Although this is a subjective topic, there are a few easy assessment criteria that may be utilized to determine a data model’s correctness. The following are the details:
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A well-designed model should be able to anticipate outcomes. This refers to the capacity to easily foresee future insights when they are required.
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If necessary, a rounded model can readily adjust to changes in the data or workflow.
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If there is an instant need to massively grow the data, the model should be able to handle it.
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To assist consumers in obtaining the desired outcomes, the model’s operation should be simple and straightforward to understand.