Fast customer onboarding is often a challenge for growing software companies. Development, product, and customer onboarding teams spend considerable time manually managing customer data. This leads to a higher risk of error and compromised data quality, letting customers wait longer to experience the value of your product.
Luckily, nuvo was built to handle that complexity and take the load off your teams. With nuvo’s flexible dataHandler, you can easily import any data file in the format that your system requires. This guide walks you through how our advanced solution can help you automate and scale your data onboarding process.
With dataHandler, your internal teams or customers don't have to prepare data beforehand or manually transform it.
Here are some examples of complex import cases that dataHandler solves, though it's important to note that it's not limited to these examples:
With nuvo, you can upload data in any shape and format, and end-users work with the data directly using nuvo’s Data Importer.
During the data upload process, among other things, end-users need to decide:
Let’s take a closer look at the data import procedure:
As you can see above, dataHandler can be started after the initial data is uploaded, and either
Let’s assume your system expects the following headers: ID, full name, and street name.
However, your customer delivers you a file where the first column contains the headers:
Before your users move onto header selection, nuvo’s dataHandler can transpose the file directly after upload to look exactly as your system requires. dataHandler is a function that is executed after the file upload which your tech team can define. This means you can write the logic for transposing the data.
You no longer have to worry about the data structure of the sheet, and the end-user can easily map and review the contents of their files, without any additional manual work.
With nuvo, you can merge two sheets based on a unique column.
Once your end-users upload the file containing multiple sheets, they can decide which ones they want to continue with the import.
Here, you can see two sheets with the same column “id”. Based on this column, you can merge the data found in these two sheets into one.
Here’s the first sheet:
It contains the columns Company and id.
Here’s the second sheet:
It also contains two columns, id, and name.
In this case, the dataHandler function is triggered after the header selection, enabling the efficient merging of data from multiple sheets based on the specified identifier.
Let’s assume your customers put multiple values in a single cell, whereas you need a single value per cell.
In this case, the dataHandler is executed in between Match Columns and Review Entries steps.
In the Select Header step, we’ll ensure your customer selects the right row that contains your headers. Before the import, in the Review Entries step, you’ll notice that we’ve decoupled the information contained in the second row.
This way, your customers can start the export from their system and import it into yours without any additional manual work from both parties.
The same option works the other way around. If you have a file with de-nested data that you want to nest in your database, dataHandler is the feature you’ve been looking for.
In some use cases, you may need additional information about the file you’re uploading, such as its name, size, and type.
nuvo enables you to configure the dataHandler to take this metadata and add it alongside the file's contents to your database.
Let’s assume you’re importing sheets that contain values you want to sum up, e.g., item price.
The first row in the sheet above also represents the header name. In the next step, your customers will map it to the corresponding header from your target data model.
During the implementation of nuvo, your dev team can insert additional logic that will affect every header and automatically summarize the column data.
With dataHandler, the sum of column values is calculated and presented in the Review Entries step, as a newly inserted row.
nuvo’s dataHandler has multiple modifier functions. The reviewStep function, for example, can be used to accommodate various address import scenarios.
Let’s use an example where you have a file where each address is separated into multiple columns.
In the review step, you can see that the rows containing address information are merged.
By leveraging the reviewStep function, logic can be implemented to handle such cases where the end-user uploads a single address column or multiple address columns, including street, postal code, and city. This approach ensures flexibility and seamless integration for importing diverse address data structures.
With dataHandler, you can create custom warnings for your users, so there is no ambiguity while reviewing the import data.
Let’s take this sheet as an example:
In the Review step, you can have errors, warnings, and information.
nuvo's dataHandler changes the way businesses handle complex customer data imports. Once you automate this process, your teams can say goodbye to data onboarding bottlenecks—saving time and ensuring top-notch data quality for smooth product performance.
No matter your import requirements or the edge cases your company deals with, nuvo has you covered. Our team is here to support you every step of the way. Book a call today and discover how nuvo can supercharge your growth.
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