Data organization is a process of organizing raw data, by classifying them into different categories. This raw data includes the observations of variables. For example, arranging the marks obtained by students in different subjects is data organization.
As time passes and our volume of data increases, the time consumed to search for any information from the data source increases if it hasn’t been organized already. Let us consider the following example to understand the concept and need for data organization.
What is Data Organization?
Data organization is the way to arrange the raw data in an understandable order. Organizing data include classification, frequency distribution table, picture representation, graphical representation, etc.
Data organization helps us to arrange the data in order that we can easily read and work. It is difficult to work or do any analyses on raw data. Hence, we need to organize the data to represent them in a proper way. Let us understand with the help of an example.
Example: The marks scored out of 50 in a maths exam taken by 25 students are as follows:
26,15, 40, 18, 26, 24, 48, 40, 39, 26, 23, 37, 38, 40, 45, 48.
This form of representation of data becomes confusing if the number of students increases to 1000. Now, this data can be organized in the form of a table as shown below:
|Serial Number||Marks Scored|
This form of representation is easy to interpret and to analyse the data, in this case, is organized. Data organization may initially take some time, but in the long run, you will understand the worth of time spent. Once the data is organized properly as per the requirement, it can help us to gather the required information in a quick span of time in the future.
Need for Data Organization
There are a lot of benefits to organizing data. The first and foremost benefit are it decreases the time-consuming of searching for data. Disorganized data has many bottlenecks in terms of data structuring. Suppose you have data on the results of 1000 students in a school, and you need to find out how many students scored a percentage greater than 90.
If your data is unorganized, it will take a lot of time and resources to gather the required information, but suppose you have organized the data in descending order of percentages, and then it will be very quick and easy to sort out the required information. Organizing data also helps in reducing data loss and reduces errors. Suppose you have confusion in different sets of data, then the only solution to such problems is to organize the data properly.
Data organization also helps you to understand why the data was collected and what the proper use of it is? Once the data is organized, it gives you the validity of the work undertaken. A sequential view of the data is always accepted as compared to abrupt and disorganized view. Data organization can be of various types, depending on the requirement of the user. Sometimes, the repeated values in the data are collected together to know the mode of the data or sometimes the data is organized in increasing or decreasing order, to find the median of the given set of data.
Classification of Data
Classification of data brings order to raw data. We can classify a bulk of data based on their need or purpose. The different types of data, based on which they are organized are given below:
- Chronological data
- Spatial data
- Qualitative data
- Quantitative data
Chronological data are grouped or classified according to the time, such as days, weeks, months, and years. For example, the growth of population with time in years.
Spatial data are classified based on geographical locations or areas such as cities, states, countries, etc.
Qualitative data are categorized under different attributes like nationality, gender, religion, marital status, etc. Such data cannot be measured but can be classified based on their presence and absence of qualitative characteristics. For example, categorising the population of males and females in a city.
Quantitive data is the type of data when the above attributes (in the case of qualitative classification) are further categorised into number-based data such as height, age, marks of students, salary, etc.
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