You exported a dataset from your CRM and imported it into your analytics tool. The "revenue" column — which contained numbers like "45000" and "12500" — was imported as text. Every calculation on that column failed silently, returning null instead of a number. That's a data type inference failure.
Data type inference is the process of automatically determining what kind of data a column contains — text, integers, decimals, dates, booleans, or email addresses — based on the values in it. It matters because most data processing tools, imports, and analyses behave very differently depending on whether a column is treated as a number or a string.
Why Type Detection Fails
Types are inferred, not declared, in most file formats. A CSV file contains only text — there's no metadata that says "this column is a date." When a tool reads the file, it has to guess the type from the values.
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Inference fails when:
Mixed types exist in a column — If 950 rows have numeric values and 50 have text like "N/A" or "-", the column may be inferred as text rather than numeric.
Date formats are ambiguous — "01/02/2024" could be January 2nd or February 1st depending on locale. Many tools default to one convention without warning.
Numeric values have formatting characters — "$45,000" is text until the dollar sign and comma are removed. The same applies to percentages ("45%") and formatted numbers.
Leading zeros are present — A zip code column containing "01234" might be inferred as integer and imported as "1234" — dropping the leading zero and making the value incorrect.
What Good Type Inference Catches
A proper type inference scan will flag:
- Columns with mixed types (mostly numeric but with some text values)
- Dates in ambiguous or inconsistent formats
- Numeric values with formatting characters preventing numeric operations
- Boolean-like columns with unexpected values beyond true/false
- Potential email, phone, or zip code columns that require specific validation
Sohovi lets you set up validation rules for any column and instantly see which rows fall outside them — no code or SQL required.
Why This Matters for Imports
Every time you import data into a new system — CRM, analytics tool, database — the receiving system infers types. If the inference is wrong, calculations break, sorts fail, and filters return incorrect results. Type issues are one of the most common causes of silent import failures.
Sohovi runs type inference on every column when you profile a CSV — flagging mixed types, ambiguous dates, and formatting issues that would cause problems in downstream systems. Catching type problems before an import prevents hours of debugging after.
