How to handle missing values in a dataset
Missing values are rarely just empty cells. They show up as N/A, null, a dash, TBD or a typed "not known", and each system spells them differently. Before deciding what to do about gaps, you need to be able to see them all.
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Finding every kind of missing value
Omixa treats truly empty cells as missing and recognises common placeholders such as N/A, NA, null, None, a dash, TBD and "not available" as missing too. The recognition is a visible rule, and each conversion is written to the change log.
Missing values count against quality. A column that matters to your analysis and is largely blank will pull the data-quality score down, and the findings show where the gaps are.
Deciding what to do about them
There is no single right answer. Leaving a gap empty is honest. Filling it is a modelling choice that changes your results.
Omixa keeps that choice with you. Filling gaps with estimates (the median for numbers, "Unknown" for text) is off by default and only runs if you switch it on, because those values are estimates, not data you collected.
Guidance, not guesses
Before filling anything, ask whether the gaps are random or follow a pattern, whether a blank means "none" or "not recorded", and whether the column is needed at all. The change log and report let you document whichever approach you choose.
Steps
- Upload your file and read the missing-value findings.
- Keep "Recognize blanks" on so placeholders are counted as missing.
- Decide whether to leave gaps or turn on "Fill gaps with estimates".
- Download the cleaned file and keep the change log.
Common questions
Does Omixa fill in missing values automatically?
No. Filling gaps with estimates is off by default and only happens if you choose it.
Which placeholders count as missing?
Empty cells and common placeholders such as N/A, null, a dash, TBD and similar worded markers.
Will placeholder conversion be recorded?
Yes. Every conversion appears in the audit log, so you can see exactly what was treated as missing.
Related guides
- Data quality assessmentGet a data-quality score and report for a spreadsheet: what was found, what was fixed, and what still needs review. Practical, explainable, no guesswork.
- Data validationCheck CSV and Excel data for values that do not fit their column, like words in phone numbers, impossible ages and future dates. Pro adds Quality Profiles.
- Prepare data for researchGet survey and research data ready for analysis with a clean copy, a change log and flagged items for your review. Built for analysts, students and NGOs.