I used to think that I was overly sensitive to data issues given my profession. I can't help but trace almost all issues back to data problems. The fact that we're in the Information Age might explain why data problems seem to be at the heart of many issues. Nonetheless, look a bit deeper and you'll see that data is often the common denominator.
We had a case locally where a bus, that was too tall for a footbridge overpass, tried to drive under it anyway. You can guess the result (see: http://seattlepi.nwsource.com/local/359497_bus18.html). A cursory review of the circumstances shows that data problems were at the heart of this issue. For example, the GPS unit the driver was using is programmable for car, bus, or motorcycle. The driver set it for "bus". The expectation/assumption from the driver was that the GPS unit would know if a route contained hazards that were specific to buses. It seems like a fair assumption given the choice of settings. Despite the indications of the bus height (within the bus) and the overpass height (big yellow sign on the side of the overpass), the driver tried to drive under the footbridge. The lack of GPS data about the footbridge as a hazard to buses contributed to this issue.
Luckily, the bus accident did not result in any serious injuries. There have been other data problems at the root of much more serious issues, however, especially in the medical field. Pharmacy errors are the subject of news stories all too frequently, and some of those errors end tragically. One doesn't have to do much more than search the Internet for data error stories to find daily occurrences of issues where data is a contributing cause. This isn't a case of someone being overly sensitive to data issues and seeing them because they're top-of-mind. The next time you encounter a problem, look deeper. Data is likely the problem!
Friday, October 31, 2008
Subscribe to:
Post Comments (Atom)
1 comment:
Data quality was definitely a root issue during my mining career. My recently blog post at http://www.tomsalzer.net/2009/12/process-of-discovery.html discusses how data that may be factual at the point it is collected can result in incorrect conclusions when extrapolated. Sometimes what we call data is really assumptions and interpretations!
Post a Comment