Guide
How to find missing fields and duplicate employee IDs in HR data
A practical guide to checking missing values, duplicate IDs, and conflicting records before HR reporting starts.
Short answer
Start by identifying which fields affect the output, such as employee ID, hire date, termination date, status, employment type, department, location, manager, and other grouping fields. Then check for duplicates, missing values, mixed values, and repeated rows before any report pack or workbook is generated.
Why this matters
- Duplicate IDs can distort headcount, hires, terminations, and lists.
- Missing values often do not fail loudly; they quietly weaken logic and groupings.
- A good report usually begins as a good review process, not a perfect source file.
What HR should check
- Fields that affect counting, grouping, or follow-up actions.
- Whether duplicates are true errors, historical rows, or valid multi-row records.
- Missing manager, location, employment type, or date fields that limit follow-up outputs.
- Rows with conflicting values across the same employee identifier.
Review checklist at a glance
| Data issue | Why it matters | What to review first |
|---|---|---|
| Duplicate employee IDs | They can distort headcount, hires, terminations, and filtered lists. | Decide whether the duplicate is an error, a valid multi-row case, or historical detail. |
| Missing key fields | Blanks in dates, manager, location, or employment type weaken logic and follow-up lists. | Prioritize fields that affect counts, splits, or downstream operational actions. |
| Mixed values across rows | Conflicting values on one identifier can make grouping and explanation unreliable. | Check whether one person carries conflicting department, status, or date values. |
| Repeated-row noise | Rows can look complete individually while still creating duplicate counting risk. | Review repeated identifiers before any chart, workbook, or audit summary is finalized. |
Common mistakes
- Looking only for blank cells and missing the repeated-row problem.
- Trying to clean everything after the PPT is already drafted.
- Ignoring the impact of low-quality fields on split charts or daily lists.
How KYBN helps
- KYBN flags repeated rows, missing values, and mixed-value issues during the review step.
- Daily and Audit outputs make it easier to separate operational follow-up from pure reporting cleanup.
- Issue exports can be retained as support files for cleanup or later explanation.
Quick questions
Are blank cells the main data-quality risk?
Not always. Repeated rows and conflicting values can be just as damaging because they often survive into charts and counts without obvious visual warnings.
Should cleanup happen after the report is drafted?
No. Cleanup should happen before outputs are generated, otherwise HR ends up reworking charts, lists, and explanations after the number has already circulated.
Related resources
Try the workflow
If this is the kind of HR reporting problem your team is dealing with, start with a sample workspace or review sample outputs before using a real employee file.