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What is Data Normalization? Definition, Function, Purpose and Examples

Understanding Data Normalization

Data normalization is the process of improving the structure of a database so that it is organized so that it can form a good relational structure without repetition.

Data normalization is very important because the function of data normalization is to eliminate repetition and also standardize information so that the flow of data can work better and can also be used to scale data to change its scale to a smaller scale.

4 Purposes of Data Normalization

Here are 4 purposes of data normalization.

  • To Reduce Database Size

When you have repeated data in a database, it will definitely require a lot of storage space. Well, this is where data normalization can reduce database storage space. That way, you will save your data storage space.

  • Simplify Maintenance

Normalization can prevent problems from occurring and can also make database maintenance easier to manage. That way it can save resources and costs.

  • Improve Performance

The database is used as the basis for every application or even software in general. That way the performance of the database will definitely decrease. With data normalization, database performance can be accelerated, so it can speed up the data retrieval process, and also improve the performance of your application.

  • Increase Security

Normalization requires very accurate data placement and is also organized uniformly, so a significant increase in security is needed.

Data Normalization Methods

Data normalization is the process of improving data structures that require several methods in their use. Here are the data normalization methods that you need to know, especially for those of you who want to use data normalization:

  • Z-score method

The Z-score method or can also be called the standard score method. By using this method, each value in the feature is reduced by miu which is the average value of the feature, then divided by sigma which is the standard deviation.

This method will produce a new value between 0 and is usually in the range of -3 and 3 but can be higher or lower.

  • Feature Scaling Method

The Feature Scaling method is used by dividing each value by the maximum value in the feature. Using this method will produce a new value that ranges between 0 and 1.

  • Min-Max Method

The min-max method is used by subtracting each value in the feature from the minimum value of the feature and then dividing it by the range of values. Or by subtracting the maximum value from the minimum value of the feature. This method will also produce a new value between 0 and 1.

  • Formula in Data Normalization

The data normalization formula is a method of arranging data value measurements so that the range is common with other data. This formula is used to process all data sets so that they can be compared with other data sets and can be accessed by everyone who wants to access them.

  • In the data normalization formula, the highest data point will have a value of 1, and the lowest data will have a value of 0. The decimal value of the normalized value will be between 0 and 1. For further explanation of how to normalize data as follows:
  • The first step is to group the minimum and maximum data values, which are symbolized by x = maximum and x = minimum.
  • After that, calculate the range of the data set by subtracting the minimum and maximum values. Range = x (maximum) – x (minimum)
  • After that, select the minimum value of the variable to be normalized from the minimum value, by subtracting the minimum value of the variable. Namely with x – x (minimum)
  • The last step is to calculate the normalization of the variable, you need to use the following formula, x is obtained by dividing the expression in step number 3 by the expression in step number 2, as shown above.

Data Normalization Techniques

For success in normalizing data, several appropriate and appropriate techniques are needed. Here are the data normalization techniques.

1NF or First Normal Form

In the first normal form, there can only be one value in one entry and there cannot be two identical entries.

2 NF or Second Normal Form

The second normal form actually has the same rules as the first normal. It’s just that there is an addition in the process technique, namely each data only has 1 main keyword. This aims to reduce excessive data. 3 NF (Third Normal Form)

The third normal form only ensures that the database table is free from dependency relationships between software components.

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