Data which is inconsistent with our models, should be dealt with. Common sense can also be used to detect such kind of inconsistency.
Examples:
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The same name occurring differently in an application |
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Different names can appear to be the same (Dennis Vs Denis) |
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Inappropriate values (Males being pregnant, or having an negative age) |
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A particular bank's database had about 5% of it's customers born on 11/11/11, which is usually the default value for the birthday attribute. |