Why the “Why” Behind Selecting Comp Data Sources Matters More Than Ever

I recently had the privilege of moderating a panel of some of the sharpest minds in compensation: Belinda Roberts from Mercer, Charlie Franklin from Compa, Ephraim Edelman from Aon, and Heather Ryan from WTW. The topic was one that comes up in nearly every conversation I have with compensation leaders today: how do you navigate a market intelligence landscape that has more data, more sources, and more noise than ever before?

We opened the session with a simple poll: what is your biggest compensation data challenge today? Sixty percent of attendees pointed to the same answer: finding data for new and emerging roles. That single data point told us everything we needed to know about why this conversation matters right now. The old playbook of waiting for next year’s survey cycle does not work when the roles you are trying to price did not exist eighteen months ago.

Here are our recommendations for bringing together multiple data sources while staying true to your compensation philosophy.

Start with a data north star, not a data shopping list.

When I asked the panelists how compensation teams should combine the growing universe of data effectively, the consensus was immediate: do not start with the data. Start with your philosophy.

A resilient strategy begins by defining what you are actually trying to achieve.

• Are you optimizing for competitive pay, internal equity, retention, or all of the above?
• How do you want to benchmark the lion’s share of your jobs: through ranges, market reference zones, or survey anchors?

These are the decisions that form the foundation of every pricing choice that follows. Without that north star, more data simply means more confusion.

Match the data source to the problem you are solving

One of the clearest threads through the discussion was that not every data source is built for every job: real-time data is excellent for faster-moving, specialized roles, where job postings, offers, and new hire data can tell you whether you need to extend your ranges to accommodate hot skills. But real-time data fluctuates quickly, which is why Belinda she recommends anchoring it with stable, traditional survey data.

The instinct when you face a gap is often to buy another survey, but that is not always the right answer. Surveys are designed for stability. Other data sources are designed for other purposes: speed, granularity, signal on emerging skills — but they don’t replace the need for surveys. The job of a modern compensation leader is to purpose-fit the source to the question, and to be honest about the fact that different parts of your workforce will evolve at different rates.

What’s necessary to keep in mind about compensation data is that while all data is not created equally, each data source carries their weight in their own unique ways.

• Third-Party Salary Surveys: Best for structured, audited benchmarking with high data integrity; ideal when you need granularity by industry, company size, or geography and safe harbor compliance.
• Boutique Salary Surveys: Best for highly specialized roles, niche industries, or specific company stages (e.g., Series B SaaS, biotech) where broad surveys lack sufficient depth.
• Peer-Reported Data: Best for competitive intelligence against companies you directly compete with for talent; especially persuasive for boards and leadership.
• Real-Time Aggregated Data: Best for understanding how the market is shifting right now, particularly for fast-moving or emerging roles where annual survey lag is a problem.
• Crowdsourced Data: Best for understanding candidate perception and expectations, and for spotting emerging roles not yet captured in formal surveys.

However, what’s necessary to keep in mind when curating a variety of data sources is where their drawbacks lie and what your exact purpose is for using them. For instance, Charlie pointed out that while you don’t always need a new traditional survey added to your data set, you may want to explore a new type of data to supplement additional information into your compensation program.

Avoid the trap of more for more’s sake

Our panel raised something I hear from comp teams constantly: analysis paralysis. Technology has made it possible to pull in more data sources than ever before, and that is genuinely powerful, but only when there is intent behind it. More data adds more risk if you are not strategic about why you are pulling it in. The intent is what drives meaningful analysis. Volume alone does not.

There are two questions every compensation leader should be able to answer about every data source they use: What am I using? Why am I using it?

The question, “What am I using?” goes deeper than naming the vendor. It means understanding how the data was collected, how it is verified, how current it is, and what its limitations are. A few sub-questions that fall under this umbrella are:

• Is the data HR-reported and audited, or self-reported by employees?
• Is the data refreshed annually or in near real time?
• Does this data set cover the industries, geographies, and job levels that are actually relevant to your workforce?

A data source you cannot describe with confidence is a data source you cannot defend, and one that can quietly introduce inconsistency into your benchmarking without you realizing it.

Answering, “Why am I using it?” means being able to draw a direct line between that data source and a specific decision or gap it is helping you address. Some follow up questions to address when answering this big question are:

• Is this data anchoring your ranges for stable, high-volume roles?
• Will this data give you a faster signal on a competitive skill set?
• Is this data going to help you understand what candidates see when they research your company?

Each of those is a legitimate reason, but they are different reasons that call for different sources. If the honest answer is “we have always included it” or “someone added it a few years ago,” that is worth revisiting. A source without a clear purpose adds noise to your analysis and makes it harder to act with confidence when numbers conflict

If you cannot answer both what the data is and why you are using it, you are not running a data strategy. You are running a collection.

The takeaway

The new era of market intelligence is not about who has the most data. It is about who has the clearest reasoning behind their choices. Be confident in the why behind your compensation decisions by solidifying your compensation philosophy first, then evaluating the why behind every data source you bring in to support it.

At Bettercomp, this is the work we see compensation leaders doing every day — moving from data collection to data conviction. The teams who get this right are not the ones with the biggest data stack. They are the ones who can defend every choice they make with a clear line back to their philosophy.