Valid JSON can contain a negative quantity, a missing identifier or a date your application does not accept. Successful parsing answers whether the text follows JSON syntax. It does not decide whether the values describe a usable record.
This distinction saves time when an import rejects a file that a formatter accepts. The two systems may be checking different requirements.
Separate the checks into layers
The JSON specification defines a data-interchange syntax. A text such as {"quantity":-5} can follow that syntax. Whether negative quantities are permitted depends on the system receiving it.
JSON Formatter can help inspect supported JSON and locate syntax problems. Indentation can make structure easier to read. It does not establish that a customer exists, a product code is current or a quantity follows your import contract.
A schema adds another set of requirements, such as field types and required properties. Application rules can add further conditions that depend on context or other records. Identify which layer produced the error before changing the file.
Build examples that exercise the actual rule
Suppose a sample stock-import contract requires sku as a nonempty string and quantity as a nonnegative integer. Prepare a small sample with an ordinary record, a missing SKU and a negative quantity. The receiving system should handle each according to its documented rules.
Do not infer those rules from one successful record. A single example cannot show every permitted field, boundary value or relationship. Obtain the current contract from the system owner or its official documentation.
If an error names a field, inspect that field's value and type. The string "5" and the number 5 can have different meanings to a validator. Reformatting the JSON will not resolve a type mismatch merely by changing indentation.
Use the appropriate tool for each input
Data Quality Rule Checker checks supported CSV data against declared rules. It can help with a tabular quality-review stage, but it is not a JSON schema validator. Converting nested JSON into a flat table can also change what you are able to inspect, so do not flatten away meaningful structure just to use a convenient tool.
Keep the failed source and the exact error message while investigating. Make one targeted correction, then validate again through the authorized import preview or test workflow. Changing several unrelated fields can hide which requirement was actually wrong.
The useful outcome is a record that satisfies the receiving system's contract, not just a file that looks tidy. Syntax checks are valuable because they isolate one class of problem. Treat them as the beginning of the review, and keep the remaining requirements explicit.