Onboarding customer data involves considerable effort, and many companies continue to handle it manually. This is where nuvo’s AI-powered Importer SDK makes a difference. It can help your customer success or onboarding teams transform and import unpredictable data more quickly and efficiently with its Cleaning Functions.
This robust capability checks for duplicates, corrects data formats, applies business logic, and more. With nuvo’s Cleaning Functions, you can easily clean imported data in any way you need—allowing your teams and customers to save time and resources. Here’s how.
nuvo’s Data Importer SDK comes with various cleaning and validation rules you can set and adapt to your data import use case.
Once implemented, nuvo can scale with your growing needs, giving you more time to focus on your core product.
The process consists of the following steps:
Upload Files: Users can upload one or more files by either dragging them into the designated area or by clicking the "Select file" button.
Choose Sheets: If the uploaded file has multiple sheets or if multiple files are uploaded, users can choose which sheet(s) they want to import.
Confirm Headers: Users verify the row that contains the column names for each imported sheet.
Combine Sheets (Optional): This step appears if multiple files or sheets are uploaded. Here, users can combine all selected sheets and files into a single dataset.
Match Columns: The system matches the imported columns to the columns of the schema your system requires.
Review and Clean Data: In this final step, users review the reorganized data, correct any errors, and then submit their cleaned data.
This graph shows when different Cleaning Functions can be executed:
As you can see above, Cleaning Functions can be started after the initial data is uploaded, and either:
Here are some common data import use cases that nuvo’s Cleaning Functions can streamline:
When you pull information from different sources, the formatting of values can differ from one system to another. The easiest way to showcase this is the US vs. European number formats. For example, for one million, we would have:
US: 1,000,000.00
EU: 1.000.000,00
When you work with sheets and databases, you need the same standard—otherwise, you’re compromising data quality, and this is where nuvo’s cleaning functions come into play.
Here’s an example of data your user wants to import:
As we can see, the numbers are formatted differently. Once your user reaches the “Review Entries” step, you’ll see that nuvo’s Cleaning Functions applied the rules and reformatted the data so that it matches your exact needs.
You can easily prevent duplicates in your database by running background checks with nuvo. Let’s see how you can check whether a user is uploading data that already exists in your database.
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In the “Review Entries” step, your user will see duplicate entries marked as errors in the file they’ve been trying to upload.
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If you’re clear on the data format you expect, you can help your customers avoid importing faulty data.
For example, country-specific VAT codes differ from country to country. To prevent importing data that doesn’t follow the expected structure, you can code the correct formats directly in nuvo.
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In the example above, you can see two different VAT code formats for the same country— Germany in this case. The first one follows the expected format, and the second one doesn’t.
In the “Review Entries” step, you can mark fields with the wrong data and throw a custom error message explaining the issue. This way you prevent your users from importing faulty data.
In this example, we’ll showcase adding country codes to the corresponding column depending on the value in the country column. This way, you ensure data quality and reduce the workload for your users.
As you can see in the example above, the user uploaded a single column with the names of countries.
In the “Review Entries” step, you can see that nuvo added an extra column to their data, which contains country codes for each country.
The best part about this workflow is that it can be applied to other examples where the values in one column can be used to create data needed in another column.
Sometimes, you need to validate data being imported through your backend. After the “Match Columns” step, with nuvo you can run validations against your database and inform your end users whether the data being imported contains errors.
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This way, users can see the errors with rows that contain descriptive warnings and how to fix them.
With nuvo’s Cleaning Functions, you can perform any other cleanings that need to be executed per row or per cell. To execute cleanings that involve the whole data set, use our advanced dataHandler to execute actions such as nesting, splitting, filtering, transforming, or restructuring the data, and more.
nuvo’s Data Importer SDK can transform the data onboarding experience for both your team and customers. By utilizing Cleaning Functions, your developers save time on adding new data import use cases—and if any edge case arises, nuvo is there to help you handle it effortlessly. Thanks to nuvo’s fast data onboarding process, you can easily scale data imports to serve more customers for greater business growth.
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