In light of data’s growing prominence in modern business, enterprises must ensure that they have a colossal technological foundation in place. Technology can not only help organizations extract value from large streams of data but also foster communication across the ecosystem to deliver better experiences.
Unfortunately, the ad hoc development of a multitude of legacy systems has resulted in heaps of information silos that entail redundant and duplicate data. To harness data effectively, enterprises need to integrate these silos and leverage existing IT assets for creating more flexible, agile enterprise systems. The role of data transformation comes into play here.
What Is Data Transformation?
The process of data conversion, from one particular format into a required format, is called data transformation.
The data transformation tools and methods are important since information can reside in different locations and has a plethora of formats, and organizations must have access to solutions for converting this diverse information depending on the unique needs of its business ecosystem. The end goal of this process is to make data more readable when it is moved from one application or database to another.
In a typical scenario where data conversion is crucial, information needs to be cleansed before it is transformed. Data cleansing or cleaning helps companies prepare data for transformation by eliminating errors, missing values, or inconsistencies. Post this, the data is ready to be transformed.
What Are the Steps of Data Transformation?
Here are five simple steps of data transformation:
Step 1: Data Discovery -Data transformation’s first step is to identify and realize data in its original or source format, hence the name data discovery. Normally, a data profiling tool is used to carry out this step.
Step 2: Data Mapping -In this step, data mapping is performed with the help of ETL (Extract Transform Load) data mapping tools. This step is the most time-consuming segment of data transformation as it entails sub-processes such as validation, translation, value derivation, enrichment aggregation, and routing.
Step 3: Code Generation -In this step, code is generated to run the data transformation task. Modern integration platforms are employed to generate the code, thereby simplifying the task for enterprises.
Step 4: Code Execution -In this step, code is executed to produce desired output.
Step 5: Verification -Finally, the converted output is verified or reviewed to check accuracy and precision.
In addition to these five primary steps, the process of data transformation encompasses several other steps like filtering, enriching, splitting, merging, and eliminating duplicate data. Following the data transformation process, information is loaded into its target destination for further analysis or usage.
What Are the Benefits of Data Transformation Software?
Any-to-any data transformation tools that automate connection, transformation, and integration of business-critical data exchanges offer a number of benefits.
- Data Collection: According to Forrester, 60% to 73% of data fails to meet the eye. With the help of a data transformation tool, companies can gather the maximum amount of data for making better business decisions.
- Data Handling: Data produced by myriad sources sometimes has inconsistencies in metadata and organizing as well as comprehending such data is both challenging and time-consuming. Data transformation software clears metadata to organize it and understand what’s in the data set, making the handling part easier.
- Data Integration: The data transformation process plays a crucial role in enhancing data integration for companies. By converting raw data into a standardized and consistent format, it enables seamless merging of disparate data sources. Through data transformation, companies can cleanse, validate, and enrich their data, ensuring accuracy and reliability. This process also facilitates the mapping of data fields across systems, enabling smooth data flow and reducing compatibility issues. Furthermore, data transformation allows for the application of business rules, data aggregation, and summarization, empowering companies to extract valuable insights, make informed decisions, and deliver value.
- Data Integrity: Maintaining data quality is a major concern in organizations owing to increased risks and higher costs of bad data. Organizations must maintain data quality to deliver better business intelligence. Data transformation helps organizations reduce or eliminate quality issues like inconsistencies and missing values.
- Data Efficiency: Data transformation tools support end-to-end processes by creating one-to-many ecosystem data exchanges between APIs, cloud applications, etc., consequently driving data efficiency. It also removes the integration process bottlenecks by consolidating integrations to a single, easy-to-use platform.
Adeptia’s data integration platform can transform data into any format while automating validation, transformation, and orchestration processes for greater business intelligence and operational efficiency.
Conclusion
Enable digital transformation and support growth objectives with Adeptia’s scalable, secure, and reliable integration platform. By providing a consolidated and agile approach to complex integration challenges, Adeptia allows enterprises to rapidly adapt and innovate while ensuring governance, managing risk, and maintaining compliance. With pre-built connectors, drag-and-drop process flows, and built-in data quality features, customers can rapidly integrate and exchange data across clouds, on-prem systems, and third parties to underpin transformation