What is unstructured data, and how can an enterprise benefit from it? – Mantium

What is unstructured data, and how can an enterprise benefit from it?

December 8, 2022   ·   4 min read

Over the past few years, data has exploded.

It is projected that by 2025, data will grow to over 180 zettabytes globally. Yes, you read that correctly, zettabytes. In the world of big data, most of that data is unstructured. Unstructured data can be anything, including media, imaging, audio, sensor data, text data, and much more. Let’s explore what unstructured data is and if we can benefit from this data.

What is unstructured data anyway?

Unstructured means that datasets (typically extensive collections of files) aren’t stored in a structured database format. Unstructured data has an internal structure, but it’s not predefined through data models. It might be human-generated or machine-generated in a textual or non-textual format.

Unstructured data is data that is not actively being managed in a database or system. Usually, this data lives in a data lake, a repository of unprocessed data stored without organization or hierarchy. Most enterprise businesses have a plethora of unstructured data in unlimited forms. 

What are some examples of unstructured data? 

There are several types of unstructured data, below, we cover a few. 

  • Presentations: These formats are generated by presentation software like Apple KeyNote or Microsoft PowerPoint. Examples include ppt, keynote, gslides, or ppz.
  • Binary Files: These files represent the operating system library and other executable files, such as gsf, hex, exe, or bpk.
  • Database Files: These files are associated with different databases, such as OpenOffice Base or Microsoft Access. Examples include 4db, adt, box, kexic, contact, pdb, and more.
  • Word Processing: Word processors, such as Apple Pages or Microsoft Word, create these files. Examples include doc, docx, otm, wps, etc.
  • Compressed Data: As the name suggests, these file types are used to indicate compressed or archived data. Popular examples include 7z, zip, rar, rar5, etc.

What challenges do enterprises face when working with unstructured data?

Enterprises face issues of scale when looking to decipher unstructured data. Let’s explore how scale can affect the processing of unstructured data. 

  • Enterprise datasets can easily be on the scale of tens or hundreds of billions of items. These items or files can be a few bytes to terabytes in size. It becomes impossible to manage this data scale quickly with traditional file approaches. 
  • When scalability is an issue, it is difficult to find out if the data is relevant to an organization and what real benefit lies within the data. 
  • Searching for information and categorizing large amounts of data is a challenge. The human bandwidth needed to approach this is nearly impossible in many circumstances. 

So, what can be done to benefit from unstructured data?

Identifying the data sources within your organization is a great place to start.  Lack of visibility is a concern of every enterprise with unstructured data. Organizations can begin by locating all the resources, systems, and applications across legacy, multi-cloud networks, or data lakes where data could be located.

Unstructured data isn’t going to disappear on its own. It exists and will eventually grow and become even more challenging. With advanced technologies and automation, organizations can automate and streamline their unstructured and structured data discovery, classification, and cataloging to gain insight for sales and marketing, human resources, and accounting departments. Engineers will no longer need to rely on data scientists to provide data, allowing them to focus on higher-level tasks. 

Mantium can help.

Mantium’s end-to-end AI automation platform allows businesses to gain the insight they need from files of any type with the most innovative pre-processing engine available today. Turn your files into insightful, actionable data with endless powerful business-improving capabilities with Mantium. 

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