Why data preparation is crucial in artificial intelligence (AI) workflows - EDN

For design engineers, an artificial intelligence (AI) workflow encompasses four steps: data preparation, modeling, simulation and testing, and deployment. While all steps are important, many engineers often overemphasize the modeling stage, presuming that it plays the largest role in producing accurate insights. However, since data flows throughout the entire AI workflow, the initial data preparation step is crucial. It ensures that the most useful data is entered into a model. Figure 1 Data is the driving force in the development of an AI workflow.