How Does The Data Transformation Process Work?
The modern business world is all about data. Large amounts of data are generated daily in every organization, ranging from startups to multinational corporations, and the business that uses the data efficiently becomes the one to thrive.
To use the data to the best of its capabilities, businesses need an efficient technology that will assist them in extracting data from the large streams and derive meaningful insights. Different departments across your organization can use such insights or actionable information to make decisions that can bring better outcomes.
The tremendously increasing volume of data offers businesses endless opportunities to make better decisions and get expected results. By knowing your business, customers, and competitors better, you can create strategies that will make it more accessible for your business to gain a significant share of the market and reach the top of the competition curve.
One such technology that can help you turn your data into useful information is data transformation.
What is Data Transformation?
Data transformation is a process that involves the conversion of data from one particular format into another. There are big data transformation tools available that can convert diverse forms of data into a single one.
As data reside in different locations and can be in a plethora of different formats, the process of data transformation is not less than a boon for organizations. The process’s goal is to make data into a more understandable and readable format when it is moved from one application or database to another.
Data transformation perfectly fits the scenario where data conversion is critical and needs to be cleansed before it is transformed into a data warehouse. Data cleansing, or we can say cleaning helps companies prepare data for transformation by removing missing values, duplicate data, inconsistencies, and errors. After all the inaccuracies are removed, the data becomes ready to be transformed into a preferred format.
The process of Data Transformation
The process of transforming data involves five steps, which are as follows:
Step 1: Data Discovery
Data discovery is the initial step where the data is identified in its original format. Usually, the step is performed using a tool named data profiling.
Step 2: Data Mapping
In the second step, data mapping is performed with the help of ETL (Extract, Transform, Load) tools. It is the most time-consuming step in the entire process of data transformation as it involves various sub-processes like validation, transformation, value derivation, aggregation, enrichment, and routing.
Step 3: Code Execution
In this step, a code is generated that is used to run the task of data transformation. Advanced integration platforms are used for code generation, thereby eliminating manual efforts to simplify the task for organizations.
Step 4: Code Execution
This is the step where the execution of code is done to derive the desired output.
Step 5: Verification
The last step of data transformation is verification, in which the converted output is verified or reviewed to check precision and accuracy.
Apart from these five primary steps, data transformation may also involve steps like filtering, splitting, enriching, merging, and eliminating duplicate or inconsistent data. Once the task of data transformation is done, information is loaded into a target destination for further analysis and reporting.
Benefits of Data Transformation
Data Collection: As per a report, nearly 70% of data fails to meet the eye. But with the help of a data transformation tool, you can consolidate the maximum amount of data for making better business decisions.
Data Integrity: Bad quality data can cost a company its ability to survive. Therefore, it is crucial for businesses to maintain the highest data quality to deliver better business intelligence. To help you maintain data integrity, data transformation tools can help you by reducing or eliminating issues like missing values and inconsistencies.
Data Handling: Data gathered from multiple sources sometimes contain inconsistencies in metadata, and organizing and comprehending such data is quite a daunting task. Data transformation tool clears metadata to organize it and make its handling easier.
Data Efficiency: Data transformation tools support end-to-end processes by allowing data exchanges between cloud applications, APIs, and others, boosting data efficiency. The tool will also remove the bottlenecks involved in the process by combining integrations to a single, easy to use platform.