Pros and Cons of Each Salary Data Source for Employers


This is an excerpt from our forthcoming whitepaper entitled “The Compensation Data Landscape: A Review of Data Sources, Plus How to Choose.” Enjoy!

Much as the employee data landscape is rich, there are a lot of compensation data sources and types of sources available to employers. As you consider each, pay special attention to the benefits and disadvantages to evaluate whether using a specific source makes sense for your organization.

Traditional (Standard) Surveys and Industry Surveys

This category includes some names you’re probably already familiar with: Mercer, Radford, Aon-Hewitt and Willis Towers Watson. Organizations participate in these surveys by matching their employees to survey job titles and descriptions, then submitting the data to the consulting firm. The firm then verifies and crunches the data to provide distributions back to the participants, and sometimes nonparticipants, typically for a fee.

Many industries offer industry-specific surveys that service only a given vertical. Trade associations will often make data available to their member bases as well.

  • Benefits: The methodology of this data source is well-understood. They typically provide a participant list, giving visibility into which businesses (and competitors) are also participating in the survey. Usually this list contains mostly larger companies.
  • Disadvantages: Sometimes the data are broad or perhaps don’t provide info for more rural areas. And, because the data often lack freshness, they may have gaps for new and hot jobs.
  • Freshness: These sources are usually published annually based on data that are up to nine months old. As a result, they often come with “aging coefficients” to apply to the data.
Traditional comp data sources often lack freshness, and they may have gaps for new and hot jobs.Click To Tweet

Pre-Mixed Data

This category involves the most black-box methodology of all the data sources. It’s clear that the data are market data, but you can’t always be sure what the set consists of. The data provided by pre-mixed data providers are often a curated mix of traditional and industry surveys with some data modeling to fill in the gaps. An example of a pre-mixed data provider is Salary.com.

  • Benefits: Because of the mix of data sources and methodologies, pre-mixed data sets can often be one-stop-shops for compensation data. They can cover many of the gaps that show up in traditional data.
  • Disadvantages: There is low transparency in terms of data source with pre-mixed data. As a result, explaining either methodology or data to anxious managers may be a challenge.
  • Freshness: Because of the black-box nature of these sources, it’s unknown how often the sources are refreshed and updated versus aged or otherwise manipulated.

HRIS or Internal Data

One source of salary information is your own HRIS or other internal data. These data can be used to ensure internal equity. Extracting information from your own workforce has meaning.

  • Benefits: Internal data are great for looking at pay fairness. You can analyze them for compression, below-range pay and compliance concerns like gender pay equity and pay for other protected classes. This data set is an easy resource for running reports to compare departments or people within the same title.
  • Disadvantages: Gathering, sorting and structuring data can be a more manual process. Often analytics are difficult or have to be developed internally. And obviously, internal data do not give you visibility into the external market.
  • Freshness: The freshness will depend on how updated you keep your internal systems.

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Crowdsourced Data

Crowdsourced data sets are what they sound like: data sourced from a crowd. In this case, the “crowd” is employees. Crowdsourced data providers, like PayScale, use a real-time survey as the data collection mechanism. When using crowdsourced data, it’s important to know the validation process for the data.

  • Benefits: Because employees know the most about their own jobs, crowdsourced data allow for much more specific and granular data. They typically cover more jobs and locations, as well as fast-moving and newly emerged jobs.
  • Disadvantages: Some groups are underrepresented with crowdsourced data. Often there is no motivation for executives to fill out online surveys; they know what they’re worth. Similarly, people in minimum wage jobs are less likely to fill out online surveys. Finally, online surveys tend to skew white collar, since they require easy access to a computer or smartphone.
  • Freshness: Crowdsourced data are updated on a daily basis. Things change in real time.

“Scraped” Data

These data are gathered from job listings. The practice of posting compensation data in job listings is much less common in the U.S. (about 25 percent of listings include salary) than in other parts of the world (greater than 80 percent include salary). Data can be extracted from listings using technology or browsing what’s available online.

  • Benefits: This is the only data source that provides direct insight into the demand for labor, since postings are created when a position is open.
  • Disadvantages: Scraped data can be messy, requiring a lot of moving pieces to gather the data. They also don’t reflect actual pay, since it’s not possible to infer from a posting what the incumbents actually received when they accepted the job.
  • Freshness: The freshness of scraped data is variable, since it requires the listings to be available with compensation data associated.

Government Data

The U.S. government provides some very broad compensation data trends. They’re available for some locations and some industries. They’re often fairly dated to the point that they’re no longer relevant or remotely competitive, but they’re free.

Need some pointers on how to choose the best sources for your organization? Don’t miss tomorrow’s blog on just that!

Tell Us What You Think

Which data sources does your organization use for compensation planning? We want to hear from you! Tell us your thoughts in the comments.

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