Data Hygiene: Passing The First Pay Audit

Leaders enter their first gender pay gap audit feeling confident. All of the pillars are set for the payroll to run on time and people are receiving the correct amount of money. This runs true end to end. The assumption of owning clean data as a result of this workflow would also run true, you would think. If the machine works, the data underneath it must be sound.

Contrary to this belief, the data is rarely clean or enough.

The truth we see on a regular basis is that pay audits don't fail because of poor intentions or software limitations. They fail due to the underlying bad data. No employee sets out to build a messy data factory but the mess accrues quietly. It then breaks when someone asks this messy data factory to prove something that it doesn't have the means of proving.

The 3 Pitfalls of Unclean Data

Multiple Sources of Truth

The number of systems required for people data grows. The data is contained within a HRIS, a payroll system, an accounting platform and each system has different data pertaining to each person.

If a promotion occurs in March, it will be logged in the HRIS. The payroll system will then reflect the promotion in the next pay cycle. A full department is rebranded and a new name for the team surfaces. The hours for a part-time worker are changed and both systems are conflicted on what the right number of hours are for this particular worker. Individually these are small changes. On an aggregate level, they create the question, what is the real source of truth? One source of truth is required for a gender pay gap audit.

Job Classification Gaps and Incomplete Records

• Job categories that are outdated and no longer correlate with the current job title.
• Missing written agreements for scheduling and pay. Verbal understandings do not allow for compliant pay auditing.
• Allowances and bonuses that sit outside the total compensation do not allow for transparent pay practices.

Many practices create gaps and records that can be exploited through an audit. These gaps can remain invisible until an auditor makes an effort to group employees into job categories and establishes that there is no methodology for how to do so.

The Ownership Vacuum

Data ownership sits between three functions. HR owns the employee lifecycle. Payroll owns the money. Finance owns the reporting. Every function may presume that another is preserving the records. This creates a vacuum whereby an outdated job code becomes a problem that joins every function but is the responsibility of nobody. As a result, data quality can deteriorate quietly. Accountability slipping through the cracks deteriorates data hygiene between systems that were not designed to communicate.

The correct connector software proves its worth in this case. A platform built for pay analysis does more than calculate. Three job functions now have a shared view of the same records. It flags gaps and mismatches automatically. It is clear which team must act. An invisible shared responsibility now has one owner.

Proactivity Beats Reactivity

1. Unify your data sources to a pay analysis software before any formal pay audit - Combine your HRIS data with your time and attendance software as well as your payroll systems in the one pay analysis software. Find the contested fields and clean them using the one source of truth, the pay analysis software. It is also the single most valuable process in gender pay gap reporting. Every number following this depends on it.
2. A standardised job architecture - Agree on a consistent job-level framework. Hold this job framework within your pay analysis software. Apply it uniformly across the organisation. One simple objective arises here. When questioned whether two particular people are comparable, the data within the job-level framework proves it on its own.
3. Progress towards pre screening - Data quality is not something to repair after the fact. Internal checks to sustain compliance should be made at the pre hiring stage to maintain current compliant data.

Conclusion

Pay audits and reporting should not be a scramble. Clean, unified and standardised data changes the narrative. It is no longer a threat, it becomes a valuable process. It creates a defensible picture of how an organisation operates in an equitable and transparent way.

Organisations that thrive through pay transparency don't have the largest budgets. They are organised at an early stage. PayAlign can help you evaluate your readiness and act as your pay analysis software to be your source of truth for every pay audit and report.

Frequently Asked Questions

How often should a company perform payroll data hygiene checks?

This is a continuous task. Internal pre-checks for new hires as well as every pay cycle or every quarter catch divergence at more regular times. It should be supported by a fuller review every 12 months. Changing data at a regulatory deadline is too late.

What is the difference between a system audit and a data hygiene review?

A system audit relates to software configuration. It confirms that everything is working correctly. A data hygiene review confirms the information within that software is accurate. A system that is configured correctly can produce faulty results due to incorrect underlying data.

What is the single biggest data issue that causes pay equity or audit projects to stall?

Inconsistent job classification. Job levels and job titles must be standardised across the organisation. Comparable roles cannot be grouped which creates a challenge to every equity comparison. Create a job architecture as a main priority. The remaining problems become considerably easier to solve after this.