Structured Data vs Unstructured Data vs Semi-Structured Data-What is the difference?
Let's deep dive into all the three classifications to learn more about them
To begin with, let me first all that data is all about?
In computing parlance, data is referred to as a piece of information that can be processed in a meaningful way.
Due to the technological revolution in the past decade, the internet generates an enormous amount of data. As per IBM, the world will collectively generate around 175 zettabytes (175 trillion gigabytes) of new data
Data exists in different forms, sizes, and formats. Think of images, social media videos, and news online- All of this is, of course, data!
Data can be segregated into 3 categories:
- Structured Data
- Unstructured Data
- Semi-Structured Data
Let’s dive in and check the three categories in depth
Structured Data:
Structured data is highly organized and easy to understand. It is also referred to as quantitative data as it can be measured. It is stored in rows and columns and is related to each other. Think of data stored in Microsoft Excel and Google Docs-That’s a perfect example of structured data
Structured data requires little to no formatting and is very easy to decipher the context. The raw data is mapped is stored in pre-designated fields and can be extracted using SQL(Structured Query Language) with ease. The data resides in form of a Relational Database.
Advantages of Structured Data
#1 Easy to Understand:
Since the data is stored in a formatted way, it becomes very easy for the decision-makers to easily decipher the data and make better business-related decisions.
#2 Compatible with Machine Learning Models
Structured data has a specific structure and format which makes it easier to feed into Machine Learning models and generate the desired output.
Disadvantages of Unstructured Data:
#1 Lack of flexibility:
Structured data can be only used for limited analysis. It is not scalable or flexible.
#2 Limited Storage Options:
Structured data is stored in Data Warehouse with a predefined structure. Any change in the data will require updating all the other data in the same format, leading to higher expenses.
Industries where structured data is Used:
#1 Online Booking:
Whenever you book a hotel room or make a flight reservation, the database stores the relevant data- Like Name, ID, Date, Price, Tariff, Destination) in a predefined format.
# 2 Accounting:
Accounting software leverage predefined structures in the database to store all the financial transactions.
Unstructured Data:
Unstructured data also referred to as Qualitative Data, is a category of data that does not have a predefined structure and cannot be processed or analyzed with the help of conventional methods or tools. The data is nonrelational and cannot be comprehended easily.
Images, Pdf, Word, Media logs, Videos, Text messages, social media posts, or IoT sensor data are some of the examples of unstructured data.
Unstructured data is categorical and characteristic. For example, Social media posts can be analyzed to understand the purchasing trend or monitor consumer behavior. Text and videos can be monitored to catch fake news.
Unstructured data is stored in Data Lakes and is preserved in a raw format for further processing.
Advantages of Unstructured Data:
#1 Flexible and Scalable:
Unstructured data can be stored in raw native format and is not dependent on schema, which makes it easy to store massive amounts of data in Data Lakes with ease.
Disadvantages of Unstructured Data
#1 Specialised Skills:
As the data is stored in a raw format, it requires specialized skills to format and analyze the data.
# 2 Sophisticated Tools:
Sophisticated tools are required to extract and process the unstructured data, which makes many companies abandon the data due to the additional processes involved.
Usage of Unstructured Data
#1 Predictive Analytics:
Unstructured data can help businesses to process the data and understand the sales pattern and accordingly adjust the manufacturing and supply chain to maximize the sales.
#2 Data Mining:
Enables businesses to analyze the unstructured data to understand consumer behavior, current trends to help serve customers in a personalized way
Semi-Structured Data:
Semi Structure data lie somewhere between the structured and the unstructured data segment. Although it does not have a predefined structure and is more complex, it is relatively simpler to understand than unstructured data.
Emails. For example, are a good example of semi-structured data. It has different aspects like Sender details, recipient, and dates or consider tweets mapped with hashtags. Even news headlines can be considered a semi-structured data
Data is stored in different formats like CSV, XML, and JSON
Advantages of Semi-Structured Data:
# 1 Flexible:
Semi-structured data does not have a rigid architecture. A NoSQL database can store an enormous amount of data in any format to be processed later on.
Disadvantages of Semi-Structured Data
# 1 High Storage Costs.
Although semi-structured data is easier to store and port compared to unstructured data, it involves a much higher cost than storing structured data.
Summary:
To sum it all, please refer to the table below to understand the difference between the three categories
