🤷‍♀️Not all missing data is equal
Missing data is a problem that often comes up in Data Science and Machine Learning. There are many reasons why data may be missing, depending on the type of data and collection methods. But not all missing data is the same, they can be broken down into the following categories:
• Missing due to the impossibility of their collection or the high cost of this procedure
• Structurally missing data that cannot be obtained due to the characteristics of the research objects;
• Missing due to random failure;
• Missing due to non-obvious or unknown reasons.
Depending on the reason for the lack of data in the dataset, you can smooth or even eliminate it. For example, structurally missing data suggests a change in the structure of questions about the object of study in order to get answers to them. Instead of collecting rare or expensive data, you can choose proxy metrics that allow you to make a decision. Constantly recurring failures need to be addressed, as well as more knowledge about the reasons for the lack of expected data.
https://medium.com/@nahmed3536/types-of-missing-data-e718e6ac2a55