Classification of Data

Classification of Data means organizing data into groups as per their characteristics. As it is hard to interpret raw or unstructured data, classification becomes essential. The purposes behind the classification of data are listed below:

• Comparing data 
• To facilitate easy analysis and for measuring data. 
• To group data as per their nature and properties.
• To distinguish data as per their characteristics. 
• To simplify complex data.

Types of Data Classification Methods 

Data is clear, homogenous, comprehensive, elastic, suitable, and stable when classified correctly. Data can be classified according to its properties, geography, and various other factors. The types of data classification methods are described below: 

1. Geographical Classification

When data is classified according to the dissimilarities in its location or geography, it is known as the geographical classification of data. Geographical limits or boundaries like city, village, district, taluka, state, country, etc., are considered while classifying data geographically. 

For example, while calculating the number of senior citizens in all the states of India, we will consider the people who are above 60 years old. However, as the comparison is made statewide, it can be considered as a form of geographical classification. 

2. Qualitative Classification

When data is classified according to its properties or qualities, it is referred to as qualitative classification. Qualitative classification can be further segregated into two types — simple classification and manifold classification.

• Simple Qualitative Classification –  Simple Qualitative Classification involves classifying the data into two groups. One group reflects specific properties or characteristics, and the other one doesn’t reflect those qualities. Therefore, we can say that only one quality or attribute is considered in this type of qualitative classification. 

For example, while finding the people who have the O+ve blood group from a group of people, we will only bring the people who have the O+ve blood group in one group. The remaining people will be grouped into another group. Therefore, it is an example of Simple Qualitative Classification. 

• Manifold Qualitative Classification – When more than one quality or attribute is considered while classifying data, it gets further divided into different classes and subclasses. If the number of attributes is more, classes and subclasses also increase. Such a classification is referred to as Manifold Qualitative Classification. For example, 

For example, multiple factors like age, sex, experience, qualification, etc., can be considered while choosing candidates for a job. Therefore, it becomes an example of Manifold Qualitative Classification. 

3. Chronological Classification

When data is classified with respect to time, it is called Chronological Classification. For instance, while determining a company’s growth in terms of profit from 2000 to 2021, we will figure out the profit as per the specific years. Therefore, it is an example of Chronological Classification.  

4. Quantitative Classification

When data is classified according to the numerical values, it is known as Quantitative or Numerical Classification. It also includes the data classification that is done by variables. The variables may be discrete or continuous in nature. 

For instance, while classifying people based on their height, weight, or both, you perform a Quantitative Classification. 

Database and Data Science 

After classifying data following any of the above-mentioned methods, it is usually stored in a database. To understand database meaning, remember that it is a collection of data that is organized on the basis of its characteristics. The data is structured while storing it in a database that is usually done electronically on a laptop, mobile, or computer. The database does not refer only to the equipment or device used for storing the data but also includes the technology or program used for storing it. 

Data science refers to the study of data by using statistical tools. A person must have knowledge about statistics, mathematics, and domain expertise to be a data scientist. Also, programming and data analytical skills are required for analyzing and interpreting the data. 

Data science is closely related to the classification of data as it uses various data analytical tools to interpret raw or unstructured data. After classification of the raw data by using different techniques suggested while studying data science, one needs to store it in a database for future use. 

Why is a classification of data necessary?

Classification of data is necessary because it enables governments and organizations to structure raw data. Governments use structured data to create public welfare programs and strategies. Organizations utilize structured data to create strategies and risk management plans. 

Brands use the data to promote their product and services through social media channels and the internet. MNCs use the data obtained from digital mediums to improve their products and services. Overall, data has become essential nowadays. However, it would be difficult to understand the insights hidden in the data without proper classification. 

Through the classification of data, raw data is converted into statistical series. Statistical series means the logical or orderly flow of data. The different types of statistical series are described below:

Individual series: A series in which items are sorted in a singular fashion is called an individual series. For example, the daily wages of different workers of a company is an individual series. 

Discrete series: A series in which each item is associated with multiple values is called a discrete or frequency series. The frequency, i.e., the number of items associated with a particular item, plays an important role in this type of series. 

For example, the number of family members in all the houses in a given area is a discrete series as the number of family members can be the same in several houses. 

Continuous series: Items do not have a specific value in a continuous series, but their values are expressed as a range. The number of diabetic patients in different age groups is an example of continuous series. 

Conclusion 

Classification of data is essential to remove the complexity and heterogeneous components from it. Data classification is a core fundamental component of any security program. It is the framework for how IT security is weaved into information security and ensures the protection of your business’s most sensitive information