Data Security

What is Structured Data?

rectangle Written by: Anne Gotay rectangle 2 2 min read

What is Structured Data?

What is Structured Data? Structured data is a type of data that is organized and formatted in a way that it’s easily searchable and analyzable by data analysis tools and algorithms. It adheres to a data model – a predefined framework of how data is organized and accessed. This type of data is typically arranged in tables with rows and columns, where each column represents a particular variable, and each row corresponds to a given value for that variable. Examples of this data type include relational databases and spreadsheets.

What is the Difference Between Structured and Unstructured Data?

When we talk about the types of data, we often categorize them into structured and unstructured data. The key difference between lies in their level of organization and how they can be accessed and used.

As we’ve discussed, this data is highly organized and follows a specific format or schema. It’s easy to search, store, and analyze. On the other hand, unstructured data  doesn’t follow a specific format or model. It’s more free-form and can include everything from text files and social media posts to images and videos.

Unstructured data can be more challenging to analyze and use because it doesn’t fit neatly into traditional databases or tables.

How to Protect Structured Data

Protecting structured data is a critical aspect of data management. Here are some steps to ensure the safety of your data:

  1. Access Control: Implement strict access control measures. Only authorized personnel should have access to sensitive data.
  2. Encryption: Use encryption to protect data both at rest, in transit, and in use. Encryption converts data into a code that can only be deciphered with the correct key.
  3. Backup and Recovery: Regularly backup your data and have a recovery plan in place in case of data loss.
  4. Regular Audits: Conduct regular audits to identify any potential vulnerabilities and address them promptly.
  5. Data Masking: Use data masking techniques to hide sensitive data, especially when used for testing or development purposes.

Why Protecting Structured Data is Important

Protecting structured data is crucial for several reasons. Firstly, this data often contains sensitive information, such as personal details, financial information, or proprietary business data. Unauthorized access to this data can lead to severe consequences, including identity theft, financial loss, and damage to a company’s reputation.

Secondly, regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) mandate strict data protection measures. Non-compliance can result in hefty fines and legal action.

Lastly, protecting data is vital for maintaining trust with customers and stakeholders. People and organizations want to know that their information is safe and handled responsibly.

Use Cases

There are a wide range of use cases across various industries:

  1. Healthcare: In healthcare, structured data can help track patient information, manage treatment plans, and analyze health trends.
  2. Finance: Financial institutions use structured data for risk analysis, fraud detection, and investment decision-making.
  3. Retail: Retailers leverage this data to track inventory, analyze sales trends, and personalize customer experiences.
  4. Education: Educational institutions use this data to track student performance, manage enrollment, and conduct educational research.


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