Find missing values before they break your metrics
CSV tool
Missing data is rarely just “blank.” It shows up as NA, N/A, -, 0, or whitespace that looks empty until you sort or filter. Your first job is to make absence explicit.
Why this helps
Separate null from sentinel
A column may use 0 or -1 as “unknown.” Without inspection, you will happily average those into real quantities.
Catch whitespace masquerading as empty
Some exports pad cells. They look blank in a preview but are not equal to a true empty value in code.
Protect joins and keys
Missing keys silently multiply rows or drop matches. Find weak keys early.
How it works
- Profile columns for empties and suspicious literals in CSV Unwrap.
- Cross-check with row samples where metrics look wrong.
- Normalize representation once you know the rules.
Why exports lie quietly
Spreadsheet tools sometimes coerce types. Logs sometimes emit empty strings where SQL had NULL. The CSV is a snapshot of those decisions.
A practical definition of missing
For each column, decide what should count as unknown, then verify how often that appears. The definition belongs in your pipeline documentation — not implied by the file.
Missing values are a schema issue
If you do not agree on representation, every downstream chart inherits silent bias. Inspection is how you align the team on what empty means.
FAQ
Are empty strings the same as null?
Not in code, and not in meaning. Decide per column whether empty string is valid data or should be treated as unknown.
How do I prioritize columns?
Start with keys, dates, and measures that feed KPIs. If those columns have messy null behavior, fix them before cosmetic columns.
Can CSV Unwrap find every missing value automatically?
It helps you see patterns quickly, but domain rules still come from you. The tool accelerates discovery; it does not replace judgment.