Developers

Data Editing with Flatfile: 3 Use Cases

Roby Hyde

Posted 8/6/2021

In addition to onboarding data with Flatfile, did you know you can include data validations within your Flatfile workflow to quickly and easily edit existing data? 

Below is a video that walks through a Flatfile workflow and the validation sequencing necessary to edit data with three helpful use cases. The flexibility of Flatfile is such that it can be moderated to become a data editor in these three unique situations! 

For more insight, check out the developers page for our documentation on how Flatfile’s platform can be used to solve your specific use case or simply reach out to us directly and we can talk through your needs.

Use Case 1 - Cleaning up user data that you already have on your server

In this use case a user sends over a file via an SFTP or FTP server (or some other method) and instead of cleaning the data internally, you can rely on Flatfile. With Flatfile, the data is pre-populated and ready for review. The file is converted into a CSV and then Flatfile’s matching step does its job, walking the user through the data clean up process.

Use Case 2 - Databasebase clean up with your QA team 

There may be certain scenarios where you know your data in-depth and don’t necessarily need to go through certain matching and validation steps. In this use case, data can go directly into the review step within Flatfile’s workflow. For this situation, there is data in your backend database that needs to be reviewed and cleaned by an internal data QA team. You can put a button in front of this team that launches Flatfile, preloading data from an API call to your backend database. Once the QA team goes through and edits the data directly, another API call is used to update the records directly in the database.

Use Case 3 - Creating a multi-step importer to remove non-US records  

A familiar use case for companies that are importing data across multiple countries, we see this use case quite a bit. If your importer is assuming all records are US records, like addresses for example, a user importing international data will break the validation logic in your importer. By creating a multi-step importer, you can edit the uploaded file, removing all non-US records before it hits your validation logic. In the Flatfile workflow, after a user uploads data, just filter out records you don’t want, like international addresses. From here you can chain together an importer.

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