What Is Unstructured Data?

Unstructured data is information that isn't predefined and searchable in a table, such as text messages, images, videos, audio files, and emails. Unlike structured data, which is easily placed into tables like those found in Microsoft Excel, unstructured data can't be quickly analysed and searched without further processing.

However, it is still very useful data. Unstructured data can be a valuable source of insights for businesses and researchers.

Discover more about unstructured data, including how it's used, differs from structured data, and what tools help you manage and process it. 

Unstructured data definition

Unstructured data refers to information that does not have a predefined model or organisation, making it difficult to store, process, and analyse using traditional relational databases or spreadsheets. Unlike structured data, unstructured data lacks a consistent format or schema, which makes it challenging to extract meaningful insights without additional processing. 

Nonetheless, unstructured data can provide valuable information for data scientists and other professionals who use it to generate insights on various topics, such as customer sentiments and experience. In effect, unstructured data allows data professionals to analyse the vast amount of qualitative data produced by consumers daily rather than relying solely on narrowly defined, quantitative metrics.

Unstructured data examples

Unstructured data can take various forms, including text documents, emails, social media posts, images, videos, audio recordings, presentations, and more. It often contains free-form text, natural language, and multimedia content. In other words, unstructured data encompasses all kinds of qualitative data individuals produce daily without clear-cut quantitative data points.

In turn, data professionals can find unstructured data from a wide variety of different sources. Some particularly rich sources of unstructured data include:

• Customer reviews

• Social media conversations

• News articles

• Sensor data from Internet of Things (IoT) devices

These and many other sources provide a trove of unstructured data you can mine to better understand how individuals view a product, topic, or brand. Using this information, businesses and organisations can improve their products and services to achieve their overall goals.

What is the difference between structured and unstructured data?

Structured data refers to any kind of data that is defined and searchable, such as dates, prices, phone numbers, product SKUs, and banking information. As a result, structured data is easily placed into tables within relational databases and is generally quantitative in nature.

By comparison, unstructured data refers to data that is not defined and easily searchable, such as text messages, videos, online reviews, and social media posts. In effect, unstructured data is stored in non-relational databases, which don't store information solely in tables and are often qualitative in nature.

Both structured and unstructured data can potentially provide valuable insights to data professionals and researchers.

Unstructured data uses and tools

Because it lacks structure, unstructured data requires specialised tools and techniques to extract valuable information.

Machine learning, natural language processing (NLP), and other data mining techniques are commonly used to analyse unstructured data and uncover hidden patterns, sentiments, and trends. Text mining, image recognition, and speech recognition are techniques employed to process and derive insights from unstructured data. 

To identify these insights, data professionals use a variety of tools. Some of the most common include:

• Apache Hadoop

• MongoDB

• DynamoDB

• Azure

• Power BI

• Tableau

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Unstructured data, like emails and social media posts, is information that isn't organised in a predefined way. Unlike data in tables, you need special tools to analyse it more easily. This vast amount of qualitative data can provide valuable insights, so data scientists use techniques like machine learning to process and extract useful information.

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