The big data revolution has impacted the world in various ways. It has created a plethora of opportunities for business users and helped them grab a larger market share. While the data volume has increased at an unprecedented rate, the capacity to handle such unfathomable volumes of information is only just catching up. However, modern techniques of data transformation have made the journey easier. Let us find how data transformation techniques have enabled organizations to deal with big data for enabling better decision-making. In addition, this blog discusses the steps of data transformation along with its importance. Let’s begin.
Data Transformation Explained
Wikipedia defines data transformation as a process of converting data from one particular format of a source into another. This process is an essential segment of data integration and data management tasks such as data wrangling and data warehousing. Depending on the type of changes that must take place in data before it can be used for various purposes, data transformation can either be simple or complex.
The data transformation process can be carried out using manual or automation intervention methods. In the age of Big data, a process like data transformation proves to be an essential segment of business operations. A multitude of programs, devices, applications, or systems is responsible for generating vast volumes of data, the purity of which is at risk. Data transformation software enables organizations to streamline data conversion, meaning it converts data into a format that can be integrated, analyzed, and stored to extract actionable insights.
Understanding Data Transformation Procedure
The primary objective of data transformation is to carry out data extraction from a particular source, followed by conversion (into the required format) and delivery (into the destination). In short, this process is called ETL, which involves Extract, Transform, Load.
During the extraction phase, data is first garnered from different sources or locations and stored into a database or target repository. Now, data extracted from sources is usually present in the crude form. In other words, this data cannot be used as it is. Companies need to employ techniques such as data transformation to convert this raw data into a usable form. The first and most important process of ETL, data transformation process allows companies use data to extract valuable insights. This, in turn, drives their decision-making capability. To do so, data is converted into the required format, In some cases, data is cleansed first. Data cleansing helps enterprises prepare data for transformation by getting rid of any missing or duplicate values, or errors.
For better understanding, let’s delve into the 5 major steps of data transformation.
Step 1: In this first step, data is identified in its source or original format. This step is known as data discovery. To carry out this step, a data profiling tool is used.
Step 2: In this step, data mapping is performed with the aid of ETL data mapping tools.
Step 3: Then, the code is produced to run the data transformation process. For generating this code, developers use data transformation software.
Step 4: In this process, data transformation is carried out.
Step 5: At the end, the process is verified and reviewed for checking accuracy and precision.
In addition to these 5 major steps, the transformation process involves processes such as filtering, merging, splitting, enriching, and more. Let’s explore them.
- Filtering (that involves selecting only a specific number of columns to load)
- Enriching ( that involves enriching the quality of data such as converting full name to first name, middle name, and last name)
- Dividing or splitting the column into multiple columns
- Merging data from a wide range of sources
- Eliminating or removing duplicate data
Perks of Data Transformation
The big data revolution has sparked the need for organizations to rely on the automated data transformation process. Such solutions offer a multitude of benefits, such as:
- Improving Data Value: As per Forrester, almost 60 to 73% of data goes unrecognized. Companies can use robust data transformation software to collect as much data as possible, and more data means more value.
- Handling Data with Precision: Data present in various data sources has inconsistencies in metadata, and analyzing such data is challenging and time-consuming. Using ETL mapping tools can help companies organize and understand what’s in your data set.
- Enhancing Data Quality: Data transformation improves quality issues in data by removing errors and missing values; thus helping organizations deliver improved business intelligence. Improved data quality not only enables organizations to boost business intelligence but also helps make informed decisions through better-quality insights. By making informed decisions, companies can create more sales and meet customer demands without difficulty in driving revenue.
- Managing Queries Faster: Using data transformation software, data can be standardized and stored in a source location where it can be analyzed for further purposes at a faster pace.
- Increasing Data Efficiency: Top-notch data transformation solutions provide aid to end-to-end processes by morphing one-to-many ecosystem data exchanges between APIs, cloud applications, and more, accelerating data efficiency by leaps and bounds. These tools also remove the potential integration bottlenecks by consolidating integrations to a single, easy-to-use platform.
big data concept -DepositPhotos
