How to Deal With Missing Data: Databases, Zeroes and ImputationDecember 10, 2012 By Stephen H. Yu
Then, what to do with the "unknowns"? Do we just dismiss them and move on? Properly treating missing data may boost targeting efficiency as not all missing data are created equal, and missing data often contain interesting stories behind them. For example, certain variables may be missing only for very rich people and very poor people, as their residency may not be as exposed as others. That in itself is a story. Some data may be missing in certain geographic regions or for certain age groups. "Not" having access to broadband may mean something interesting, too.
Filling in the Blanks
Like other targeting challenges, missing-data management starts with proper database design. Even at the data collection stage, reasons why certain data points are missing should not be ignored. If you are dealing with numeric data, such as dollars, frequency counts, dates, etc., why are they missing? Is it because they are really unknown and incalculable (no transaction to deal with), or a simple issue of mismatches among different data marts and sources? Database managers may not always know the actual reasons why they are missing, but they should never blindly fill the missing values with "0"s. Zeros must be reserved for known and verified zeros.
Users may agree that "true" missing values must be stored as ".", for instance. If a variable such as "number of children in the household" is missing, data managers should never put it in the system as zero unless it's confirmed that the household does not include any children. Further, one should assign separate codes for "missing values due to non-matches to external data source" (i.e., matching issue) vs. "matched to external source but still missing" (i.e., even your data vendor doesn't know). After all, not matching to a professional data compiler's list may mean something, and the missing denotation may act as an independent predictor in models.