You are not alone in case you have ever had trouble with messy spreadsheets or databases with duplicate information. The answer to this is data normalization which can turn the messy data into something more systematic, clear and even useful.

What is Data Normalization?

The process of grouping data in a database in order to minimize redundancy and enhance the integrity of data is known as data normalization. Imagine that it is clearing your computer files. Rather than having the same information being stored in various locations in a number of instances, normalization can be used to make sure that that particular piece of information is stored only once and that it is accurately related to any other information.

What Is the Importance of Normalization?

There are a number of issues when the data is not normalized. Redundancy to records occupies storage space, updates are made complex as well as errors are increased exponentially. Normalised data, however, is homogenous, valid and far simpler to maintain and analyse.

Strategic (Normalization) Techniques

Normalization has a number of forms, each of which extends the former one:

First Normal Form (1NF) does not allow any repetition of groups within a particular field and only one value. To say the least, rather than having several products in a cell, the products are assigned rows.

Second Normal Form (2NF) goes a step further to ensure that all the data is completely dependent on the primary key and therefore there is no partial dependence.

Third Normal Form (3NF) eliminates transitive dependencies, i.e. non-key fields should not be dependent on other non-key fields.

Pragmatic Statistical Techniques

In addition to the structural normalization, statistical methods are used to standardize numerical data:

• Min-Max Normalization alters the value to be within a fixed range of values usually 0 to 1.

• Z-Score Normalization is an adjustment of values according to how they differ in relation to the mean value.

• The Decimal Scaling changes the decimal points in order to bring the numbers into a smaller range.

Actual Advantages You Will Have

Normalized data presents better faster query capabilities, space saving, fewer errors and much easier database maintenance. Regardless of what type of information you are dealing with, be it customer information, inventory data or analytics you will find that normalization will make your data work in your favor rather than against you.

In the situation when you have to work with big data and require a stable data handling and recovery tool, DataRecovee provides the whole package of solutions aimed at ensuring data integrity and restoring the essential information in the most efficient way possible.