Background

In our last article, we discussed the benefits and risk mitigation provided by a data-driven compliance assessment of payroll and timekeeping data. To summarize, a payroll assessment:

1. Identifies areas of risk for potential remediation efforts.
2. May help avoid or minimize the impact of a lawsuit.   
3. Demonstrates internal compliance with policies and procedures.
4. Due diligence before a merger may help avoid future lawsuits around the sale.

In this article, we discuss how sampling can be an effective tool and the types of data that are typically gathered.

Sampling and Purposes of the Audit

A thorough and sufficient payroll compliance assessment does not require analysis of all available data. As explained in our earlier articles on sampling, a properly defined random sample from the population can be used to correctly and accurately estimate the population.^[1] Assessments can be based on a sample of available information that should be sufficient to cover any potential issues that may arise. For example, if a company has an annual bonus and the purpose of the audit is to evaluate whether the regular rate of pay is properly calculated, then at least one year of data is required to audit the calculation. However, the review can be limited to a sample of employees who received the bonus. We do not need to gather data on all the employees to determine if the regular rate of pay is correctly calculated. If the purpose of the audit is to evaluate meal and rest break compliance, the data from several pay periods for all employees or a random sample of employees and pay periods may be sufficient. 

A properly scoped audit, tailored to the issue(s), using the correct set of records — via sampling or otherwise — can save time and costs, such as the cost incurred to defend future litigation, the cost to remediate, the time to coach and recoach, and the impact to the business and resources if litigation does occur.  

Types of Data

The scope of a data request should fit the purpose of the audit and typically includes timekeeping, payroll, and human resources (HR) data. Many different wage and hour compliance risks can be addressed during a payroll assessment — more on that in our next article. Generally, the following data are required:

• Timekeeping Data – A dataset comprised of employees’ punch records. The data should show the date and time of the punch and should clearly indicate “in” punches (i.e., the start of an employee’s work time), “out” punches (i.e., the end of an employee’s work time), meal and rest periods, and total time worked. A unique employee identifier should also be present.

• Payroll Data – A dataset comprised of employees’ pay records as they would be reflected on a pay stub or wage statement. This information would include pay period start and end dates (i.e., the period of time that the wages and hours were earned). Additional data points would include hours, wage rates, gross earnings, and pay codes (e.g., “regular” and “overtime”).

• Human Resources Data – This should be a dataset comprised of employees, dates of employment, and other categorical information that is relevant to the audit (e.g., departments, supervisors, and/or job titles). For example, this data can be used to identify trends based on compliance in different departments. HR data can also be used to validate the timekeeping and payroll data to ensure that timekeeping and payroll are complete (e.g., the data has appropriate coverage between an employee’s dates of employment).

Other Items to Consider

The audit should also consider the intersection of local, state, and federal laws. An overtime audit may vary between state and federal rules. For example, an audit of California employees must consider both weekly overtime and daily overtime, but an audit in Texas would only have to consider weekly overtime. Another example would be to consider minimum wages for company size and location. Additionally, many municipalities have enacted their own regulations for minimum wages by area and by type of work (e.g., see California’s new minimum wage for fast food restaurant employees).^[2] Audits that span multiple jurisdictions should consider how the rules of those jurisdictions differ and should take appropriate steps to account for those differences.

Conclusion

In our next article, we will explore the various types of analyses that a payroll audit can and should consider. We will also do a deeper dive into how municipal, state, and federal regulations overlap.

Sources

 [1] https://ankura.com/insights/demystifying-statistical-sampling; https://ankura.com/insights/demystifying-statistical-sampling-what-litigators-should-know-about-statistical-sampling-in-labor-and-employment-disputes; and https://ankura.com/insights/de-mystifying-statistical-sampling-extrapolation-and-interpretation

[2] https://www.dir.ca.gov/dlse/Fast-Food-Minimum-Wage-FAQ.htm.

Disclaimer: The analyses described in this article are based on the application of analytical and statistical techniques to payroll, timekeeping, and HR data. They are intended to support risk identification and decision‑making and do not constitute a financial statement audit, legal advice, or a formal determination of compliance.

^{© Copyright 2026. The views expressed herein are those of the author(s) and not necessarily the views of Ankura Consulting Group, LLC, its management, its subsidiaries, its affiliates, or its other professionals. Ankura is not a law firm and cannot provide legal advice.}