Data transformation is the process of changing or converting the format, structure or values from one source to the format, structure or values of a destination.
Data transformation is often used in processes such as data migration, data integration or data management tasks such as data wrangling and data warehousing.
In projects involving data analytics, the data can be transformed at stages. Companies that have data warehouses that are on-premises typically use the ETL (extract, transform, and load) process where data transformation is one of the middle steps. Most organizations lately use cloud-based data warehouses where computing and storage resources can be scaled with extremely low latency that can be measured in seconds. This scalability allows organizations to bypass the preload transformations and load raw data into the data warehouses. It is then transformed at query time. This model is called ELT (extract, load transfer)
The data transformation techniques can be constructive with functions such as copying, adding or replicating data or destructive with deleting records and fields. It can also relate to the aesthetics such as standardizing street names or salutations or be structural with tasks such as moving, renaming and combining columns in a database.
Considering what is happening with Bid Data today, data transformation is a critical tool for businesses than ever before. An exponentially growing number of devices, programs and applications constantly generate tremendous amounts of data and with it comes the challenge of compatibility risk. This is where data transformation comes in to enable organizations to convert data they receive from multiple sources and transform it into a format that can be stored, analyzed and integrated and finally mined for businesses purposes.
Going by the data transformation definition, the process takes data from a source and converts it into a destination format that is usable for various purposes. It takes place in the ETL (Extract, Load, Transform) process wherein the extraction stage, the data needs to be recognized and pulled out from where it is saved and moved into one repository. This raw form of data must first be cleansed and prepared for transformation by fixed issues with missing values and inconsistencies. Then these data transformation steps come into play:
Additionally, further customized operations such as filtering, enriching, splitting columns, joining and removing duplicate data are done in the basic steps to prepare the data to be sent to the target destination.
Every industry today is being revolutionized by the data they are able to collect on the behaviour of their customers, supply chain processes, internal processes, or any other measurable variable. Insights gained by data can tremendously improve operational efficiencies, streamline processes and generate higher revenues. However, the challenge is to make sure that the data being gather can be used in a meaningful way, and the first step to it is a data transformation process. Here are the benefits of data transformation:
Organizations of all types are now using data transformation to manage their processes at various levels. Save the Children UK, a nonprofit, for example, springs into action during times of natural disasters. They manage tremendous volumes of data related to volunteers, donors and compliance initiatives to fulfil their goals. The global technology and manufacturing company Johnson Controls uses 200 ERP and CRM systems for its operations management across the globe. With their massive workforce spread across 150 countries, they rely on fast and actionable data to run their operations.
The data transformation functions can also be worked using hand-coding, but companies choose to use data transformation tools or platforms as they are more efficient, cost-effective and less prone to mistakes. Hand coding is cumbersome, and the code must be rewritten for each process, leaving the door open for errors while they are harder to replicate. ETL tools are much better from the standpoint of cost and offer a range of features including data flow representations, monitoring, parallelization and failover features.
Beyond the business costs, hand-coding in scaling and innovation requires skill that is harder to find and maintain. Moreover, modern hybrid data processing environments are much higher in complexity than they were in the past. The data transformation tools as-a-service models have gained popularity, making it a lot simpler for organizations to retrieve and use their data.
The data transformation process enables companies and organizations to extract data they need from various sources and formats to convert it into a useful form to offer insights. As data is ever-increasing from all sources, there are limitless opportunities to leverage data into making better business decisions or improve any desired result. Data transformation plays a central role in this.
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