Table of Contents

  • What is Unstructured Data?
  • How do I turn Unstructured Data into Structured Data?
  • What is Data Centric AI?
  • Why is Unstructured Data Important?
  • What are the Differences between Training Data Tools, Training Data Platforms, Data Labeling, and Annotation?

What is Unstructured Data?

Process of Unstructured Data to Human Review to AI/ML
Fig 1: Process of Unstructured Data to Human Review to AI/ML

Images, videos, 3D, text, and audio are all examples of Unstructured data. It’s data that doesn’t have a structure easily readable by supervised machine learning algorithms.

Fig 2: Example of 3D Annotation UI, Image Annotation UI, Attributes with Radial Form

People annotate data to convert it from unstructured to structured. Humans draw shapes, fill in forms, and otherwise annotate the data. This creates a structure that makes it usable by machines.

In the past for classic cases unstructured data was still processed with minimal human review. In new modern deep learning systems often a human must review the data.

How do I turn Unstructured data into Structured Data?

By using software like Diffgram your team can annotate data and make it ready to be used for machine learning.

This is an iterative process. The machine learning system will feed data back to Diffgram, which your team will then continue to update and improve.

What is Data Centric AI?

Data centric AI is the idea of focusing on training data as being more important than data modeling. This is because systems that may be impossible without training data may be easy to create with training data. And that for existing systems it’s easier to improve performance.

Why is Unstructured Data Important?

There are more users.  Subject matter experts and vast armies of data entry folks. There may be 100 annotators or more for every 1 data science person. And those annotators spend more time 4-8+ hours with training data tools like Diffgram. As compared to 0-1 hours per day in traditional data science tools.

If 99% of the time is training data, and 1% in data science modeling, where are you spending your budget?

What are the Differences between Training Data Tools, Training Data Platforms, Data Labeling, and Annotation?

Some systems have more functions, for example Open Source Diffgram rolls 9 training data tools into one platform. Data labeling and Annotation focus on the core Annotation user interaction. This is very important, however it is only one piece of the puzzle. The data must also be properly ingested, there are task workflows to consider and more. Sometimes when people say data labeling they are referring to that entire overall process. This is better then alternatives like Labelbox that are closed source.