Short answer: Structured data is organised in fixed formats like tables and databases, making it easy to search and analyse. Unstructured data lacks a predefined structure and includes information such as text, images, and videos, which requires advanced tools to process and analyse.
Why the difference between structured data and unstructured data matters
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.
What is structured data?
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
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What is unstructured data?
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
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Structured and unstructured data in applications
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.
Unstructured data management challenges
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.
Unstructured personal data and the GDPR
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.
FAQ om GDPR-software
Hvad er GDPR-software?
GDPR-software er værktøjer designet til at hjælpe virksomheder med at overholde kravene i databeskyttelsesforordningen gennem automatisering og effektivisering af processer relateret til databeskyttelse.
Hvorfor er det vigtigt at bruge GDPR-software?
Det hjælper med at reducere risikoen for databrud, sikrer korrekt håndtering af persondata og kan forhindre potentielle bøder for manglende overholdelse af GDPR.
Kan GDPR-software integreres med vores eksisterende systemer?
De fleste GDPR-softwareløsninger er designet til at integrere med eksisterende IT-systemer for at lette implementeringen og sikre en gnidningsfri overgang.
An easier way to handle unstructured 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.
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Sebastian Allerelli
Founder & COO at Safe Online
Sebastian is the co-founder and COO of Safe Online, where he focuses on automating processes and developing innovative solutions within data protection and compliance. With a background from Copenhagen Business Academy and experience within identity and access management, he has a keen understanding of GDPR and data security. As a writer on Safe Online's Knowledge Hub, Sebastian shares his expertise through practical advice and in-depth analysis that help companies navigate the complex GDPR landscape. His posts combine technical insight with business understanding and provide concrete solutions for effective compliance.
