skills, specialized knowledge or role-specific certifications. Document assumptions made when selecting functional equivalents.

4. Leverage Internal Slotting

When external benchmarks fall short, turn to internal value alignment. Identify roles with comparable scope, complexity or internal value and use them as reference points to maintain consistency.

Example: If “machine learning strategist” has no data but is valued like a “data science lead,” adopt the same pay range.
Pro tip: Consider the job’s value, not the person. Avoid letting an individual’s credentials skew the benchmark.

Geographic Gaps: What If I Don’t Have Data in a Given Country?

Applying exchange rates or simple cost-of-labor adjustments to translate benchmarks into another country’s local currency rather than relying on local benchmarks can lead to major misalignment in compensation, as labor costs can vary significantly by region.

For example, in one benchmarking data source, a P2 software engineer in the U.S. earns a median salary of $138,000. Using the exchange rate for Denmark (approximately 6.85 Danish krone [DKK] to $1 U.S.) would suggest equivalent pay should be DKK 945,000. However, local market data in the same benchmarking database shows the typical salary for this role in Denmark is only DKK 640,000, a variance of nearly 50%.

It is highly recommended to purchase local market benchmarking data whenever possible. There are more choices than ever at a variety of price points to help compensation professionals be data-driven in multiple markets.

Documentation and Governance Are Key

In all these cases, consistency matters. Document the rationale behind any proxy or assumption used. Include:

The data sets consulted;
The reasoning for the chosen substitutes;
Any adjustments applied (e.g., for specialty or geography); and,
The internal value-slotting logic.

This documentation provides defensibility and promotes fairness.

Being Market-Driven Without Being Data-Dependent

A market-driven strategy doesn’t require perfect data. It does, however, require a disciplined, logical approach to inference and estimation. When data is missing, your role as a compensation leader is to fill gaps with smart substitutes, document your decisions and remain consistent in your methodology.

By following these strategies, you can preserve market alignment and internal equity, even when compensation data is sparse or unavailable.

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