Missing or Incomplete Employment Data – Understanding Data Gaps Is A Key Compliance Strategy

Data is the foundation of workforce analytics, compliance reporting, and many of the employment decisions organizations make. A good HRIS and ATS are essential to capturing employment activity, but even the most comprehensive HRIS and ATS can have gaps. Missing information, inconsistent coding, changes in data collection practices, and other data quality issues can all affect an analysis. Knowing where your data has limitations can be one of the most valuable steps in the analytical process. It allows organizations to better understand their results, address potential issues proactively, and make more informed decisions.

Why Data Gaps Matter

Data shortfalls can take many forms. An applicant record may be missing a disposition reason, employee records may contain inconsistent job classifications, or demographic information may not have been collected consistently over time. In some cases, the issue may be easy to correct. In others, historical information may simply not be available.

The first step is understanding what the gap is and how it could affect the analysis. Knowing where potential data gaps exist can help organizations:

• Identify limitations
• Determine where data cleanup is needed and establish a plan to improve data collection going forward.
• Better understand whether an issue is isolated or systemic.

Most importantly, identifying a data gap provides context when analyzing workforce data. It helps distinguish between a result that reflects an actual workforce trend and one that may be influenced by limitations in the underlying data.

The Benefit: Better-Informed Decisions

One of the greatest benefits of identifying data limitations is the ability to make decisions with a clearer understanding of what the data can, and cannot, tell you.

For example, an employer may discover that certain applicant disposition codes are too broad to determine why candidates were no longer considered for a position. Rather than assuming what those dispositions mean, the employer can evaluate the impact of the issue and determine the most appropriate approach for the analysis as well as identify possible changes that they can make going forward so they get the information that will be more helpful in the future. This does not necessarily mean the analysis cannot be completed. It means the organization has additional context when interpreting the results.

The Downside: You May Not Always Be Able to Fix It

Identifying a data gap does not always mean there is an easy solution.

Historical data may not exist, systems may not have captured certain information, or data collection practices may have changed over time. Attempting to “fix” a gap by making assumptions on information that cannot be reliably verified may create a different problem. Doing so could introduce inaccurate information into the analysis, potentially changing the results and making it more difficult to distinguish between what is real versus assumption. In these situations, the better approach may be to document the limitation, understand its potential impact, and consider it when evaluating the results.

There can also be a practical cost to addressing data issues. Correcting records, reviewing historical information, updating systems, or changing collection processes can require significant time and resources. Organizations should consider whether the effort will meaningfully improve the reliability of the analysis.

The Bigger Risk May Be Not Knowing

A known data limitation can be evaluated. An unknown limitation can quietly influence an analysis without anyone realizing it. When organizations understand where their data may fall short, they can ask better questions about their results. Is the issue significant enough to affect the analysis? Does it affect only a small portion of the data, or does it have a broader impact? Can the information be corrected reliably? Should the limitation be considered when interpreting the results?

These questions allow organizations to move from simply identifying a data problem to understanding its significance.

Want to Learn More?

Understanding where potential data shortfalls exist and how they may impact your analysis is an important part of the data analysis process. If you have questions about your data or want to discuss potential limitations in more detail, consider reaching out to your current Berkshire consultant or a member of the Berkshire Associates team.