What is Unstructured Data? Beginner’s Guide

Unstructured data is all around us. Most of the data you consume daily, from videos to images and text, is unstructured. While this is not an issue when consuming said data, it becomes a burden when you need to analyze it.

Today, we look into unstructured data: its definition, examples, and differences between structured data formats.

What is unstructured data?

Unstructured data is information that does not have a predefined and organized format and structure, which makes this data type difficult to analyze, store, and categorize using traditional data tools.

Unlike structured data, which comes in the shape of tables and databases, unstructured data has more freeform formats such as text files, images, video and audio files, and others.

Structured vs. unstructured data

There are several main differences between structured and unstructured data.

Format

Structured data is organized in a predefined format such as tables or databases (rows and columns). This data type is easy to categorize and search through.

Unstructured data does not have any predefined structure or format and includes diverse types such as unstructured text, images, or video.

Storage

Structured data is typically stored in relational databases such as SQL, facilitating data discovery.

Unstructured data is stored in data lakes or NoSQL databases, in formats such as files or documents.

Searchability

Structured data is easy to search and query with SQL and similar languages.

Unstructured data is much more difficult to search through and requires advanced technologies such as natural language processing. For use cases such as sentiment analysis, this can slow things down significantly.

Data size

Due to its format and high level of organization, structured data is typically smaller in size.

Unstructured data is larger and more complex since it’s comprised of different formats such as images and audio.

Analysis complexity

Structured data is much easier to analyze because it has a predefined data model and is easy to run through with standard data analytics tools.

Unstructured data requires more complex tools that rely on artificial intelligence to process datasets.

Flexibility

Structured data is not very flexible and it must follow a very rigid schema.

Unstructured data is highly flexible and can accommodate various data types.

Semi-structured data

Semi-structured data falls somewhere between the two data types. It’s not fully structured like a table or a database, but it has some elements of structure in it.

Essentially, it’s unstructured data with elements such as tags or markers, that help define its structure and make it easier to parse and analyze.

Here are some key characteristics of semi-structured data:

• Flexible structure: a loose organizational framework with elements such as tags or key-value pairs that indicate relationships
• Hierarchical or nested: data can be nested within other data
• Easy to parse: can be parsed more easily than unstructured data, if you have the right tools

Examples of semi-structured data include:

• XML or JSON files: contain data and tags or keys for describing its structure
• NoSQL databases like MongoDB: where fields can vary from record to record
• HTML pages: contain a mixture of unstructured content and structured tags such as headings and links

Challenges of working with unstructured data

Whether you simply want to organize your data or use it for data analysis, unstructured data presents unique challenges.

Data organization: if data does not follow a predefined pattern and format, it’s much harder to organize and categorize it compared to structured data with a relational database.

Data integration: if you want to integrate data from multiple data sources into one location (e.g. data lake or a business intelligence tool), this can be very complex and time-consuming, without any possibility of automation.

Data storage and scalability: unstructured data such as video files and various kinds of multimedia take up quite a lot of space compared to text files. These types of data can clog up data warehouses very quickly.

Data quality and cleaning: unstructured data typically contains errors, missing fields, or irrelevant information. This means that cleaning and standardizing it (e.g. for later use in analytics tools) will take significant time and resources.

Processing complexity: to process unstructured data, you’ll need knowledge of artificial intelligence, machine learning algorithms, and the availability of natural language processing (NLP) tools. These tools analyze images audio files and similar data to turn it into standardized data.

Data security and privacy: it’s more difficult to ensure the security of your unstructured data, especially if it’s scattered around in different formats, apps and locations.

Lack of standardization: unstructured data does not follow a consistent format, which makes it difficult to apply traditional data management techniques and tools.

Examples of unstructured data

Here are some of the most common types of unstructured data:

• Text documents (Word documents, web pages, PDF files, Microsoft Excel files)
• Email messages (email content, including attachments and metadata)
• Social media posts (Tweets, Facebook posts, likes, comments, etc.)
• Multimedia files (images, videos, audio recordings)
• Chats and messages (from various apps)
• Sensor data (data from the Internet of Things (IoT) devices such as camera devices or industrial equipment)
• Customer feedback (reviews, survey results, and other qualitative feedback)
• Presentations (slide decks with a mix of text, images, audio and video)

While unstructured data is a rich asset to manage, it’s also incredibly complex for regular use in data analytics and similar applications.

Start your journey to data analytics with Luzmo

If you have structured data and wondering how to get real-time valuable insights from it, Luzmo can help. We specialize in embedded analytics for software products and we can help your end-users with decision-making based on data from your app.

Your end users don’t have to be data scientists to analyze and visualize data and you can add more value to your app, drive retention and lower churn.‍

Book a free demo today to learn how Luzmo can help you!