Structured Vs Unstructured Data

Structured Data

Highly organised information that adheres to a predefined schema, making it easily searchable and analysable using standard tools like SQL databases and spreadsheets. Examples include customer records, financial transactions, and inventory lists, where data is stored in rows and columns with clear relationships.

Unstructured Data

Lacks a fixed format or organisation, making it more flexible but also more challenging to process and analyse. It encompasses a wide range of content such as text documents, emails, images, videos, and social media posts. Advanced techniques like natural language processing (NLP) and machine learning are often required to extract meaningful insights from unstructured data.

Aspect	Structured Data	Unstructured Data
Definition	Highly organized data with a fixed schema.	Data without a predefined structure or format.
Examples	Databases (SQL), spreadsheets, CSV files.	Text documents, emails, images, videos, social media posts.
Schema	Defined and consistent (tables with rows and columns).	No fixed schema; flexible and varied formats.
Storage	Relational databases, data warehouses.	Data lakes, NoSQL databases, file systems.
Ease of Access	Easily searchable and analyzable using standard queries (e.g., SQL).	Requires advanced processing techniques for analysis.
Scalability	Efficiently handles large volumes due to organized structure.	Managing large volumes can be challenging without proper tools.
Tools & Technologies	SQL, pandas (Python), Excel, RDBMS (e.g., MySQL, PostgreSQL).	Natural Language Processing (NLP) tools, machine learning frameworks (e.g., TensorFlow, PyTorch), NoSQL databases (e.g., MongoDB).

Mining & Analysing Structured Data

Let's work through an example of mining and analysing structured data in order to insights. We will use the table below which contains sale data for widgets A, B, C.

SaleID	Product	Quantity	Price
1	Widget A	10	2.50
2	Widget B	5	5.00
3	Widget A	7	2.50
4	Widget C	3	7.50