Structured and Unstructured Data

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.

Characteristics of Structured Data

  • 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.

Characteristics of Unstructured Data

  • 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.

Practical Applications

  • 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.