Why You Need Data Transformation in Machine Learning

Thanks to machine learning and the advancements in software and technology, enterprises can now process and understand their data much faster using modern tools with established algorithms. This effectively allows them to deliver more powerful marketing campaigns, deploy efficient logistics operations, and significantly outpace competitors. But enterprise data can be convoluted and messy in its raw state.

This means some form of data transformation is required prior to any data analysis to help you achieve business use cases like the ones mentioned above. Simply put, data transformation makes your data useful. Data transformation is the process in which you take data from its raw, siloed and normalized source state and transform it into data that’s joined together, dimensionally modeled, de-normalized, and ready for analysis.

Author: Damian Chan

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