Structured data vs. unstructured data

In a world where the amount of data is growing exponentially, the ability to understand and work with different data types is essential. Data can generally be divided into two categories: structured and unstructured. Structured data is organized in a fixed way, while unstructured data is far more complex and does not follow a fixed structure. Understanding the differences between these two types of data is key to effective analysis, management and data protection.

Structured data is organized in a clearly defined format, typically in tables or databases, where data fields are predetermined (eg rows and columns). It makes it easy to search, analyze and manipulate.

Characteristics of structured data

• Data has a fixed structure (rows and columns in a database, such as SQL).
• Each data unit fits into a predefined field (eg name, address, phone number).
• It’s easy to search, filter and analyze.
• Often used in relational databases.

Examples of structured data

• Customer data in a CRM database (eg name, email, phone number).
• Financial transactions (amount, date, account number).
• Excel sheet with well-defined columns.

Advantages of structured data

• Easy to search and access with defined machine learning (ML) algorithms
• Easy to track and understand the outcome
• Requires less treatment and is easier to handle

Disadvantages of structured data

• Less flexible as the structure is pre-defined and cannot be changed
• It takes more time and resources to change and update the format

About 80-90% of the world’s data is unstructured. This type of data lacks a defined structure and does not fit easily into a database format. It often contains text, images, audio or video and requires more advanced processing and analysis methods (eg text mining or image recognition) to extract valuable information.

Characteristics of unstructured data

• Data has no fixed structure or format.
• Information is often stored in files such as documents, images or sound files.
• It is more difficult to search and analyze without specialized tools.
• Often used in NoSQL databases or big data solutions.

Examples of unstructured data

• Emails, PDF documents, social media posts.
• Videos, pictures, sound recordings.
• Chat conversations, blog posts, reports.

Advantages of unstructured data

• It is more adaptable
• It can be collected quickly and easily
• It is cheap and easy to store in large quantities

The disadvantages of unstructured data

• Lack of visibility
• Difficult to see how it is best used and protected
• Data management tools are needed to manipulate unstructured data

When talking about structured and unstructured data, it is important to understand that you cannot divide applications into being structured and unstructured. Many modern applications handle both types of data at the same time. For example, a CRM application or an email service may process contact information about customers in a structured format, but at the same time accommodate emails and notes in an unstructured format. It is partly about how data is processed and partly in what format the data is in, since certain data types are unstructured by nature – for example images and videos that do not fit into a standardised data model.

Worldwide, unstructured data is far more abundant than structured data. Because it comes in so many formats and is easy to store, most companies have a considerable amount of unstructured data in their systems. Managing unstructured data without the right tools is difficult, because its raw and unorganised nature makes it difficult to search and access which results in low visibility.

These terabytes of unstructured files your company has accumulated over the years are certain to contain plenty of personal data and sensitive personal data. The low visibility of unstructured personal data presents a special challenge for compliance with privacy laws like the GDPR, CCPA and others. New privacy laws put limits on how long you store personal data, and they require you to monitor and protect it to make sure it will not be accessed by unauthorised persons. 

Leaving unstructured files in data lakes without keeping track of the personal data contained in them is a good way to get fined. Make sure personal data does not linger in your systems too long. When you are no longer using data for the purpose for which you collected, it should be deleted.  

To meet these requirements, you must have systems in place to sort, classify and monitor unstructured personal data. 

The technology has to keep up with the increasing demand for handling unstructured data. Our data discovery tool, DataMapper, is ideal for handling both structured and unstructured data with a focus on personal data privacy and GDPR compliance.