Reading What Your Data Does–or Doesn’t–Tell You

ByA massed formation of British Lancaster bombers flying overhead at the start of a Thousand Bomber Raid. Matthew Hulme, CAPP, MPA

It is no secret that “data driven decision making” has become a buzzword (buzz-phrase?) in the parking industry. However, it is not a new concept. Good data provides the backdrop for strategic planning, driving new initiatives, and evaluating old ones. The key word here is “good,” as data is just meaningless numbers unless it is properly qualified and vetted.

As a military history buff, an example I love to use is aircraft bullet hole data from WWII. The military wanted to learn strategic areas to place armor on airplanes, as armoring an entire plane made it much too heavy. Data was compiled from planes that returned from engagements over Europe and the bullet holes were tabulated. The prevailing thought was to armor the areas that took the most damage, based on the data.

On first glance, this seems to be sound logic, particularly as the fuselage of the aircraft is where the data said took the most hits (and where the crew is located). However, it is important to remember that the data was only gathered on the planes that returned after engagements, not from the ones that were shot down. Because someone realized this, the section of the plane with the lowest amount of hits per square foot (the engines) was actually chosen as the location for additional armor. This proved to be the correct answer, and the armoring of engines continued into the Vietnam conflict.

I give this example to provoke thought about how you are interpreting and forming hypotheses about your own data. Sometimes the right answer does not lie in what you think the data says, but what may be missing from the data altogether.

Matthew Hulme, CAPP, MPA, is parking services supervisor with the City of Cincinnati.