What is Data Transformation? Definition, Types and Benefits
Tags: data, dataanalytics
Published: 2024-03-13 12:03:18

What is Data Transformation? Definition, Types and Benefits



The insights gained from the raw data accessible within the firm are one of many factors that contribute to the ever-changing nature of business operations in today's environment.

With this knowledge, you can handle company crises with grace, make educated decisions, and keep stakeholders informed by tailoring your reports to their specific needs. Companies that are focused on innovation utilize these insights to explore new boundaries and operate at different levels.

The complexity and amount of data are both increasing at an exponential rate, making data maintenance an increasingly daunting task. Although there is no one correct method, there are several recommended procedures for laying the groundwork that will allow for the extraction of these insights. Because of this, the architecture is made to be both scalable and flexible enough to handle changes that are planned for in the organization's future. However, this technique does entail some basic stages.

What is Data Transformation?

The term "data transformation" describes the procedure of changing the format or organization of data. The transformation layer is where this takes place. Data transformation is an essential part of data cleansing and integration processes. In order to complete the list of sources and the sorts of data they provide, the raw data is examined. Afterwards, the data is transformed into the desired format or structure. Subsequently, fields are individually mapped, adjusted, combined, filtered, and aggregated.

In most cases, data is altered in order to improve its organization. Applications are safeguarded against possible failures including undesired null values, unexpected duplicates, and incompatible formats when data is structured, formatted, and verified.

Data Cleansing

Eliminating superfluous or irrelevant data records is known as data cleansing.

Here are the steps involved in data cleansing:

Step 1:First, using the primary keys that were defined for the source data tables, remove any duplicate items.
Step 2: Addressing the structural issues that have been discussed or are considered conventional practices, such as eliminating or adding padding, following and adhering to name rules, and fixing items that use lowercase letters.
Step 3: Applying Global Filters and Aggregations in Scope: The data is subjected to the different functions according to the fields defined in the area. Finding the data outliers is possible with this step.
Step 4: Dealing with Missing Data, Blanks, and Date Formats: At this stage, standard data formats are followed, symbols are replaced with standard functions, and blank records are filled up to assure accurate input.
 

The list of source systems and data sources, as well as the system connection, follow. Once the connection is established, the data is transformed and loaded into the structured targets. Businesspeople often hear the acronym "ETL" (Extract-Transform-Loading) used to describe a certain procedure.

There are a plethora of online and offline tools available to assist with the change, and scripting makes it easy to accomplish the task rapidly. In the end, we make sure the data is correct and precise.

The following are examples of common transformations used by programmers: Making use of joining, filtering, data deduplication, aggregation, Data is sometimes binned for use in histogram displays and normalized or denormalized depending on output needs. The data is subjected to a number of formatting and scaling operations.


Read also: Mastering the Art of Software Testing: A Path to Perfection


What is the Function of Data Transformation?
The process of data transformation is useful when you need to change the format of your current data so that it is compatible with the format of the system you are transferring it to. There are two locations where this occurs. In on-premises storage configurations, it is performed during the 'transform' stage of the extract, transform, load (ETL) process. Also, ETL isn't necessary in highly scalable cloud systems; instead, data may be transformed during upload using a procedure called extract, load, and transform (ELT). Data transformation can be done manually, automatically, or in a combination of the two in this second technique.


Steps in Transforming Data

The steps involved in transforming the data appear like this: 

1. The Finding
The discovery phase is the starting point for data transformation. Data analysts and engineers determine the necessity of transformation at this level. This may occur if the data is not in the right format, comes from several sources, or has to be ready for a particular study or report.

2. Creating diagrams
The process of developing a mapping strategy follows the identification of the need for change. Specifically, you'll need to tell the destination format how to map or transform data items from the source. Among the many possible mapping operations are column renaming, data merging, data splitting, and value aggregation.

3. Generating Code
Code for data transformation must be developed following mapping definition. Writing SQL queries, Python or R scripts, or utilizing specialist ETL (Extract, Transform, Load) tools may be necessary, depending on the complexity of the transformation. The transformations that have been defined are to be executed by this code.

4. Performed
Data transformation is carried out after the transformation code is installed. The mapping plan dictates how the transformations are executed when the source data is imported. Depending on the scenario, this phase might happen in real-time or as part of batch processing.

5. Evaluate
Before using the converted data, make sure it satisfies all quality requirements and achieves the desired results. Verifying data integrity, correctness, and completeness is part of this process. During this stage, problems with data quality might be found and fixed.

Conclusion
It is a data-driven world in which we find ourselves. According to studies, global data creation will reach 181 zettabytes by 2025. To put that in perspective, 1 zettabyte is equal to 1 trillion gigabytes. You can learn more about data transformation by looking for a [data science training program in Ghaziabad](https://uncodemy.com/course/data-science-training-course-in-ghaziabad).

Harnessing data's potential is essential for any company that wants to succeed in the digital era. But it's easy to make mistakes and get caught up in poor data when there's so much data available everywhere. Data transformation becomes relevant in this context. To help you avoid mistakes and get reliable insights, it prepares the data that is already accessible for display and analysis.