It is necessary first to identify the type of data we are going to be working with. One has to keep in mind whether the available data will be able to address an existing problem or not. We keep certain factors in consideration before selecting the data:
Data should not be of low quality: Low-quality input= low-quality output.
Dataset is not error-ridden: The more the errors the more time it consumes to preprocess it.
Dataset is unbiased: Having unbiased dataset opens new doors in terms of discoveries in predictive modeling.