Definitely,we can approach this in different stages primary and final stage .In final stageif the results match my expectations this means that data transformed in a right way especially if the analysis and graphs reflected the actual outcomes expected.
Ensuring that data is transformed correctly involves having a thorough assessment of the quality of the raw data from the outset to establish a solid foundation.
It is crucial to define clear objectives and understand the end goals of the transformation process. Then, profiling and exploring the data helps to understand its structure and relationships to develope a detailed transformation logic and mapping, using reliable tools for automation, for applying rigorous validation and testing at each stage are essential steps.
It should be taken into consideration to establish data governance policies and following industry standards to ensure compliance and consistency. And finally incorporating feedback and iterative processes enables continuous improvement and accuracy in data transformation.
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