Data Type Detector

What the data type detector does

Upload a CSV or spreadsheet export and the tool estimates the most likely type for each column: integer, decimal, date, boolean, string, or empty. That gives you a fast schema check before importing the file into a warehouse, API pipeline, BI tool, or modelling workflow.

Why type detection matters

Many data issues are really schema issues. Numbers stored as text sort incorrectly. Dates stored as strings break timeline analysis. Boolean flags written as yes, no, true, false, and 1, 0 in the same field often cause silent failures. Detecting the likely type early helps you spot columns that need normalization before they reach a downstream system.

How to interpret the results

• Integer: whole numbers with no decimal places.
• Float: decimal numeric values.
• Date: values that consistently match common date patterns.
• Boolean: flags such as true/false, yes/no, or 1/0.
• String: free text or mixed values that do not fit a cleaner type safely.

Limits of automatic detection

Type detection is a strong first-pass heuristic, not a contract. Locale-specific dates, mixed identifiers, padded codes, and Excel serial dates may need manual review. If a field contains a combination of numbers and text, the safest fallback is string. Use the detector to identify suspicious columns quickly, then clean them with more intent.

Related tools and guides

• Missing Values Checker to review completeness alongside schema quality.
• CSV Column Extractor to isolate the columns you want to normalize first.
• Data Governance Tool to document the schema after review.
• Data Governance Guide for broader schema and ownership practices.