When we talk about structured, or tabular data, the name gives it away - it's relating to data that comes in easy-to-search formats that generally resides in databases. Usually in text format, with certain structures in place - dates, phone numbers, customer names or addresses, or even transactional information.
What’s a good analogy for structured vs unstructured?
Structured data is like an organized room with items sorted in drawers, making them easy to find with just some simple labeling. In contrast, unstructured data resembles a cluttered room with clothes, books, and toys scattered all over the place. Here, finding and understanding items becomes more challenging, and you'd need a more advanced tool, like a human brain or an AI like Levity, to help you navigate and find the items.
Our website may give the impression that Machine Learning is the answer to every problem that exists, but there are cases where less is more. If you have tasks with well-defined rules that can be easily automated, traditional RPA (Robotic Process Automation) tools might serve you more effectively and efficiently, as you can rely on a pre-programmed pattern. For example, if you want to organize all emails from one sender into a folder, a simple forwarding rule in your inbox will suffice.
The unstructured data tasks that Levity excels at
Levity truly shines when it comes to handling tasks that require understanding and processing unstructured data, where rule-based solutions might struggle.
Think of your inbox where you have to read the email to understand how to file it away - perhaps you have to take a look at the attachment, or intuit the tone of the text. Another example is a product inventory, where you might need to look at the item in the photo and manually classify it into the correct folder. Tasks that need that extra layer of cognitive thinking are perfect for Levity.
Check out our success stories to find out how other companies are implementing Levity into their daily processes.
