Types of Data

Till now we have studied what is data and sources of data. Now lets us dive into various types of data we would encounter in real-time:

1. Quantitative and Qualitative Data
2. Discrete and Continuous Data
3. Structured and Unstructured Data

1) Quantitative Data and Qualitative Data:

Data can be quantitative or qualitative based on the information provided.

• Quantitative data refers to data with numeric information and figures.
• Qualitative data refers to data with descriptive information.

Let us take a simple example to understand qualitative/quantitative data and answer some of the questions.

Consider a basket that has some apples.  

Quantitative data for the same scenario will be:

• number of apples in the basket 
• total weight of all apples in kilograms

Qualitative data for the same scenario will be: 

• taste of the apple, whether apples are sweet, raw, etc 
• size of the apples, whether apples are small, medium, or big 
• color of apples like red, green, etc 

Quantitative is something which you can measure, count, or weigh, but Qualitative is something that cannot measure, count, or weigh, you need to use your senses to identify it either through taste, touch, smell, hear or through observation, etc.

2) Discrete and Continuous Data:

Discrete data can take only certain values (only whole numbers).

Examples:

1. No of students in a class: Count is always going to be a certain whole number like 20, 100, 200, etc. We cannot have 10.5 or 100.05 students. 
2. Results of flipping a coin: Outcome is always going to be one from the possible 2 values (Head, Tail)

Continuous data can take any values within a certain range.

Examples:

1. Height of a person: could be any possible value within a range of human heights, not always necessary to be a whole number or fixed values. It can be 5 feet or 5.6 feet.
2. Temperature
3. Weight of a person

3) Structured and Unstructured Data:

3.1 Structured Data

Any type of data that can be put in a tabular form having rows and columns is considered structured data. Such data can be accessed and stored in relational databases. 

Such kind of data:

1. is highly organized and formatted
2. Includes mostly quantitative data
3. is easy for manipulation, updation when working with relational databases

Examples:

• Dates
• Phone numbers 
• Pin codes 
• Customer names 
• Bank Balance 
• Credit Score

3.2 Unstructured Data

These types of data are mostly qualitative data, and cannot be easily accessed or collected via conventional methods and tools. 

Such kind of data:

1. is not organized, most of the data remain in raw format
2. More than 80 percent of all the data generated till today is considered unstructured, and this number will continue to rise with the prominence of the internet of things.
3. Can be stored in non-relational databases like NoSQL

Examples:

• Text data: Logs, Powerpoint Presentations, Blogs, etc 
• Emails 
• Social Media: Data from Facebook, Twitter, Linkedin, etc 
• Websites 
• Mobile data and communications: Whatsapp, SMS, Contacts, Apps, etc 
• Media: Images, Music, Videos, Audios, etc 

Note: In this rapidly expanding technological world, using data has become a necessity to run and grow any business. A simple saying is there “if you are not going to use data for our business in the future, you are sure to become a business of the past”