Unstructured Data Analysis: Finding the Needle in the Data Haystack

02.17.2021

Unstructured Data Analysis

While there are tools available, unstructured data is still a lost puzzle piece searching for its perfect fit. As unstructured content grows, companies have adopted numerous storage solutions, making system consolidation difficult and universal oversight nearly impossible. Developers are currently still working on unstructured data analysis tools and creating best practices for their management and governance.

What is Unstructured Data?

Sometimes called unstructured information and classified as qualitative data, unstructured data is data that has no pre-defined data model or pattern and is, therefore, unorganized and not easily searchable by AI or machines. To add, unstructured data is data most often created by people, rather than by systems.

Unstructured Data Examples:

While structured data has a formal structure in place, unstructured data on the other hand, simply put, does not.

  • Audio
  • Text files
  • PowerPoint presentations
  • Social Media Data
  • Video
  • Mobile Activity

Unstructured Data VS Structured Data Example:

Unstructured VS Structured Data

How Do You Analyze Unstructured Data?

If you cannot easily organize it, then how do organizations analyze unstructured data? The search-difficulty of unstructured data naturally makes its content analysis challenging.

While legacy approaches to managing content are constrained to a specific cloud service, on-premises storage system, or business application, there are technologies that integrate and unify all content sources.

 

Unstructured Data and AI

The volume of information entering organizations, especially unstructured data, is accelerating at a staggering 50% per year. Manual methods of unstructured data analysis are costly and often fail due to fragmented storage systems and mismanaged solutions. Companies that cannot properly identify the information they possess run the continuous risk of security breaches and costly repercussions.

Machine Learning and Unstructured Data

Machine learning algorithms classify and label content by identifying sensitive, high-risk, obsolete, duplicate, and “dark” data.

Because machines can easily search for structured data, it is, as a result, easy for those machines to analyze that data. On the other hand, unstructured data requires additional processing since it is inherently difficult for machines to find.

Unstructured Data and Compliance

Companies must operate within ever-changing data compliance requirements and pressures such as GDPR, CCPA, and HIPAA. Relying upon the user to take appropriate actions to enforce governance policies and remain complaint, companies with mismanaged data simply cannot keep up.

This lack of control opens organizations up to large regulatory fines, substantial loss of sensitive data or intellectual property, unnecessary or redundant costs, operational inefficiency, and can negatively impact an organization’s overall market value.

Conclusion

Unstructured data analysis is no small hill to climb. Unstructured data is everywhere, growing, and difficult to find, making machines and AI necessary for organizations that want to fully leverage all their data to its full potential.

Thankfully, the technology is getting smarter every day, making this a reality, and frankly, a necessity. With privacy and compliance mandates becoming stricter every day, it is now up to individual organizations to ensure their data is organized, managed, and compliant.

 

Mallorie Brazeau