Examples of Unstructured Data

Overview



What are examples of unstructured data?

The majority of the data you can use to intelligently build your business comes in the form of unstructured data. Managing intelligent data requires a sophisticated solution that can easily scale. MongoDB Atlas was built to be agile, providing developers and companies a flexible database solution, maximizing how they access, manage, and use unstructured data.

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What is unstructured data?

Unstructured data is data that you cannot store in the traditional structure of a relational database. It’s sometimes referred to qualitative data — you can derive meaning from it, but it can also be incredibly ambiguous and difficult to parse.

Because of the nature of unstructured data, document-type storage options, which are non-relational (NoSQL) databases, are ideal since they can more easily adapt their data model with a flexible schema.





Unstructured data examples

  • Email: While we sometimes consider this semi-structured, email message fields are text fields that are not always easily analyzed.

  • Multimedia content: Digital photos, audio, and video files are all unstructured. Complicating matters, multimedia can come in multiple format files, produced through various means. For instance, a photo can be TIFF, JPEG, GIF, PNG, or RAW, each with their own characteristics.

  • Text files: Almost all traditional business files, including your word processing documents, presentations, notes, and PDFs, are unstructured data.

  • Social media: Social media has a component of semi-structured data you can access through built-in analytics, but the content of each social media message is unstructured.

  • Websites and markup language: The content on the web may be tagged, but code is not designed to capture the meaning or function of tagged elements in ways that support automated processing of the information contained on each page. XML provides an element of structure, however, these building blocks are filled with unstructured elements.

  • Mobile and communications data: Your customer service and sales team are collating unstructured data in their phone calls and chat logs, including text messages, phone recordings, collaboration software, conferencing, and instant messaging.

  • Survey responses: Every time you gather feedback from your customers, you're collecting unstructured data. For example, surveys with text responses and open-ended comment fields are unstructured data.

  • Spreadsheets: While Excel and CSV files are considered structured repositories, depending on how you use them, they can store semi-structured or unstructured data.

  • Scientific data: Field surveys, space exploration, seismic imagery, atmospheric data, topographic and weather data, and medical data. While these may have a base structure for collection, the data itself is often unstructured and requires thoughtful analysis.

  • Machine and sensor data: billions of small files from IoT devices and business systems outputting information into log files are not consistent in a structured data manner.





Considerations for unstructured data

By 2025, IDG projects that there will be 163 zettabytes of data in the world, and estimates indicate that more than 80% of this data is unstructured.

Unstructured data can produce high value insights about your business or brand, which can guide important business decisions. But because of its ambiguous nature, it is often challenging to glean those valuable insights. Data platforms like MongoDB Atlas are especially adept at housing, managing, and using unstructured data.



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