Meaning and Objectives of Classification of Data

What is meant by the Classification of Data?

Classification of data refers to organizing data into various relevant categories for simplicity and ease while using them. In economics and statistics, one has to often come across data categories that have their own properties. When data is organized depending on the properties of these categories, it is called data classification. Data classification helps in analysis and research.

Data classification helps to find out and retrieve data quickly. It is useful in the sense of security, risk management, and compliance with various entities. Classification of data also means tagging it so that it can be easily tracked. It also removes duplicate data which frees up space and helps in reducing storage space, making search accelerated, and keeping backup costs under control.

In other words, classification of data means categorizing data into homogenous groups depending on their common properties. It is important because raw data is of no use unless it is presented in an organized manner. Data classification does the organization of data by putting data into relevant categories, making it easier for people to comprehend and use the data.

Example of Data Classification:

Let us take an example of data classification of welding methods. The below figure shows the classification of welding methods for semi-finished polymeric materials.

Some of the most common and significant methods of data classification are –

• Natural Breaks

• Maximum breaks

• Quantile

• Standard deviation

• Manual interval

• Defined interval

• Equal Interval

• Geometrical interval, etc.

Features of a Good Data Classification

It is easy to classify data into groups. However, for a classification to be excellent, the following characteristics must be met −

• The primary characteristic of an impressive classification is that it must be comprehensive. It should contain all the data segregated and defined in appropriate data groups.

• Most data sets are of high volume in nature and so they lack clarity. It is the purpose of good classification to bring the needed clarity so that users of the data can navigate smoothly on it.

• Data sets are often scattered and unusable as the volume of data is large and there is no common attribute that holds them in one place. A good data classification should bring all similar or homogenous data into one place. Homogeneity is a very important part of data classification.

• An impressive data classification must have elasticity so that when needs and purposes change, it can be used for the new need and purpose.

Objectives of Classification of Data

Data classification is not done without having any objective. The main objectives include the following −

• Simplifying data − Classification of data makes data simple and concise. This in turn helps to understand and analyze data for various uses.

• Bringing out uniqueness − Data classification leads to the classification of data into various categories. This helps to check the uniqueness of data that are present in a data set.

• Improving utility − Like bringing out uniqueness, data classification also brings out the similarities among various data groups. This enhances the utility of data.

• Helping in comparison − The accumulation of data into various groups helps one to compare various data in the classification process. Comparison is one of the most useful outcomes of data classification.

• Increasing reliability − Data classification is a scientific process that has proven reliability. Therefore, it is considered to be a reliable source of information that can be reliably used for better outcomes in statistical and economic processes.

• Consolidation − By classification of data, a large group of data can be consolidated into smaller groups so that required data sets can be easily obtained while conducting research or performing analysis. This helps in differentiating data into parts depending on common characteristics.

• Prioritizing − Data classification helps in categorizing the useful and prioritized data sets while segregating the unnecessary ones. This helps in keeping the useful data in hand while ignoring the unnecessary ones.

• Making data presentation attractive − Data classification helps in making the presentation of data in an attractive and useful manner which is one of its main objectives.

• Statistical Analysis − Data classification is one of the most used and convenient methods to analyze statistical information. It has a universal value and can be a tool of much importance when used effectively.

Types of Classification of Data

There are mainly three types of classification of data. They are -

• Content-based data classification is classified considering the sensitivity of the information it has.

• Context-based classification deals with segregating data based on its application, location, and its creator. This may be associated with other factors like features of the information and indirect indicators.

• User-based classification is based on the decision of users about how they can tag each piece of data. It is a completely manual process.

Methods of Data Classification

Geographical classification

This type of classification is done depending on the geography of data, such as countries and states, etc. It concerns data of a defined geographical region and is also called spatial classification.

Time-based classification

Also known as temporal classification, this type of data classification is made spending on the time of occurrence. Data is classified in descending or ascending order spending on time, such as years, months, etc.

Qualitative classification

In this type of classification, the data are grouped depending on the qualities of the subjects. This may include honesty, beauty, marital status, etc. For example, a classification of data may be made for married and unmarried people of a location.

Quantitative Classification

Unlike qualitative classification, quantitative classification is based on countable factors, such as weight, height, grades of students, etc.

Note − Classification of data is a vast area of economics where there are many types and manners of classification. However, it is usually observed that studies under one subject follow a certain type of data classification. This is done to bring more homogeneity to the subject and has no relation with any other noticeable factor. However, the classification made by someone should be universal and as error-free as possible so that it can be used universally.

Conclusion

Classification of data must be learned with utmost caution because any error in data classification may lead to erroneous results in various economic and statistical calculations. Moreover, as data classification helps in summarizing data sets, one should take enough care the data included in certain groups are relevant and useful. Just engaging in data classification is not enough. Being careful is a must too.

FAQs

Qns 1. What is meant by data classification?

Ans. Classification of data refers to organizing data into various relevant categories for simplicity and ease while using them. In economics and statistics, one has to often come across data categories that have their own properties. When data is organized depending on the properties of these categories, it is called data classification.

Qns 2. Give any three objectives of data classification.

Ans. Simplifying data presentation, increasing the utility of data, and helping in statistical analysis are three objectives of data classification.

Qns 3. What are the three types of data classification?

Ans. Three types of data classification are content-based, context-based, and user-based data classifications.