What is unstructured data?
Unstructured data includes all files and data formats that do not have the predefined attributes that are essential in a data model. This lack of identifiable attributes leads to challenges in identifying and organizing unstructured data, especially with today's rapidly rising dark data. Some examples of unstructured data include emails, texts, videos, images, and other rich media.
Unstructured data vs. structured data
The difference between unstructured data and structured data is important when considering avenues of securing both types of information. Some of them are:
|Structured data||Unstructured data|
|Adheres to a prescribed data model defined by identifiable attributes||Has no identifiable attributes that can be used to organize it|
|Can be stored in a relational DBMS for easy sorting and access||Cannot be stored in a relational DBMS as it does not conform to any data model|
|It is easier to maintain data integrity due to the systematic storage and the ability to work with and manage data through query processing. This can be leveraged to maintain updated data versions without duplicate instances.||Data integrity cannot be ensured. Maintaining the consistency of data is difficult due to a lack of attributes, which may lead to multiple iterations of the same data.|
|Can be easily and effectively analyzed to gain rich insights||Difficult to analyze owing to its vast volume and disorganized storage|
Types of unstructured data
Unstructured data can be categorized two ways: based on source and based on content.
Unstructured data based on source of generation
- Human-generated data: This includes files, memos, and other data that people create, save, and upload to websites or store in applications. Examples include profile photos, names, and other sensitive personal data uploaded to social media sites.
- Machine-generated data: This data is created for a specific purpose, such as for reports, audits, or other processes. Examples include weather and atmospheric data, surveillance footage, and satellite imagery.
Unstructured data based on content
- Textual formats: These data sets contain text like webpages, emails, or personal message threads.
- MNon-textual formats: These data sets contain formats other than text and include audio-visual components like videos, GIFs, and images.
Securing unstructured data
Identifying unstructured data is indeed challenging. However, custom tools can be used to identify and secure unstructured data in data stores. The following concepts can be deployed for securing unstructured data:
Set up data discovery to identify both text-based and non-textual data. A complete roundup of your file repository can be performed using file analysis software to detect unstructured data. Further strengthen data security by discovering and securing sensitive data instances in your file repositories using a PII scanner.
Sort the identified data to assign it the right priority. You can manually tag or automate file classification with a data classification tool. This can help organize your data stores and apply the right level of security controls based on the importance of the data.
Follow up with data loss prevention to safeguard data that you have identified and classified. Secure endpoints with multi-factor authentication and user authorization. Encrypt data and storage devices to prevent data tampering. Set up a sound track-and-response system to block potential data exfiltration attempts.