Missing Data Categories
Missing data, technically known as NULL, indicates a lack of a value. Missing data can fall into three main categories:
Missing Completely at Random (MCAR): Here, the data is missing across all the observations. For example, the customer email address is missing in all the customer records.
Missing Not at Random (MNAR): The missing data has a russia whatsapp number data structure or defined pattern. For example, income values are missing for the student category of customer records.
Missing at Random (MAR): Here, the data is missing relative to the observed data. The data is missing randomly and there is no pattern to the missing data. For example, the customer’s date of birth is missing in 12% of the customer records.
So, what are the solutions for addressing MCAR, MNAR, and MAR missing data categories? Basically, the solution for missing data can fall into three main categories:
For addressing issues pertaining to MCAR, the solution is improved digitization, including deployment of data capture technologies such as optical character recognition (OCR), intelligent document processing (IDP), Bar codes, QR codes, web scraping, and more. However, all the digital solutions need to be supplemented by user training for better adoption.