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How nuvoAI Simplifies Target Data Model Generation

Crafting a TDM is one of the most laborious parts of setting up a data importer. From now on, you can prompt nuvoAI and save hours of work.

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No matter where you look right now, people are talking about AI. The arrival of ChatGPT in November 2022 challenged us all to think about how rapidly the world is changing. Forward-leaps in progress have always ruffled feathers and many are daunted by the capacity of generative AI. However, being slow to adapt may see many people and organizations get left behind. 

At nuvo, AI has always been part of our DNA. From day one, AI considerations have shaped how we build our business and the solutions we create. As soon as ChatGPT landed, we got excited about how we might be able to leverage generative AI to make our products better, faster, more powerful, and easier to use. 

Since our very early days, AI-assisted column mapping has been one of our data importer’s most successful features. Today, we’re excited to announce that we’re taking our AI journey one step further. Our customers can now use our latest feature, nuvoAI, to generate target data models (TDM) which will revolutionize how customers set up and use our data importer. 

But before we jump to the newest feature, let’s have a look at how we use AI to achieve superior column mapping capabilities. 

AI-assisted Column Mapping

AI plays a significant role in the Match Columns step to ensure a quick and seamless experience during the import process. Our AI-assisted column mapping feature uses a machine-learning algorithm to automatically map imported data to a target data schema. Because it learns and improves with every import and every manual adjustment a user makes, this feature grows more accurate over time. With this feature we can offer our customers their own individual mapping model.

In fact, we consistently receive feedback on the high accuracy of our AI-assisted data mapping. It’s been a game-changer for companies that work with large multi-column datasets or those that receive diverse data structures from customers or suppliers. Let’s explore how we’re taking our use of AI even further. 

Introducing nuvoAI for TDM generation

A target data model is a blueprint that acts as a guideline for how an organization will use, store, and manage their data. It outlines how data should be designed or structured within a system or database to ensure data is organized, consistent, and accessible. 

TDMs are used to define the required columns, the format each column requires, and to manage data validation while taking cross-column dependencies into account. Up till now, we offered two ways to generate a TDM with our no-code TDM generator in addition to writing your own code. A user can: 

  1. Click the "Generate from File" button to upload an existing (CSV, Excel, TSV, XML, or JSON) file that outlines the desired columns and structure. 
  2. Create columns one at a time using our no-code TDM generator by clicking “Add column” to start and follow the steps.
  3. If you prefer to write your own code, go for it. Our documentation helps you with it.

Now, we’ve added a fourth possibility. From now on, our users can also generate their TDM by feeding prompts to nuvoAI. This makes it even easier to add, edit, or delete columns and manage validation rules.

We worked hard to make your work easier

Today’s release is the result of plenty of research, brainstorming, and iterations. We knew we could use generative AI to make huge improvements to our product so we’re thrilled to finally be able to share this next step. We’ve designed nuvoAI’s text-to-tdm model so that it can generate even extremely complex target data models that include diverse column types and validations from simple text instructions.  

Crafting a TDM is one of the most laborious parts of setting up a data importer. By automating TDM generation, our users will save hours of work. From now on, you can prompt nuvoAI with simple sentences like: “Generate a target data model for contact data including the following columns: Contact ID, first name, last name, email, company'', or you can add validations to an existing TDM, such as: “Add the following validations: All of the columns are required and contact ID must be unique”.

By prompting nuvoAI, you can easily add, edit, or delete columns and manage validation rules

At nuvo, we know our customers so we know that every target schema is different. Most include high numbers of columns, diverse column types, and several forms of validations. To save you effort and time, we put a lot into developing nuvoAI to be able to generate the most complex TDMs. You can also edit, add, and delete columns from existing schemas. 

Releasing nuvoAI for target data model generation is a huge step forward for us. We’re deeply proud of this achievement and can’t wait to see what you think. And we won’t stop there. This is just the first step of many and our team is already working on expanding nuvoAI’s capabilities to automate even more tasks. We’re dedicated to making it easier for you to set up, implement, and import data in a stress-free, intuitive way. Are you ready to give it a try?

Log in to nuvo’s user platform to begin exploring. If you don’t have access yet, have a short call with the nuvo team.

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