Announcing AI Transform (plus everything you need to know about data mapping)
It has been a very productive couple of weeks at Flatfile! We recently rolled out the new dashboard for Flatfile developers, and now we’ve just released AI Transform, a powerful new data transformation copilot that makes it so much easier and faster for Flatfile users to transform large data sets – you’re going to be amazed when you find out what it can do.?
If you want to learn more about its capabilities (and discover everything you need to know about data mapping), read on!
AI Transform: Your data transformation copilot
In a nutshell, AI Transform is the fast and safe way to make bulk edits to your data at scale.
You might know what the end state of your data needs to be, but the path to getting there is often unclear. What is clear, however, is that editing large data files is cumbersome and slow. The tools for the job were built for granular editing and require specialized knowledge, deep experience and considerable time.
That’s why we built AI Transform.
AI Transform lets teams use natural language to describe how they’d like their data to change. Flatfile takes this prompt and generates and executes the code necessary to make the desired changes instantly across an entire data set.
Here are a few of the capabilities your will get:
Combine data: You can now use natural language to combine data from two or more columns to create a new value to populate another column. For example, use first name, last name, and domain name to create an email address.
Calculate a final value: You can easily generate values by calculating them. For example, imagine multiplying the property value by the property tax rate to determine the amount of property taxes owed on each property.
Extract parts of your data: Sometimes, the values you need are embedded in a longer string. With AI Transform, you can simply describe the portion you want and what you want to do with it. For example, imagine having a full name field you want to divide into first, middle, and last names.?
You can do the above and so much more, like move data from one column to another, change data formatting, delete values, etc. You can even string multiple commands together into a single prompt.
If you can describe it, you can likely do it.?
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Whether you're a seasoned professional or just beginning your data file import journey, AI Transform offers almost limitless possibilities for working with your data. However, the first step towards mastering the data file exchange process is understanding the key terms and concepts. If you're new to data imports (or just want a refresher), this in-depth article will tell you everything you should know about data mapping.
The ultimate introduction to data mapping
Data mapping is an essential data management process that will help you integrate, transform and use your data effectively.?
Imagine you're in charge of a company's data strategy. You've got data coming at you from all corners: Sales figures, customer profiles, inventory updates, you name it. Your job is to make sure all the data hangs together across systems and is accurate and consistent.?
Fortunately, data mapping can help organize that chaos into a structured, meaningful format.
It's all about connecting the dots between different data sources so they speak the same language and play well together. For example, if one system calls a customer's age "Age" and another system labels it "Birth Year," data mapping is the bridge that says, "Hey, these are talking about the same thing!"
Interestingly, the systems that capture, generate or store data have different and unique requirements. It's not just that there’s a lot of information that needs to reference the same thing (the data in the “Age” column in one system can simply populate an “Age” column in a different system), but that there are usually nuances in how data is stored.
For example, if one system calls a customer's age "Age" and another system has a "Birth Year" label, simple mapping would just map "age" to "birth year," and the values wouldn't change. But if someone is 52, they weren't born in '52! That's where transformations come in, and it's where mapping can become very powerful. Taking "Age" (field) and 52 (value) and creating a mapping rule that subtracts the age value from the present year will give you a value of 1972, which can be put into the "Birth Year" field.
If you want an incredibly thorough introduction to data mapping, read this article to find out:
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