Compensation data is an essential element for organizations to competitively recruit and retain top talent. This data is used to ensure market competitiveness with employment offers to providing a foundation for complete compensation strategy reviews. Be careful about the data you use for compensation decisions. It should be from credible compensation surveys.
Compensation surveys can be complex and overwhelming at first glance. The complexity and dynamics involved in gathering survey data may tempt some to seek less sophisticated options (e.g., Google.) However, although free data might be right at your fingertips, it’s important to take into consideration the accuracy and credibility of that data.
Here’s a few key takeaways about compensation data:
Compensation or Salary data should tell you more than just job title and geographic area
When it comes to evaluating your compensation data options, it’s important to understand that more goes into it than a job title and location. The data can change based on some of the demographic breakouts and sample sizes.
Data should be matched to a survey job description versus a job title, based on key skills, responsibilities, experience, and education requirements, which you can tailor by industry, organizational size, and geography.
The compensation data you’re using should also include an effective date, demographic breakouts, sample size, job titles, descriptions, a median, other percentiles, employer and weighted averages, and types of pay reported. The more information you have, the better, (and the more credible.)
For instance, the exact same job title can have significant difference from one company to another, so make sure that both the day-to-day responsibilities, education level, years of experience, skill set, and organization size/revenue are similar. In addition, many hybrid or combination positions exist in today’s complex organizations, therefore matching jobs with data can be complicated.
It’s also good to consider whether the compensation data provides a representative sample or not. Is the data you are reviewing accounting for situations of insufficient data, or does it just give you what has been collected (which may not be reflective of the true averages?)
Be wary of internet compensation data
- Employees or individuals that cannot be verified
- Compensation surveys with participation that does not represent the broader marketplace
Data provided should be from independent and unbiased sources, typically an HR department source. The participant reporting group should be comprehensive, resulting in statistically significant data that is not skewed.
There’s also the possibility that with the crowdsourcing of compensation data, it could be inflated.
ERC HR Help Desk Advisor, Tara Motheral, notes that “it’s important to realize that data sources that are collecting through a self-reporting mechanism, allowing employees to take liberties with their own salary. Employees could have a lot to gain by inflating the numbers. Salary data that is reported by employers is far more likely to paint a reliable picture of what people are truly getting paid.”
Job titles might not accurately reflect the job description. Overall, if the data is not granular then it is not a very good indicator of how much a job is actually paid.
Unreliable data may cost you top talent
Your organization’s compensation philosophy or strategy may very well align with being competitive but if you’re not using reliable data, it could cost you more in the long run.
The compensation you provide your employees should accurately reflect the market and their performance.
ERC Senior Compensation & Benefits Consultant, Sue Bailey, says that “the key to making sound compensation decisions is access to credible, reliable, and relevant market data, aligned with an organizations compensation strategy. Be careful about the compensation survey sources you select and the way in which you interpret the data, as the results can vary significantly.”
Questions to ask yourself when evaluating compensation surveys
- Does the compensation data provide a representative sample?
- Is the job description actually a good match for the position that is being benchmarked?
- How expansive is the data?
- Does the data take into account compensation beyond annual salary?
- How current is the data?