What’s the Difference Between Structured, Semi, and Unstructured Data?

Are you actively looking to automate processes within your business? Then you know how data-driven it can be. Since you’re working with a wide array of data, finding out how to use AI tools to your advantage involves knowing the nature of the data itself.

There are three main categories we think of when we talk about data to automate: structured, semi-structured, and unstructured. What do they mean, and how are they important when it comes to applying artificial intelligence to streamline the process?

The basic idea is that structured data is relatively easy to work with because it’s consistent enough that a single approach is sufficient for all of it. With the other types, you may need more intelligence automation for the job. Let’s go into further detail here.

Structured Data

Any data within an organized database or spreadsheet is considered structured. Automation can easily map each piece of information to a fixed point in this case, as analyzing structured data largely involves repetitive tasks with little variation. Examples include:

  • Personal information, like names, addresses, and contact information.
  • Transactional data
  • Product names and identifiers

When business users think of structured data, they typically imagine relational databases where data can be viewed by criteria. You can sort the information easily this way and specify search parameters to facilitate insights, and programmers often use SQL (Structured Query Language) when working with it.

As long as you tell the tool where to look and little variance exists, extracting information from a structured data stream is easy to automate. One example is optical character recognition, which allows a machine to “read” documents literally.

Unstructured Data

Business analysts would prefer if all data was structured, but the reality is that not all the information you will collect will adhere to specific formats. Data received from emails, documents, presentations, or call transcripts are naturally “raw” and unstructured. In fact, the vast majority of business data ends up unstructured. Examples include:

  • Most text documents
  • Content on websites and social media posts
  • Communication between human users such as through chat applications or text messages
  • Video or audio files
  • Data from surveillance equipment or scientific sensors

This data type poses a problem for analysts, as past automation technologies could not handle all the random formats. It wasn’t until relatively recent advances in artificial intelligence did automation become possible here.

An AI reading unstructured data might interpret information like:

  • Numbers and figures
  • Names and addresses
  • Visual cues (such as defects in an image of a product)

The tool essentially looks at the data the same way a human would: coming to conclusions by looking at the complex parts of the document and coming to accurate conclusions.

Semi-Structured Data

At the same time, not all business data can be given a black-and-white “structured or not” moniker. The semi-structured category specifies all types of data that fall somewhere between the previous two categories.

Examples of the semi-structured class include:

  • Emails: The body text is obviously natural language that’s difficult for a machine to parse. However, there’s also the header that contains structured information, such as the identities of the sender and recipient and the time of receipt.
  • Photographs: The image itself is unstructured, but visual files also contain metadata like the location and time the image was taken.
  • XML: This markup language assists with encoding documents that distinguish between content readable by humans and by machines. XML enables a significant amount of flexibility.
  • JSON: The open standard JSON is often used to transmit data between servers and web applications. Recognized by both Java and other C-like languages, JSON is considered a semi-structured format.
  • Invoices: Invoices from a single vendor are often structured, but your company likely orders from multiple providers, each with its own invoice format. It’s not unrealistic to train an RPA tool to extract the right data from all formats in automated invoice processing.

Automation tools work with semi-structured data by first latching onto the structured side that’s simpler to process. You can quickly determine important metrics like the date and time. From there, you need an intelligent tool to process the rest of the document.

Automation in general is heavily recommended for semi-structured content because it handles changes in format well. If a single vendor changes up its invoice format, you won’t have to recalibrate your workflow manually.

Simplify Your Automation Journey With Digital Workers

Automation doesn’t have to be hard if you have access to powerful AI and the right tools to enable it.

The goal of any automation journey is to unify artificial and human intelligence to amplify creative potential in the workplace. But none of that is possible without access to the right types of data, whether it’s structured or semi-structured.

Are you looking to explore your automation capabilities? Get in touch with our automation specialists today to explore how digital workers can kickstart your automation journey.