Unstructured data is the content an organization possesses that is not categorized or otherwise in a beneficial and usable format. However, data scientists are successfully implementing ways to depend on machine learning algorithms to give it structure and help the owners of that data extract essential details from it.

Why Is Unstructured Data So Prevalent Now?

In today’s tech-driven society, there are many reasons for the increase of unstructured data. One of them is the way people can quickly capture media and sound clips stored in the cloud. Furthermore, it’s easy for anyone with a computer or tablet to become a content creator.

Machine Learning to Capture Valuable Characteristics of Conversations

When people think of unstructured data, documents often come to mind. However, some experts believe one of the most efficient ways to capitalize on unstructured data is to analyze the conversations people have. That tactic is becoming more straightforward than it once was thanks to the rise of video conferencing tools and voice-capturing software.

Some companies are experimenting with ways to use machine learning to track trends in the ways people communicate. Then, it may be possible to cater to those preferences and make organizations more productive. Similarly, a sales professional could look at data taken from machine learning algorithms and see if specific leads prefer video or voice calls, or which times of the day are best to call them based on when they’re typically home.

Machine Learning Processors Are Getting More Advanced

It appears there’s no better time to continue exploring how to harness machine learning to sort through unstructured data efficiently. Device productivity is continually evolving to meet user and manufacturer needs. At the same time, the technology needed to handle machine learning is also improving — and getting smaller.

There are plans to include AI chips in most smartphones and make them capable of handling machine learning algorithms even when they’re offline. As machine learning algorithms get smarter, the market opportunity widens for unstructured data categorization tools that are accessible even to customers who aren’t tech savvy.

A company called Io-Tahoe has machine learning technology that’s able to look deeper than metadata and use over a dozen algorithms to find the relationships between data points and plot them on a map. Io-Tahoe’s technology can also check for data across multiple repositories. The target market is reportedly non-technical-oriented business owners.

Currently, the tool is for structured and partially structured data. However, the company is working on expanding it to include compatibility for unstructured data.

Cloudian is another company that understands the worth of unstructured data and wants to help companies uncover it. The solution it offers uses file and object-based systems to put all unstructured data into a scalable pool.

Relying on Machine Learning for More Informed Decisions

One of the primary reasons people are so interested in finally making sense of unstructured data with help from machine learning is because it could allow them to see things that would otherwise remain invisible.

In healthcare, for example, a specialist could tap into a machine learning database to compare material related to a single patient with millions of others to detect anomalies. Also, physicians might pull insights from machine learning algorithms and come to educated conclusions they can share with their colleagues and potentially affect entire societies with their findings.

The health industry has both structured and unstructured data. However, the potential use cases above relate to the unstructured variety and give a glimpse into why people think machine learning for the medical field shows particular promise.

Social media also has massive amounts of unstructured data, but companies are working to learn more about what it contains. In one instance, an entertainment company wanted to track levels of social media fan engagement related to the enterprise’s artists. It also examined geographical characteristics to identify markets where the musicians could succeed and wanted to find hidden talent to possibly grow its brand.

To achieve those goals, the company chose a machine learning platform that evaluated both structured and unstructured data. In this case, the technology even had language and translation capabilities, enabling the brand to look at metrics from non-English-speaking countries. In total, the platform handled more than 50 million pieces of structured and unstructured data.

Among the benefits was a decision-making database that allowed the company’s representatives to reach more accurate conclusions when determining how to utilize an artist’s marketing budget. It allowed for an approximately 20 percent optimization of spending through various marketing channels.

The information above sheds light on how machine learning can find patterns, identify relationships and thereby make unstructured data a viable resource for its owners to utilize for business growth and societal benefits. As technology continues to advance, the people who work with data daily will come across even more ways to deal with unstructured data in ways that make sense.

You can find more insights into the nature of this critical megatrend in the CTOvision section on Artificial Intelligence. And track the most important tech concepts for C-suite executives at The Boardroom.