The Diverse Data of Compensation

Getting compensation right is a high-stakes balancing act. Pay too little, and you risk losing talent to competitors. Pay too much, and you can quickly compromise margins or create internal equity issues.

So how do you find that “just right” range?

Most compensation teams turn to market data. But here’s the catch: there’s no single source that tells the whole story. The compensation market is made up of multiple datasets, each collected differently, verified differently, and useful for different decisions.

Some data sources are deeply audited but slow. Others move quickly but come with less verification. Some show what companies actually paid, while others reflect what candidates think companies are paying.

Understanding these differences helps you build a more complete picture of the market and make better compensation decisions.

Let’s take a closer look at the main sources of compensation data and what each one contributes.

Third-Party Salary Surveys

Traditional compensation surveys are the long-standing backbone of market benchmarking. These datasets are typically produced by HR consultancies like WTW, Mercer, or Radford. Companies participate by submitting their own compensation data in exchange for access to aggregated results. Because the data comes directly from HR teams and is audited by the survey provider, it’s often considered the gold standard for compensation benchmarking.

Third-party surveys bring several advantages:

• High integrity. Data is HR-reported and validated by consultants, which helps reduce errors and inflated job titles.
• Safe harbor compliance. Most surveys are designed to comply with antitrust guidelines.
• Granularity. You can often filter by company size, industry, geography, and job level.

But surveys come with tradeoffs.

First, the data takes time to collect and validate. By the time survey results are published, they’re often six to twelve months old. In fast-moving talent markets, that lag can matter.

Second, participation can be labor-intensive. Compensation teams must map internal roles to the survey’s job library, which often requires a lot of manual matching work.

If you’re evaluating different survey providers or deciding which ones make sense for your organization, our guide on finding the right salary surveys can help. And once you’re using survey data, understanding concepts like the aging factor for survey data becomes important to keep your benchmarks relevant.

Boutique Salary Surveys

Sometimes, broad surveys don’t go deep enough. Boutique compensation datasets focus on very specific segments of the market. They might concentrate on a single industry, company stage, geographic region, or highly specialized roles.

For example, a venture capital network might produce compensation benchmarks specifically for Series B SaaS startups. A niche consulting firm might run surveys focused on biotech or fintech roles.

Because these surveys target narrower markets, they often provide more relevant comparisons for specialized talent pools. They may also be more current than large industry surveys. Some boutique surveys are ‘by invitation only’ and require an application to join and participate.

However, the narrower scope also means smaller sample sizes. That can make the data more volatile, especially if only a handful of companies contribute. Boutique datasets work best as a supplement to broader market surveys rather than a replacement.

Peer-Reported Data

Peer data comes directly from organizations similar to yours. Instead of looking at a broad market, you focus on a defined group of competitors.

This data is usually shared through compensation networks, investor communities, or technology integrations between HR systems. In many cases, it’s reported directly by HR teams through system-to-system connections, which makes the data highly trustworthy.

Why peer data is powerful

Peer datasets can give you insight into the companies you actually compete with for talent.

Precision and relevance

If you’re a mid-stage fintech company, knowing what large banks pay might not help much. Peer data shows what comparable organizations are doing.

Competitive strategy

It helps you understand the offers your candidates might be receiving elsewhere. That context allows you to adjust hiring packages or retention strategies.

Executive credibility

Boards and leadership teams often find peer comparisons more persuasive than broad market averages.

The limitations of peer data

Even though peer datasets can be extremely useful, they come with a few risks.

Small sample sizes

A small peer group can skew quickly if one company introduces a large retention bonus or unusual equity package. Similarly, if a company leaves the network, their data is pulled from the pool, shrinking the peer group and skewing the data toward the remaining companies’ data.

Echo chamber effects

If everyone only benchmarks against each other, compensation levels can drift away from broader labor market trends.

Legal considerations

In many jurisdictions, sharing current salary information directly with competitors can create antitrust concerns. That’s why peer data is often mediated by a third party.

Real-Time Aggregated Compensation Data

Newer compensation platforms and data providers combine multiple sources into a single dataset.

