Data transformation (computing): Difference between revisions

Revision as of 19:01, 16 February 2023

Revision as of 20:13, 16 February 2023

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The people who need to use the data (e.g. business users) do not play a direct role in the data transformation process.<ref name="digital.lib.washington.edu">Morton, Kristi -- Interactive Data Integration and Entity Resolution for Exploratory Visual Data Analytics. Retrieved from: https://digital.lib.washington.edu/researchworks/handle/1773/35165</ref> Typically, users hand over the data transformation task to developers who have the necessary coding or technical skills to define the transformations and execute them on the data.<ref name="The Value of Data Transformation"/>

This process leaves the bulk of the work of defining the required transformations to the developer., which often in turn do not have the same [[___domain knowledge]] as the business user. The developer interprets the business user requirements and implements the related code/logic. This has the potential of introducing errors into the process (through misinterpreted requirements), and also increases the time to arrive at a solution.<ref name="digital.lib.washington.edu"/><ref name="ReferenceA">McKinsey.com. Using Agile to Accelerate Data Transformation</ref>

This problem has given rise to the need for agility and self-service in data integration (i.e. empowering the user of the data and enabling them to transform the data themselves interactively).<ref name="andrefreitas.org"/><ref name="ReferenceA"/>