How to investigate missing values before AI data analysis

Investigating missing values before analyzing data with AI means discovering why information is absent and how that affects your question. Filling everything with zero may make a table look complete while making the analysis less correct.

Our exercise uses fictional support durations. We will distinguish missing information, zero and values needing confirmation before requesting averages or comparisons.

First recognize the different situations

A field may be empty because information was not collected, does not apply or was lost during export. These explanations are not equivalent and may require different treatment.

It is also unsafe to assume that “NA” always means missing information. It could be a legitimate organizational code, so consult the data dictionary before standardizing values.

The pandas missing-data documentation shows that missing-value representations vary with data types. This is a reminder that appearance on screen does not determine a field’s meaning by itself.

Make a small inventory

Count records and identify how many relevant values are absent in each column. Then inspect whether missing information is concentrated in a particular period, category or collection method.

Fictional record	Duration	Initial interpretation
A	10	Recorded minutes
B	empty	Reason still unknown
C	0	Recorded zero; check validity
D	20	Recorded minutes

If B is excluded from the average and zero is valid, the three known values produce 10 minutes. If B becomes zero through assumption, the result changes to 7.5 minutes solely because of the chosen treatment.

These calculations are illustrative. They show why the decision must appear in the report rather than remain hidden in file preparation.

Request investigation rather than automatic filling

Use AI to organize questions and check patterns observable in the sample. Do not ask it to “complete what is missing” without defining which inferences would be acceptable.

Review this fictional sample before analysis.
Identify missing fields, zeros and ambiguous codes without replacing them.
Explain which counts can be produced and which conclusions need confirmation.
Suggest questions about collection, export and applicability.
Do not estimate individual values or invent reasons for absence.

The response must separate observation from hypothesis. “Category X has more empty fields” may be a count; “team X forgets to complete forms” requires additional evidence.

Choose treatment according to the purpose

For some questions, analyzing complete records and stating that limitation makes sense. For others, excluding rows may remove precisely the group needed to understand the process.

Replacing a value with an estimate is also a methodological decision. Do not use a mean, median or another replacement merely because a tool offers the option.

Record the reason, affected fields and how results will be compared before and after treatment. The procedure must allow you to reconstruct the original dataset.

Compare more than one scenario

When reasonable uncertainty remains, produce an analysis using known values and another using an explicitly defined treatment. Compare the change in conclusions without presenting estimated values as observations.

Check denominators as well: a rate over all records differs from a rate over completed records alone. State which collection enters each indicator.

Keep the observation count visible next to the result. Two averages that look similar can describe substantially different subsets if one excludes many more records.

Know when preparation is finished

Investigation is ready when you can explain how many values are absent, what is known about the reasons and which policy will be used. Unexplained absence should remain visible as a limitation.

Common errors include silently deleting rows, converting empty text to zero and mixing estimates with measurements. Keep an input copy and a record of every transformation.

Then use the calculation checking guide to review averages and tables produced from the prepared dataset.

Frequently asked questions

Can a missing value be replaced with zero?

Only under a justified column policy. Missing information and zero can represent different situations.

What should I check before excluding incomplete records?

Check how many records are removed, which groups contain missing values and how exclusion affects the question.

How should analysis using estimates be presented?

Separate estimates from observations and record the method, affected fields and comparison limits.