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Worse yet, when it comes to complex or large volumes of data, the work is relegated to the small number of valuable resources with advanced data science skills, regardless of whether they have the business context or not. This on-going process of shaping, standardizing and enriching data to conform to the right analytic outputs, has long been considered tedious, time-consuming, “janitorial” work. Data element to element mapping can be complicated and requires complex transformations that require lots of rules, which is why successful analysts use these tools to help simplify the process. This data transformation process of converting sets of data values from a source format to a format consistent for a destination data system often requires tools. In addition, data transformation makes it possible for organizations to transform data from a storage database to the cloud to keep information moving. Organizations benefit from transforming data by gaining insights into vital operational and informational internal and external functions. Organizations may transform data to make it compatible with other types of data, move it into the appropriate database, or combine it with other crucial information. Transformed data is usable, accessible, and secure to benefit a variety of purposes. During the process of data transformation, an analyst will determine the structure, perform data mapping, extract the data from the original source, execute the transformation, and finally store the data in an appropriate database. The most common data transformations are converting raw data into a clean and usable form, converting data types, removing duplicate data, and enriching the data to benefit an organization. Data transformation is the process of converting data from one format to another.