What Is Unstructured Data?

Key takeaways

• Unstructured data (such as images and text messages) lacks a predefined format, so tools like relational databases and spreadsheets can't easily store or analyze it.

• Structured versus unstructured data: While structured data refers to predefined, searchable data types, such as dates and phone numbers, unstructured data refers to information that is difficult to define and search, such as online reviews and videos.

• Additional unstructured data examples include social media posts, audio files, and emails.

You can handle unstructured data using data mining techniques such as machine learning and natural language processing (NLP) to uncover patterns, sentiments, and trends hidden within it.

Discover more about unstructured data, including how it's used, how it differs from structured data, and what tools help you manage and process it. If you’d like to begin developing your data analytics skill set, enroll in the Google Data Analytics Professional Certificate. In as little as six months, you can learn about interactive data visualization, data cleansing, data structures, data literacy, and more.

What is unstructured data?

Unstructured data is all that 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 analyzed and searched without further processing. But that doesn't mean it's useless. In fact, unstructured data can be a valuable source of insights for businesses and data researchers alike.

What are the characteristics of unstructured data?

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.

Read more: Qualitative Data: Definition, Types, and Examples for Understanding User Behavior

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 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 that can be mined to better understand 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.

Structured vs. 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 in 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.

How to handle unstructured data

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

Machine learning, 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 common ones include:

• Apache Hadoop

• MongoDB

• DynamoDB

• Azure

• Power BI

• Tableau

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

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