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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"/>
There are companies that provide self-service data transformation tools. They are aiming to efficiently analyze, map and transform large volumes of data without the technical knowledge and process complexity that currently exists. While these companies use traditional batch transformation, their tools enable more interactivity for users through visual platforms and easily repeated scripts.<ref>{{Cite news|url=https://www.datanami.com/2016/05/31/self-service-prep-killer-app-big-data/|title=Why Self-Service Prep Is a Killer App for Big Data|date=2016-05-31|work=Datanami|access-date=2017-09-20|language=en-US}}</ref>
Still, there might be some compatibility issues (e.g. new data sources like [[Internet of Things|IoT]] may not work correctly with older tools) and compliance limitations due to the difference in [[data governance]], preparation and audit practices.<ref>{{Cite web |last=Sergio |first=Pablo |date=2022-05-27 |title=Your Practical Guide to Data Transformation |url=https://blog.coupler.io/what-is-data-transformation/ |access-date=2022-07-08 |website=Coupler.io Blog |language=en-US}}</ref>
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