Structured vs. unstructured data: differences

The world of data analysis is a vast universe in its own right within the field of new technologies. When analysing data, we must first and foremost consider what type of data we are dealing with. This is no trivial matter. Depending on whether we are dealing with structured, unstructured or semi-structured data, we will approach it in one way or another.

In this article, we explain in simple terms the different types of data that exist, what they entail and how they differ in terms of format, technology, analysis and practical applications.

What is structured data?

Structured data is data that is organised into a defined and predictable format. They are generally found in relational databases and spreadsheets, where they are arranged in rows and columns with labels to identify them.

Structured data is ideal for processing, analysing and visualising information in charts because it is easy to read and manipulate. It is usually organised visually into tables, rows and columns, so it is quite easy for the human eye to read.

This structured data is stored in databases relational which organise the information into interrelated tables using primary and foreign keys.

Examples of structured data:

• Relational databases (e.g. MySQL, Oracle).
• Spreadsheets (e.g. Excel).
• Transaction information (e.g. sales, stock levels).

Tools for structured data:

• MySQL
• PostgreSQL
• Oracle Database
• Microsoft SQL Server
• SQLite
• IBM DB2
• Amazon RDS
• Google Cloud SQL

What is unstructured data?

Unstructured data They do not have a predefined structure and can be more difficult to organise and analyse. This data does not follow a fixed format and may consist of text, images, videos, emails, documents, etc.

They are characterised by being more difficult to manage and analyse using traditional tools; they often require specialised technologies such as natural language processing (NLP) or analysis of big data.

Examples of unstructured data:

• Emails.
• Multimedia files (videos, photos).
• Text documents (PDFs, Word files).
• Social media posts.

Tools for unstructured data:

• Hadoop
• MongoDB
• Couchbase
• Elasticsearch
• Apache Cassandra
• Amazon S3
• Google Cloud Storage
• Apache Spark

What is semi-structured data?

Semi-structured data is a type of data that is not organised into a rigid format of tables and columns like structured data, but which, like structured data, contain labels or markers that allow for a degree of organisation and a hierarchical structure that makes it easier to interpret and analyse.

So, even though information is not as easily processed as structured data, we can follow a hierarchical structure to work out how to process it more easily.

Examples of semi-structured data:

• XML (eXtensible Markup Language).
• JSON (JavaScript Object Notation).
• Configuration documents.
• Event logs.

Technical differences between structured and unstructured data

Structured and unstructured data differ significantly in a number of technical aspects, including format, technology, analysis methodologies and applications:

Format

In terms of format, structured data is organised according to a fixed schema, usually in tables comprising rows and columns. Each column has a specific data type, and the relationships between tables are clearly defined using primary and foreign keys.

In contrast, unstructured data does not follow a predefined format. Examples of unstructured data include free-form text, images, videos, audio files and documents.

Technology

From a technological perspective, relational databases such as MySQL, PostgreSQL and Oracle are the predominant tools for storing and managing structured data. These technologies use SQL (Structured Query Language) to define and manipulate data. 

On the other hand, unstructured data requires different technologies such as distributed file systems (e.g. Hadoop), NoSQL databases (e.g. MongoDB, Couchbase) and big data analytics tools (e.g. Apache Spark).

Analysis

Analysing structured data is more straightforward due to its uniform format and the robust tools available. Data analysts can therefore use SQL to run complex queries, generate reports and visualise data with relative ease, with the help of business intelligence (BI) tools such as Tableau and Power BI, and statistical tools such as R and Python. 

By contrast, the analysis of unstructured data is more complicated and generally requires advanced techniques such as natural language processing (NLP) for text, pattern recognition for images and videos, and machine learning algorithms.

Uses

In terms of uses, structured data is ideal for carrying out quick queries. This includes customer relationship management (CRM) systems, enterprise resource planning (ERP) systems and financial applications. 

Unstructured data, on the other hand, is essential in areas where information cannot easily be encapsulated in a tabular format, such as sentiment analysis on social media, multimedia content management, security surveillance through video analysis, and social science research involving the analysis of large volumes of textual data.