Crossing the Chasm of Unstructured Data

Matt Parsons

VP of Sales

SUMMARY

The rapid growth of unstructured data is careening into crisis territory. 90% of the world’s data was created in the last two years, and 80% of the data created is unstructured. That represents a 250% increase since 2018, and this trend shows no signs of slowing. Unstructured data is information that does not follow a predefined model or schema, making it far more difficult to process and analyze than structured data (which adheres to a predefined data model).

This article explains why the rapid expansion of unstructured data is at crisis levels, and how artificial intelligence (AI) can be used to overcome it.

Solving the unstructured data crisis with AI

The iceberg principle is a theory that suggests most of a situation’s data is not visible. Similar to an iceberg, where only the tip (structured data) can be seen—with the majority of the object’s mass hidden beneath the ocean (unstructured data). This hidden data includes some forms of semi-structured information as well as fully unstructured data such as images, video, audio, and text.

Problems with traditional approaches to unstructured data preparation

There are a few common approaches that can be used to enrich and structure data so that it can be more easily interpreted by machines and used to power intelligent automations. Some of the traditional approaches to cleaning and organizing unstructured data include:

Outsourced data preparation involves companies hiring third-party vendors to label and/or annotate unstructured data so that it can be more readily processed. This is often an extremely costly process that requires constant reevaluation on a per project basis because ongoing data processing is ultimately too costly.
Hybrid outsourced and in-house data preparation is when companies combine in-house employee annotators with outsourced crowd labeling (e.g., Amazon’s Mechanical Turk) or some other third-party business process automation (BPO) vendor. Note that it can be difficult to find outsourced annotators that have the specialized knowledge required for specific scenarios.
Weak supervision data preparation is a part of machine learning where models are trained using incomplete, inexact, or otherwise less accurate unstructured information sources that are more available than verified, hand-labeled data. This approach requires limited human involvement and results in weakly labeled data. Labels are considered “weak” because the measurements they represent are not accurate or they include information not directly related to the prediction the dataset will be used for.

All of these approaches suffer from inconsistent output accuracy, high costs, or both. Additionally, none of these data preparation methods fully automate unstructured data processing (UDP) despite recent technological advances making it possible to do so.

AI-powered unstructured data processing

There have been some incredible advancements in the ability to automate unstructured data processing. New UDP platforms built specifically for unstructured data processing make it possible to automate data preparation and avoid the headaches and inaccurate outputs of traditional methods. Advanced UDP platforms empower nontechnical business users to leverage artificial intelligence using a no-code interface, making AI more accessible than ever.

The biggest boon that UDP is expected to deliver against the data crisis will be truly enabling AI. There has been an explosion in the popularity and value of AutoML platforms such as AWS, Microsoft, Google, DataRobot, Dataiku, and H20.ai. However, people using these platforms continue to face issues when it comes to leveraging them for unstructured data analysis. Existing platforms don’t offer an easy and reliable way to prepare unstructured information, making it useless for forward-thinking organizations seeking to unlock hidden insights using AI.

Companies that have invested heavily in AI solutions can quickly become frustrated by the costs and difficulties associated with sourcing or preparing accurate datasets. UDP platforms make it possible to quickly, reliably, and inexpensively prepare unstructured information for analysis. Additionally, the ability for nontechnical users to take advantage of AI makes it possible for companies to overcome the skills gap that often gets in the way of AI adoption.

Additional unstructured data processing resources

Super.AI is all about unstructured data processing. We make it possible to quickly train, test, and deploy custom artificial intelligence solutions with or without learning to code. Our mission is to make AI accessible to everyone and automate repetitive tasks so that people can focus on the work they enjoy. If you’re interested in learning more about UDP, check out the following resources: