Organization of Data

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Data is an invaluable part of statistics and plays a vital role in economics. However, raw data is not usable, and to make it meaningful, it needs to be organized. Data organization is therefore a primary and unavoidable function for statistical and economic insights.

Organization of Data: Definition

Organization of data refers to classifying and organizing data to make it more meaningful and usable. In an organization process, data is ranked in the most logical and orderly fashion so that anyone who seeks to use the data for whatever reason can get it readily available.

It is easy to see why the organization is important. Organization of data offers insights to take actionable steps that help analysts and researchers understand the population and demography better. Organization or classification of data is done in such a manner that the extractor of data gets easy access to all of the data in a simple and easy manner. So, it helps the researchers save time and resources while looking for actionable insights.

Organization of data can also be termed as categorizing raw data into simple forms. In an organization process, therefore, researchers must follow some rules to avoid misappropriation and misuse of data.

Classification: Definition

Classification is the process of classifying or categorizing data depending on their resemblances and affinities. Classification leads to the unity of attributes that may be available in a diversity of individuals.

  • Classification is a part of organizing data and it helps researchers in finding the matches and mismatches of attributes among the populations under study.

  • Classification helps in gathering entities with similar attributes as the entities are grouped according to their qualities that may be fixed according to research objectives.

  • Classification is a must for raw data to be grouped into actionable forms. Without classification, raw data will be of no use and it will require lots of hardship to find the required patterns for a study.

  • Classification removes these hardships in finding useful data from a set of received raw data patterns in a study.

  • Classification of data is usually done according to the needs of the study. The categories in a classification process must be classified in a meaningful sense so that they can be used without having to reach erroneous outcomes. That is why neat classification is so important in the statistical process of data organization.

Objectives of Classification

The objective of data classification can be discussed under the following categories:

  • Simplification and briefness: Classification helps make raw data simple to understand and use. It helps data to be presented in a brief manner. So, researchers and analysts can get simplified and brief data after classification has been performed.

  • Utility: Classified data can be utilized easily and more readily than raw data. Classification helps to make data more utilizable and meaningful for use.

  • Distinctiveness: Classification also makes data more distinct. As the categories are strictly defined, one can find the required data in a distinct form. Distinctiveness is one of the reasons why the classification of data is so popular and common.

  • Comparability: Classification of data makes it more comparable within its own limits or with another set of external data. This helps researchers check whether the data is reasonable and useful for a particular study.

  • Reliable arrangement: Classification helps to arrange data that can be accessed reliably by one and all. Users of data can use the data without having to go through a mess as it is already categorized in the required form. Moreover, as the arrangement is performed in a scientific way, it helps to derive the required data without any error.

  • Attractiveness and effectiveness: Classification of data makes it more attractive and effective for use. Therefore, working with classified data is more reliable and less time-consuming which increases its effectiveness.

Characteristics of a Good Classification

To be effective and usable in nature, classifications must have some important attributes.

Some of these attributes are the following:

  • Comprehensiveness: To be considered as good, a classification must be comprehensive in nature. It must include all data and categorize them in a reliable and usable manner.

  • Clarity: Clarity is another condition of classification to be good and useful. In a good classification, data must be in a clear and concise form. Without clarity, classifications may be confusing. That is why classification must be done with simplicity and clarity in mind.

  • Homogeneity: Godo classification must be homogenous too. Without homogeneity, achieving simplicity and clarity may be hard. Homogeneity is therefore important because raw data needs to be presented in an evenly distributed form.

  • Stability: For a classification to be enduring, it must be stable for the long term. Too many changes in data may make the presentation unstable and so attention must be paid to make data classification stable.

  • Elasticity: Good classifications should also be elastic in nature so that they can be used for a variety of purposes. As elasticity makes data classification usable for various purposes under many schools of study, it is an unavoidable quality of classification.

Basis of Classification

Classification may be based on the following attributes:

Geographical Classification

In geographical distribution, geography or the locations under study are the bases. For example, a data classification of populations of various states of India may be considered a geographical classification.

Chronological Classification

This type of classification of data is based on time differences. For example, the net income of a company for the last five years can be considered a chronological classification.

Qualitative Classification

This type of classification is based on the quality or attributes of the classification.For example, the classification of malnourished children in a region can be considered qualitative classification. Qualitative classification can be of two types - simple and manifold classifications.

Quantitative or numerical Classification

Quantitative classifications are related to numerical data. It is also called classification by variables.

Examples may include the amount of rice consumed by people in a range of regions.


It is impossible to make inferences from a lot of raw data and hence classification of data is important. Organizing data in presentable forms is necessary for a correct judgment of variables and economic situations. That is why the organization of data is so important.

Organization of data is also applicable to a detailed study of economics under various circumstances. Although the organization of data is a subject of statistics, it is used comprehensively in economics too. As data is unavoidable for a correct inference of economic situations, its use in an organized form is also important. That’s the reason why economists organize data for correct inference and to have a better view of the studies under consideration.


Q1. What are some rules of thumb for organizing data?

Ans. Some rules of thumb for organizing data are -

  • Using clear and concise names of files.
  • Keeping titles short.
  • Using consistent file version management.
  • Create and use a data dictionary to make categories standard.

Q2. What is the ultimate objective of data organization?

Ans. The ultimate objective of data organization is to organize data so that it can be found, used, and extracted easily and quickly. Successful data organization means that less time and resources are put into use to get the data as and when required.

Q3. For whom is data organization necessary?

Ans. Everyone dealing with data needs data organization. People who need data organization include economists, market researchers, scientists, corporate members, etc.

Updated on 13-Oct-2022 11:19:47