These days I’m involved in an activity in which you may say that we by creating data with questionable quality are making better information quality.
The business case is within public transit. In this particular solution passengers are using chip cards when boarding busses, but are not using the cards when alighting. This is a cheaper and smoother solution than the alternative in electronic ticketing, where you have both check-in and check-out. But a major drawback is the missing information about where passengers alighted, which is very useful information in business intelligence.
So what we do is that we where possible assume where the passenger alighted. If the passenger (seen as a chip card) within a given timeframe boarded another bus at a stop point which is on or near a succeeding stop point on the previous route, then we assume alighting was at that stop point though not recorded.
Two real life examples of doing so is where the passenger makes an interchange or where the passenger later on a day goes back from work, school or other regular activity.
An important prerequisite however is that we have good data quality regarding stop point locations, route assignments and other master data and their relations.
Liliendahl on Data Quality 