Structured and Unstructured Data

Understanding Structured and Unstructured Data

Structured data refers to any data which has a defined length and format. It is often organised in a way that is easy to understand and manage.
Examples of structured data include numbers, dates, and groups of words known as strings.
Unstructured data, on the other hand, lacks any specific form or structure, making it more complex to analyse and implement.
Examples of unstructured data might include an email body, a PDF file, a video, or a social media post.

Data consistency is one of the main advantages of structured data. All information is stored in a uniform manner, making data manipulation simpler.
Structured data uses the Database Management System (DBMS) for its management, storage, retrieval, and to enforce data integrity.
It is typically stored in Relational Database Management Systems (RDBMS), where all data items are stored in the form of tables.

Due to the lack of a specified format, unstructured data can be more challenging to analyse and process.
Unstructured data is often stored using a variety of data models, including but not limited to object-oriented databases and graph databases.
The most significant advantage of unstructured data is that it represents the raw data, more closely mimicking the human cognitive process.

Structured data is used in almost every industry that relies on big data. For example, it can be used in the medical field to manage patient records effectively.
Unstructured data also has many practical applications. It can be used to identify trends in social media posts or to analyse website user behaviour.
Both forms of data have their strengths and weaknesses. The selection of data format is usually based on the specific needs or requirements of the task at hand.