The Different Types of Data Transformation and When to Use Them

Data transformation is the process of converting data from one format into another. There are many different types of transformation, each with its own purpose and use. In this article, you will learn the different types and when to use them. Keep reading to learn more about data transformation and how it can benefit your business.

Cleansing

This is a data transformation solution that involves cleaning up and standardizing your data. It includes removing any erroneous data, filling in missing values, and standardizing data formats. Data cleansing is essential for ensuring the accuracy of your data analysis. There are a number of different ways to cleanse your data. One common method is to use a data scrubber. A data scrubber is a tool that cleans your data automatically. It can remove erroneous data, fill in missing values, and standardize data formats. Another common method of data cleansing is manual cleansing. This involves cleaning your data manually using a variety of methods, such as data scrubbers, filters, and pivots. Which method you use depends on your data and your needs. If you have a lot of data, a data scrubber is a good way to clean it quickly and easily. However, if you need to be precise in your cleansing, or if your data is messy, then manual cleansing is the better choice. Once your data is clean, you can begin your data analysis.

Normalization

This is the process of transforming your data so that it has a consistent structure. This includes converting data from one format to another, standardizing data values, and creating new columns. There are a few reasons you might want to normalize your data. One reason is to make it easier to work with. Converting data to a standard format makes it easier to compare and analyze. Another reason is to improve the accuracy of your data analysis. Standardizing data values helps to ensure that your data is reliable and consistent. There are a number of different methods you can use to normalize your data. One common method is to use a data normalizer. A data normalizer is a tool that normalizes your data automatically. It can convert data from one format to another, standardize data values, and create new columns.

Aggregation

This is the process of combining data from multiple sources into a single table. This can be done by combining data from multiple spreadsheets, databases, or even text files. Combining data from multiple sources into a single table makes it easier to compare and analyze. Aggregating data from multiple sources helps to ensure that your data is reliable and consistent. A data aggregator is a tool that can combine data from multiple spreadsheets, databases, or text files.

Transformation

Data transformation is the process of transforming your data so that it meets your specific needs. This includes transforming data from one format to another, filtering data, and creating new columns. Transforming data to a specific format makes it easier to compile and analyze your information. You may want to transform data into a format that is compatible with the database or software you are using or pivot data to change the orientation.

Transforming also involves combining data from multiple data sources into a single table or dataset, filtering data to remove unwanted values or rows, and sorting data to change the order of the values in a column.

There are different types of data transformation that can be used to improve the accuracy and completeness of data. These transformations should be used together to create a complete and accurate data set.