Of the vast commentary generated by the high-profile failure of Apple Maps, this bit stands out as highly perceptive. Again, the human factor in data quality (see the previous post) makes itself known.
Perhaps the most egregious error is that Apple’s team relied on quality control by algorithm and not a process partially vetted by informed human analysis. You cannot read about the errors in Apple Maps without realizing that these maps were being visually examined and used for the first time by Apple’s customers and not by Apple’s QC teams. If Apple thought that the results were going to be any different than they are, I would be surprised. Of course, hubris is a powerful emotion.
If you go back over this blog and follow my recounting of the history of Google’s attempts at developing a quality mapping service, you will notice that they initially tried to automate the entire process and failed miserably, as has Apple. Google learned that you cannot take the human out of the equation. While the mathematics of mapping appear relatively straight forward, I can assure you that if you take the informed human observer who possesses local and cartographic knowledge out of the equation that you will produce exactly what Apple has produced – A failed system.
The issue plaguing Apple Maps is not mathematics or algorithms, it is data quality and there can be little doubt about the types of errors that are plaguing the system. What is happening to Apple is that their users are measuring data quality. Users look for familiar places they know on maps and use these as methods of orienting themselves, as well as for testing the goodness of maps. They compare maps with reality to determine their location. They query local businesses to provide local services. When these actions fail, the map has failed and this is the source of Apple’s most significant problems. Apple’s maps are incomplete, illogical, positionally erroneous, out of date, and suffer from thematic inaccuracies.