1095-C Data Quality Controls to Look for in Software

These classes organize work; they are not official IRS error codes. A product may use different labels. Evaluate whether its explanation directs the reviewer to the right facts and whether a record can have more than one issue without becoming several unrelated cases.

Use approved synthetic records in a nonproduction workspace. Establish a clean baseline first, then introduce one documented defect at a time. Keep an answer key showing the affected source row, expected signal and proper reviewing role. Do not seed fictional full SSNs that could be mistaken for real people.

For the sample, start with six fictional employee identities. Duplicate one employee's source row, remove a required source value from another, and introduce a two-month conflict for a third. The file now has seven rows representing six intended identities. Record these defects before import so the evaluator knows what the software should help discover.

Some fields are conditionally required. Use the 2025 instructions to establish the applicable case before calling a blank erroneous. A useful product should let the reviewer distinguish missing data from a field that properly remains blank.

Ask the vendor to show the equivalent of this conceptual queue. The aliases and labels are invented for the demonstration; no row represents an IRS message.

Open each case and inspect its source link, current value, proposed change and history. A broadly shared queue can use internal aliases, while authorized reviewers retrieve sensitive detail through the controlled record view. Check whether filtering by employer or owner preserves the case's actual month scope.

Fictional worked example: the sample produces one duplicate-row defect, one missing-value defect and two conflicting month cells. That is four defects organized into three employee cases. The duplicate case begins with seven imported rows but only six intended employee identities.

The reviewer confirms that E-02's second row repeats the same source facts, then uses the supported process to resolve the duplicate while preserving the decision. The missing value for E-04 is obtained from its source owner. For E-06, reviewed effective-date evidence resolves July and August together.

The final population is six employee identities, with the four seeded defects resolved. A system reporting four issues and another reporting three cases may both describe this sample accurately. The evaluator must inspect the counting unit and actual records instead of assuming the larger number means better detection.

Ask the operator to mark an issue resolved without changing the source or attaching a decision. Observe what the product permits and what remains visible. Some cases may be resolved by a documented explanation, but a dismissed warning should not erase the fact that no source change occurred.

Reimport the unchanged flawed source in a controlled test. Does the issue recur, remain linked to the earlier decision, or silently reintroduce the defect? Then supply the corrected source and inspect whether the case connects to the new version. These tests reveal how the tool behaves beyond a single cleanup session.

Include a legitimate blank or distinct employment episode to test false positives. A reviewer should be able to explain why a signal does not require the suggested change. Automatic deletion or forced values can damage correct records when the software has only partial context.

Record whether each seeded defect was detected, whether the explanation was accurate, how the reviewer resolved it and whether the final output matches the reviewed source. Keep undetected defects and misleading messages as separate findings. Time spent investigating a false signal is useful operational evidence, without assuming a universal acceptable duration.

Distinguish source quality checks from format validation and agency processing. Publication 5165 describes AIR schema, business-rule and acknowledgment handling. A locally accepted import or a passed structural check does not establish the truth of an offer date or enrollment fact.

Export the completed case history and repeat the sample after a material mapping change. Use the employee scenario set to check that the product handles valid changes as well as deliberately bad data.