A proactive compensation audit can be an efficient and cost-effective way to review your organization’s pay equity, check for legal compliance, and maintain internal compensation consistency. However, the insights that can be generated are only as good as the underlying data. HR compensation teams can save time and money by addressing certain data issues before conducting the audit. Here are examples of some of the most common human resource information system (HRIS) data pitfalls to look for in preparation for a compensation audit.
Inconsistencies in Dates
Many employers have instances in which an employee’s date in job or date in grade in the data is earlier than their date of hire. Perhaps this is someone who departed and was rehired but only their date of hire was updated in the data, or an individual joined the employer via a merger or acquisition and issues arose regarding how much credit the employees receive for years of service with their original organization.
Problem: The data may not provide accurate and consistent information on how long this individual has been in their job or pay grade, and how long they’ve been with the employer overall. Given that tenure is often one of the most influential ingredients in the compensation structure, it is imperative that the dates in the data are correct and internally consistent.
Solution: Check for this inconsistency beforehand. Include a field such as continuous service date, which represents the individual’s total service time with the organization. For individuals who join the organization through a merger or acquisition, consider including a measurement of their tenure with the predecessor entity. This will allow an analyst to measure the employees’ years in their role as well as years with the organization.
Same Job Title, Different Classification
Sometimes individuals with the same job title are compensated under different schemes or have a different exempt/nonexempt status. This can happen if an employer has broadly defined job titles or an outdated job architecture.
Problem: These individuals might be quite different in terms of their compensation or scope of responsibility but in the data would appear to be doing the same work. This makes it difficult to identify the correct apples-to-apples comparison of people with similar qualifications and responsibilities.
Solution: Tailor the job title more narrowly to the specific scope of responsibility and compensation scheme such that employees in the same title have similar levels of qualifications and responsibilities. For example, distinguish “sales associate” from “sales manager” or “sales associate exempt” from “sales associate nonexempt.”
Missing Demographic Information
External compensation audits often focus on key demographic factors such as gender, race/ethnicity, and age. However, an employer may be inconsistent in collecting this information or, increasingly, employees choose not to disclose their demographic information.
Problem: It is not necessarily the case that those who choose not to disclose their demographic information are representative of those whose demographics are known. As such, the exclusion of these individuals may produce biased results of an analysis that only contains those who chose to self-disclose their demographic information.
Solution: Individuals with unknown race or ethnicity could be assigned a “not disclosed” race/ethnicity category, which ensures that everyone who was a participant in the compensation decision-making process would remain in the statistical model.
Follow legal counsel’s guidance on what steps are permissible to fill in missing demographic information. Some options may be to ensure that self-identification surveys of demographic information are part of the onboarding process. Consider follow-up surveys of incumbent employees. If missing information is still prevalent, consider getting the advice of employment counsel about the possibility of asking managers to visually identify their direct reports, while clearly distinguishing self-identified data from manager-identified data.
Inclusion of Nonapplicable Population
The data provided to the analyst may include a wider population than the key population to analyze. For example, the data may include executives under a different compensation plan, unionized employees, temporary/seasonal workers, part-time workers, or interns.
Problem: These individuals may be systematically different from the overall pool of regular full-time employees and thus cause misleading findings in the analysis.
Solution: Think carefully about defining the relevant population for analysis. Limit the data to the set of workers applicable for a pay equity study.
Missing or Outdated Education and Skill Data
Some roles have specific education or skill requirements to enter the role, while for others, these provide opportunities for advancement and increased compensation. However, some employers consider education and skills during the application process or the performance review process without recording any specifics in their HRIS data.
Problem: Individuals with a particular level of education or a particular skill set may differ in pay from those without these characteristics, but an analyst can only observe these characteristics if they are systematically recorded. In some instances, an employee completes an academic degree or acquires tangible skills while employed, but the data may reflect only their education and skill level at hire.
Solution: Periodically survey managers and direct reports for this information, including incumbent employees. This information can go a long way in distinguishing the compensation of one individual versus another.
Imprecise Pay Grades
As time goes on and an organization faces changes in the internal and external labor markets, it is necessary to make pay adjustments to retain key talent and attract new talent. However, ad hoc deviations from a set pay scale structure can lead to a misalignment between a carefully managed compensation system and market realities.
Problem: Pay adjustments over time can lead to actual pay that is not aligned with the planned pay structure. For example, suppose pay grade 2 ranges from $70,000 to $180,000 and pay grade 3 ranges from $75,000 to $150,000. Pay grades that are wide and overlapping are less optimal for a statistical analysis to identify, for example, similar levels of responsibility across differing functional areas.
Solution: Whenever there is an individualized change in pay, consider reviewing where the individual falls within the pay structure. When appropriate, the pay structure can be recalibrated to more closely reflect scope of responsibilities across functional areas. Work with legal counsel and conduct compensation audits on a regular basis to identify opportunities and follow workforce trends.
Consider Alternative Options
Understanding the strengths and weaknesses of the data is crucial for the validity of a compensation audit. The solutions mentioned above may generally apply, but there may be unique circumstances that call for alternative options. Regardless, working with legal counsel, management, the compensation and HR team, and an experienced analyst will help employers achieve their compensation goals.
Paul F. White, Ph.D., is a labor economist and partner with Resolution Economics LLC, headquartered in Washington, D.C. Daniel Galitsky, MBA, is a senior manager at Resolution Economics LLC. White is a speaker at LEAP Employment Law Conference, In Partnership With SHRM. LEAP 2027 will be held from March 31 to April 2, 2027, in Las Vegas.
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