Data Engineering

Data Transformation And Its Benefits

4 minutes read

Modified on July 29, 2022

eCommerce

In today’s world, continuously evolving business processes depend on many things, including the insights that are derived from the raw data available within the organization.

These insights help make informed decisions, deal with business crises, and ensure stakeholders are well informed segregating information at each individual’s need for reporting. Innovation-driven companies use these insights to open and operate at various horizons.

As the size of the data is growing every minute, the complexity of the data also increases with it, making it more and more challenging to maintain it. There are many best practices of setting up the infrastructure to enable the extraction of these insights, but there is no right or wrong way of doing it. Hence, the architecture is designed in a way that it is scalable and flexible enough to accommodate changes foreseeing the organization’s roadmap. But there are some fundamental steps involved in this process.

Elemental Steps Involved In Setting up the Infrastructure

Gathering requirements and planning contextual scope for the organization, also called as Business Planning Layer.
Defining and building the data model structure and data exploration, also called Modeling Layer for Analytics.
Identification of data and mapping with raw data is also called a Transformation Layer.
Accordingly, the chosen platform is chosen, also called a Technology Layer.

What is Data Transformation

Data Transformation is a process in which data is converted from one form or structure into another. This happens in the transformation layer. In the process of data integration and data cleansing, data transformation plays a vital role. The raw data is analyzed to finalize the list of source and their data types. Then the structure is put together where the data will be converted into the expected format or structure, and then individual fields are mapped, modified, joined, filtered, and aggregated.

Data is generally transformed to make it better organized. Structured, formatted, and validated data improves the data quality and protects applications from potential failures such as unwanted null values, unexpected duplicates and incompatible formats.

Data Cleansing

Data Cleansing is the process of removing unwanted redundant data records.

Data cleansing involves the below steps:

Step 1: Eliminate entries that are duplicates based on defined primary keys of the source data tables.
Step 2: Fixing the structural errors agreed upon or standard practices like correcting entries with lower cases were not allowed, adding or removing padding such as 0s, and following and adhering to naming conventions.
Step 3: Applying aggregations and Global filters in scope: based on the definition of the fields in the area, the various functions are applied to the data. This step can be used to identify the data outliers.
Step 4: Handling insufficient data, blanks and date formats: Replacement of symbols with standard functions, filling up blank records to ensure correct entry, and following standard data formats is done at this step.

Later come the system connectivity and the list of source systems and data sources. Once connected the data transformation and loads to the structured targets are done. The process ETL (Extract-Transform-Loading) is a well-known term in business.

This can be done quickly using scripting as well as many online and offline tools are available in the market to help with the transformation. Finally, the data is checked for accuracy and precision.

Listing a few types of transformations used generally by developers: Applying Aggregation, Data deduplication, Filtering, Joining, At times data is normalized and denormalized based on output requirements and even is binned to be utilized in displaying in histograms. Various formatting and scaling are applied to the data.

Benefits of Data Transformation

Enhanced Data Quality – The pre and post-checks ensure data validity and accuracy.
Ease of Data Management – The uniformity of the data helps manage the data sets better.
Improved Query Performance – Higher and more precise data enables faster index searches, and hence query performance improves.
Flexibility for integration with other data sets – Ease of joins, absence of duplicates, and summary data become more flexible to join, and analysis becomes wider in reach.

Key considerations before Data Transformations

Time: This stage is time-consuming, keeping the end in mind the correct decision should be made.
Cost: The cost involved with this process is much higher hence keeping the timeline and budget in check the scope should be defined.
Performance of the process: Overall process slows down due to the additional transformation layer.
Format: the format has its limitation since converted data can be available in a particular form only.

The term "data transformation" refers to the procedure of changing data from one format to another, usually from the format of a source system to the format required by a destination system. Most data management and integration activities, such as data wrangling and data warehousing, necessitate some type of data transformation. Data is transformed into a more structured format. Both humans and machines might have an easier time working with transformed data. Data quality is enhanced and programs are safeguarded from pitfalls caused by improperly structured and verified information, such as missing values, duplication, improper indexing, and incompatible file types. Integration, administration, migration, warehousing, and wrangling are just a few examples of data-centric operations that necessitate data transformation. It's also essential for any company that wants to use its data to make informed business decisions in real time.

When two or more variables are plotted against each other and the parameters are not uniformly distributed, the resulting data points will be clustered. Reshaping the data to make it more uniformly distributed over the network can lead to a more satisfying visual representation. Data cleansing involves erasing inaccurate or irrelevant information from a dataset or database, whereas data transformation involves changing the data's original format to a new one.

You can better anticipate your customers' changing wants and needs and keep up with industry developments with the help of a data purification tool. Faster response times, more qualified leads, and a more satisfying customer experience are all possible benefits of data cleansing. By regularly purging outdated or irrelevant information, data cleansing helps you quickly and easily locate specific data when you need it. In addition, it reduces the likelihood that you have an excessive amount of sensitive information stored on your machine.

Correcting inaccurate, misleading, or otherwise flawed information in a data set is the goal of data cleansing, also known as data cleaning or data scrubbing. Finding inaccuracies in data and fixing them by editing, adding, or erasing information. It's important to keep an eye out for specific problems in your raw data before you start cleaning. To achieve this, keep an eye on the habits that cause the majority of your slip-ups. This can simplify the process of finding and fixing incorrect information.

The process of cleansing data is intricate. In order to validate your results, you must first clean your data by removing any extraneous or irrelevant information. This is not a simple or easy job to do by hand. Calling them to double-check that they're still in the same role and to confirm or update their contact details. (Phone number, email, etc.). Adding new contacts to take the place of those who have been lost. Incorrect, incomplete, irrelevant, duplicated, or incorrectly formatted data must be cleaned before it can be used for analysis. However, as we've already established, it's not as easy as rearranging some rows or clearing out old data to create room for new.