Requires an active data import, parsing sequence, or validation routine where source data is being evaluated against numeric database fields.

The user must have access to the raw source data (e.g., CSV, API payload, JSON file) generating the error to review the formatting.

Must be using a supported JSON Schema version (if applicable), as very old historical versions drop support for certain validations.

Inspect for Non-Numeric Characters: Open your source data and review the specific fields mapped to numeric columns. Remove any non-numeric characters present in these fields, including commas, currency symbols, and trailing spaces.

Verify Locale Decimal Notation: Check for locale decimal mismatch conflicts. Ensure the decimal separator used in your data (e.g., a comma , vs. a dot .) matches the expected regional format of the destination system.

Check for Value Overflow: Evaluate the length of the numerical values in the source data. Ensure that the values do not extend beyond the defined column decimal precision limit in the destination schema (e.g., trying to import 5 decimal places into a field strictly configured for 2).

Identify and Correct Empty Strings: Look for empty or blank strings within the source data that are being directly cast into a numeric data type. Replace these blank strings with a standard default value (such as 0) or map them to explicitly register as NULL.

Contact Support: If the issue persists after reviewing the data formatting and schema, please contact support. Provide the following information to ensure efficient troubleshooting:

Data Parsing / Validation: The process by which the system reads incoming data strings and verifies they conform to the required data types before committing them to the database.

Locale Decimal Mismatch: A formatting error that occurs when a number's decimal structure (using commas vs. dots) contradicts the system's expected regional settings.

Value Overflow: A condition that triggers an error when a numerical value contains more characters or decimal places than the database column is configured to accept.

JSON Schema: A vocabulary used to annotate and validate JSON documents. The system relies on a default JSON Schema unless explicitly referenced otherwise.

Standardize Source Data First: Whenever possible, use formatting tools or scripts to clean your source data before initiating a transfer. Stripping out currency signs and commas proactively will prevent the majority of parsing failures.

Manage Blank Fields Intentionally: Avoid relying on the system to interpret blank cells. Standardize your export processes to output a strict 0 or NULL instead of an empty text string when numerical data is missing.

Stay Updated on Schema Versions: If your integration explicitly references a specific JSON Schema version, audit it regularly. Extremely old versions lose support over time, which can trigger unexpected validation errors on previously functional workflows.

Test with Small Batches: When rolling out a new data integration or adjusting field types, run a test import with a small, highly varied sample of data to immediately highlight precision limits or locale mismatches.