Information is power, and when you have access to the right data, it drives you to huge business growth and success. As per McKinsey Global Institute, data-driven organizations are 23 times more likely to acquire customers and 6 times more likely to retain customers.
But the real challenge is how to migrate data from various disparate sources to a single readable format and transform it to draw meaningful inferences from it.
Data transformation plays a significant role here. It includes migration, integration fabrication, and management of data in a manner that aids organizations in making informed decisions, therefore transformation of data is becoming the holy grail for most modern-day businesses.
While each process of data transformation is crucial, data migration still remains the core, and, thus the major focus of this blog.
Let us understand it in more detail
Data migration is the process of transferring and transforming data between databases, formats, or enterprise applications. There are various reasons why an organization may decide to migrate data to a new environment, such as to replace legacy applications with modern tools, switch to high-end servers, or consolidate data post-merger or acquisition.
Regardless of the underlying reasons, ETL remains a proven method that many organizations rely on to respond to data migration needs.
What is ETL
- Extract: Extraction is the process of pulling the source data from the original data source or database and sending it into a data lake storage system.
- Transform: Transformation is the process of changing the structure of information so that it can integrate with the target data system and the other data in the system.
- Load: Loading is the process of depositing information into a data storage system.
Using ETL tools, businesses extract and consolidate data from different repositories, both from external and internal sources, and get a unified, well-rounded view of all business operations. Additionally, ETL also enables near real-time data warehousing, thereby providing business users and decision-makers fresh data for reporting and analysis. It also helps enterprises create more flexible, agile enterprise systems and ensures that only clean and consistent data makes it to their data repository and BI tools.
ETL is a key data management process and an integral part of data transformation. Business owners can capitalize on the entire data transformation process to maximize their business potential and reap substantial value from their data.
Other processes of data transformation
Document Conversion: Converting documents in various formats like HTML, XML, epub, and word.
Data Integration: Defined as the simple process of combining data from more than one source.
Data Digitization: Converting documents or data present in physical formats into the online medium
Data Management: Review, verification, and validation of data and QA of data displayed on the application
Workflow and document management: Integrate digital asset supervision and distribution directly with other applications, so that users may retrieve relevant and authorized documents directly from anywhere.
Business benefits of data transformation
Transformation, when used correctly, can improve data quality significantly and remove redundancies and junk.
Transformed data is easier to use, trustworthy, and compatible with end systems and applications.
Transformations can help businesses standardize data and improve data management thereby improving process efficiency
Transformed data can be utilized by various tools for different applications, such as visualizations, reporting, analysis, etc.
Questions around data transformation?
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