This guide will help familiarize you with the causes of import errors and the basic techniques to trace each each error back to its source.
What causes bad data?
Bad data is usually caused by a formatting character being inserted in a non-standard location. Formatting characters include single quotes, double quotes, commas, tabs, and line break characters. You are notified of bad data when you receive an Import Error Email.
Your import error email will contain error files that look something like this:
What is needed to fix bad data?
Fixing bad data is a tedious task, and usually involves updating entries one by one. You will need edit access to your brand's e-commerce database.
To identify and resolve these issues we need to use the proper tools. Here is what you will need:
- A spreadsheet editor like Excel or Google Sheets
- A text editor like Sublime Text or Atom
- Your import files (see below)
- Your import error files
Custom integrations can find their import history through their FTP archives folder. (see: Setting Up FTP)
Shopify and Magento Integrations may need to email Tech Support to request copies of their files. Make sure to include the import ID and specific files requested.
How do I find the errors?
Each error notification consists of two parts: the entry location (row) of the error and a description of the error. Error reports help you identify the incorrect data, but you will still need to diagnose the cause.
Open your errors file and its partner file in your text editor program. Start with the errors file. In the following example, we can see that the errors appear in lines 3, 5, and 6.
Here is how a file might look in your spreadsheet editor. It's difficult to spot all the formatting errors:
This is why it's important to view your files in a proper text editor.
When viewed in a text editor, you can see that Andre Agassi's record will not import correctly, as it's split across two lines:
This is a very common error that will appear in your error report as a pair of "could not be parsed" errors:
The above example used quote-wrapped text, but not every file is formatted the same way. Here is the same example file as a plain .csv:
Without quote-wrapped text, every comma represents a new column. Here is how our importer reads the original file:
These are the errors produced by that file:
Using the entry numbers and error descriptions we can trace back the sources of these errors. In row 3, we can see that "michael" is an invalid email. So we know that something happened to break apart Michael Jordan's email address. Looking at the plain text version we can see that a typo put a comma in his email address.
How do I correct it?
Now that you've found the cause of your bad data, it's time to correct it! This usually means going into your store's customer profiles and editing the data to an acceptable format.
Sometimes diagnosing an error may require the assistance of multiple programs, or it may be difficult to identify the cause of the error in that row. The above examples are not all-encompassing and you may need to be creative or experiment. For further assistance please open a request with the tech support team.