What Is Unstructured Data?

Written by Coursera Staff • Updated on

Unstructured data comes in many different forms and depends on specialized tools and expertise to transform it into usable information.

[Featured image] A data scientist works on cleaning some unstructured data at her workstation with a laptop and computer monitor.

Unstructured data is all that information that isn't predefined and searchable on 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 analyzed and searched without further processing.

But, that doesn't it's useless. In fact, unstructured data can be a valuable source of insights for businesses and data researchers alike.

In this article, you'll learn more about unstructured data, including how it's used, how it differs from structured data, and what tools help you manage and process it. At the end, you'll even explore a cost-effective, flexible that can help you learn even more about data.

Unstructured data definition

Unstructured data refers to information that does not have a predefined model or organization, making it difficult to store, process, and analyze 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 a wide range of topics, such as customer sentiments and experience. In effect, unstructured data offers data professionals an opportunity to analyze the vast amount of qualitative data produced by consumers every day, 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, then, unstructured data encompasses all the different kinds of qualitative data produced by individuals every day that lack clear-cut quantitive 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:

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

What is the difference between structured and unstructured data?

Though they sound similar, unstructured and structured data are actually very different from one another.

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 on tables within relational databases and is generally quantitative in nature.

Unstructured data, by comparison, 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 have the potential to provide valuable insights to data professionals and researchers.

Read more: Structured vs. Unstructured Data: What's the Difference?

Explore related data terms on Coursera

Want to learn more about core data concepts? Explore these articles covering data-centric terms:

- Relational database

- Big data analytics

- Data wrangling


Unstructured data uses and tools

Because of its lack of structure, unstructured data requires specialized tools and techniques to extract valuable information from it.

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

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

  • Apache Hadoop

  • MongoDB

  • DynamoDB

  • Azure

  • Power BI

  • Tableau

Read more: Looker vs. Tableau: Differences and Use Cases Explained

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