Difference between structured, unstructured and semi-structured data

What is the difference between structured, unstructured and semi-structured data?

If until now we have always worked with more readable information, with structured data, to do analysis and generate reports, now we have to be able to also analyse a whole other kind of data coming from the most disparate sources, from emails to social media posts, from online transactions to Internet searches. In short, from data that can be more or less structured.

The age of data

In today’s digital age, we are inundated with an infinite amount of data from multiple sources. This is the digital age and the age of Big Data, data characterised by a number of factors (including huge volume, high speed and diversity), the analysis of which requires companies to make a quantum leap in the level of digitalisation.

All three types – structured, unstructured and semi-structured data – belong to Big Data, so it is important to be able to exploit Business Intelligence to analyse all three forms.

What is Structured Data

Structured data are organised according to a rigid schema and a relational management model, thus well-defined as a database consisting of tables, spreadsheets or statistical reports. Hence, simple to query.

This data is extracted, interpreted/analysed and stored in a formatted repository, such as a SQL database. In this type of structure, there are a whole series of elements such as tables, rows and columns, and relational keys, which allow data to be mapped into predefined fields.

Examples of structured data

This type of data is generated by us, by our daily actions, and by machines (POS, barcodes, weblog statistics…). The data that we generate may be the data that we enter in the classic spreadsheet (for example, when we fill in tables with the names of customers in alphabetical order and some related data).

This type of structure and organisation allows us to analyse data easily and quickly. This is why structured data are used to manage large amounts of information and complex operations such as research and statistical analysis.


  • easy to store, query and analyse
  • can be easily used by applications and systems
  • are ideal for creating reports and dashboards.


  • can be expensive to collect and process
  • may be limited in the amount of information they can contain.
  • can be difficult to update and maintain.

What is Unstructured Data

In contrast to structured data, this type of data comes in different formats and in a raw form: it has no predefined schema or standard format, and can be text files, multimedia files such as images, video, audio, PDF documents.

Unlike structured data, unstructured data are not suitable for a relational database and are more complex to analyse. In fact, they require alternative platforms and more advanced processing techniques that can identify and interpret the data content without a predefined schema.

Natural language processing (NLP) tools help to understand unstructured data that exists in written form.

Examples of unstructured data

Unstructured data can be any information without a specific format: social media posts and comments, chats, emails, images and videos, audio files, text documents such as Word or PDF files, data generated by IoT sensors, the text of a web page, a log file.

All of these are qualitative data, because they are essential information for predicting trends or monitoring the performance of marketing campaigns.

Unstructured data accounts for most of the data generated in the digital world.


  • contain a large amount of information
  • can be easily collected from a variety of sources
  • can be used to identify trends and patterns.


  • difficult to archive, interrogate and analyse
  • difficult to use with applications and systems
  • not suitable for creating reports and dashboards.

Semi-structured data

Semi-structured data, such as JSON or XML files with some formatting rules but with a more flexible structure, lie somewhere between structured and unstructured.

This kind of data contains information with hybrid characteristics, with a flexible structure, but not ‘constrained’ in a relational database. Nevertheless, semi-structured data are characterised by certain organisational properties that facilitate their analysis, i.e. they also contain additional information such as metadata or tags that make them more organised than unstructured data.

Examples of semi-structured data

An example of data in a semi-structured format is a digital photograph, the image of which does not have a predefined structure, but has certain structural attributes that make it semi-structured.

For example, a photo taken from a smartphone will have some structured attributes such as geotag, device ID, date and time. After saving them, structures can be created through tags that can be assigned to the images (e.g. ‘yellow flower’ or ‘white cat’).

Another example of semi-structured data is the XML format, with which we can define fields and structures, but without the rigidity of structured data tables. Semi-structured data are often used in the semantic web and in contexts where greater flexibility in data management is required.


  • can be stored, queried and analysed more efficiently than unstructured data
  • can be used by applications and systems more easily than unstructured data
  • can be used to create reports and dashboards.


  • more expensive to collect and process than unstructured data
  • limited in the amount of information they can contain
  • difficult to update and maintain.

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