These aggregated datasets typically blend:

• HR survey data
• employee-reported data
• algorithmic estimates
• offer data from ATS
• other market inputs

The goal is to create a continuously updated market signal rather than relying on a once-per-year survey cycle. Real-time aggregated data has some clear benefits. It can help you understand how the market is shifting today, especially for competitive or emerging roles.

But aggregated data also comes with some uncertainty. Because these datasets rely on blended inputs, it can be difficult to understand exactly which sources and market forces are influencing a specific benchmark.

That “black box” challenge is something many compensation teams consider when evaluating aggregated datasets. It helps to separate two things that often get bundled under the same label. Real-time data sources add value by surfacing signal for volatile job categories and fast-moving markets, and their main tradeoff is depth and confidence, since a fresh signal isn’t always a deep one. Aggregated data, on the other hand, blends several sources into a single number, and its tradeoff is that black box quality, since it’s hard to see which inputs are actually driving the result. Both are useful. They just carry different risks.

If you’re combining surveys with newer datasets, our article on pairing salary surveys with real-time data explores how the two can complement each other.

Crowdsourced and Public Compensation Data

Not all compensation data comes from HR teams. Some of it comes directly from employees. Sites like Levels.fyi and Glassdoor allow workers to report their salaries, bonuses, and equity packages. Because this data is publicly accessible, it often shapes how candidates perceive the market.

Crowdsourced datasets can be useful in a few ways:

• They show what candidates see when researching your company.
• They often include emerging roles that may not yet appear in formal surveys.
• They’re frequently updated.

However, this type of data has clear limitations. Self-reported information isn’t verified. Employees may omit parts of their compensation package, exaggerate certain numbers, or misunderstand how bonuses and equity are structured.

There’s also the issue of sampling bias. People who submit compensation data online aren’t always representative of the broader workforce. Ghost job postings aren’t just a concern for jobseekers, they could also add misaligned data to the larger pool. 

That doesn’t make crowdsourced data useless. It just means you should treat it as a signal rather than a definitive benchmark.

Government and Scraped Labor Market Data

Another category of compensation information comes from public datasets. Government labor statistics and scraped job posting data can provide valuable insight into regional labor markets and compensation trends.

Compensation teams often use these sources to understand broader market movements, like:

• Regional cost-of-labor differences
• Emerging job categories
• Industry-wide pay trends

But these datasets typically lack the detail needed for precise benchmarking. Job categories are often too broad to match modern role structures, and job postings reflect intended salary ranges rather than the compensation that candidates ultimately accept.

Still, they’re useful for understanding the broader economic context around compensation.

Bringing Multiple Data Sources Together

No single dataset gives you a perfect view of the compensation market.

In practice, most compensation teams rely on several sources at once:

• Traditional surveys for structured benchmarking
• Peer data for competitive intelligence
• Aggregated datasets for real-time signals
• Crowdsourced data to understand candidate expectations
• Government data for broader labor trends
• Company proxies (for many C-suite roles)

The real challenge isn’t accessing these datasets. It’s reconciling them.

Each data source uses different job titles, leveling structures, and taxonomies. Matching internal roles to multiple external datasets can become time-consuming very quickly.

That’s where strong job architecture and data management processes become critical. If you’re thinking about how internal job structures affect your benchmarking, our article on keeping job and comp data in sync explores this challenge in more detail.

Turning Compensation Data into Decisions

Compensation data is only useful if you can apply it effectively.

Surveys provide structure. Peer data provides competitive context. Aggregated datasets add speed. Crowdsourced platforms reveal candidate expectations.

The best compensation strategies combine these signals rather than relying on a single dataset. And once you start using multiple data sources, the operational side of compensation becomes just as important as the data itself.

Platforms like Bettercomp help compensation teams bring these datasets together. You can load different types of market data, speed up job matching with AI-powered job recommendations, and simplify benchmarking through job family mapping.

Instead of spending hours manually reconciling survey job libraries with your internal roles, you can focus on what matters most: building a compensation strategy that attracts and retains great talent.