Difference between Structured, Semi-structured and Unstructured data

Data plays a crucial role in understanding the business trends. Many organizations generate and process huge volumes of data. This huge and complex data is referred to as "Big Data". Big data is of three types: structured data, semi structured data, and unstructured data.

What is Structured Data?

Structured data is generally stored in tables in the form of rows and columns. Structured data in these tables can form relations with another tables. Humans and machines can easily retrieve information from structured data. This data is meaningful and is used to develop data models.

• Structured data is used by many business organizations. Companies apply data visualization techniques on the structured data to extract some meaningful insights from that data and develop data models. Machine learning algorithms are applied on this data so that they can predict the future outcomes based on this.

• Data present in a Relational Database is the best example for structured data and this data can be accessed using a structured query language (SQL).

• Structured data is highly secured and requires low storage space. About 20% of the data is structured. Tools used on structured data are MySQL, PostgreSQL, SQLite, etc.

Following are the advantages of maintaining structured data:

• It is easy to search for data

• Less storage space is required

• More data analytics tools can be used

• Data is highly secured

And, listed below are the disadvantages of keeping the data in a structured manner:

• Data is not flexible

• Its storage options are limited

What is Unstructured Data?

Unprocessed and unorganized data is known as unstructured data. This type of data has no meaning and is not used to develop data models. Unstructured data may be text, images, audio, videos, reviews, satellite images, etc. Almost 80% of the data in this world is in the form of unstructured data.

• Unstructured data needs a lots of storage space. Here, data is not secured. It is difficult to search this data as it is not organized properly. This data is stored in NoSQL databases as they can’t be managed using relational databases. It is very difficult to get insights from this data.

• Text files, Emails, data from social media applications, IoT, media etc., are examples of human generated unstructured data. Satellite images, scientific data etc., are examples of machine generated unstructured data.

• Tools used on unstructured data are MongoDB, Hadoop, DynamoDB, Azure, etc. Data visualization is best for analyzing unstructured data as they show hidden meaning of that data.

Following are the advantages of using unstructured data:

• Data is flexible.

• This data can be used for a wide range of purposes as it is in its original form.

The disadvantages of using unstructured data are as follows:

• It requires more storage space.

• There is no security for data.

• Searching for data is a difficult process.

• There are limited tools available to analyze this data.

What is Semi-Structured Data?

Semi structured data is organized up to some extent only and the rest is unstructured. Hence, the level of organizing is less than that of Structured Data and higher than that of Unstructured Data.

• Semi-structured data is partially organized by means of XML/RDF.

• In semi-structured data, transaction management is not by default but is get adapted from DBMS, however there is no data concurrency.

• Data versioning is done only where tuples or graph is possible because semi structured data supports partial database.

• Semi-structured data is more flexible than structured data but less flexible and scalable as compared to unstructured data.

• If there is semi-structured data, then we can query only anonymous nodes, so its performance is lower than structured data but more than that of unstructured data.

Differences: Structured Data and Unstructured Data

The following table highlights the major differences between Structured and Unstructured data:

Structured Data	Unstructured Data
Structured data is processed and organized.	Unstructured data is not processed and unorganized.
Data is stored in the form of tables.	Data is stored in the form of text, images etc.,
Structured data is managed using Relational database management system (RDBMS)	Unstructured data is managed using NoSQL
Data is highly secured.	Data is not secured.
Data models can be developed from structured data	We can’t develop data models using unstructured data.
This data is stored in Data warehouses and Data lakes. It requires less storage space.	Unstructured data can be stored only in Data lakes. More storage is required to store this type of data.
Structured data is quantitative data	Unstructured data is qualitative data
Analytical methods used are: Classification Regression Clustering	Analytical methods used here are: Data stacking and Data mining
Searching is easy in this data	It is difficult to search as the data is not organized
Around 20% of the data is in structured form.	About 80% of the data is in unstructured form
As storage required is less, structured data is highly scalable	It is not scalable as it needs more storage
Data is not flexibleData is not flexible	Data is flexible
Example − Names, contact details, etc., are examples of structured data. Excel spreadsheets, Google sheets, relational databases contain structured data.	Example − Social media reviews, satellite images, polling results, etc., are examples of unstructured data. Unstructured data is stored in non relational database management systems.

Conclusion

Most of the data present in the world is unstructured. Despite its disadvantages over the structured data which is well organized, unstructured data helps organizations and companies to understand customers and users better through reviews, polling, etc. This helps companies to analyze and understand the interests and buying habits of customers, their mindsets etc., so that they improve their product or services further.

Structured data is readily useful to make data models and helps organizations to understand the trends in that data and take necessary actions based on that.