Old Habits Die Hard
director of marketing
Travel, Technology and a Tale of Two Bridges
My wife and I grew up in the Philadelphia/South Jersey area and relocated to New Hampshire in 1992. Since we both still have family “Down South”, we make trips around major holidays to visit them. 25 years of driving through one of the busiest traffic corridors in the US and we’ve become experts on the routes- how to get around Boston and NYC and when to leave so we miss rush hour traffic as well. When our kids were younger, we even knew where all of the McDonald’s Play Place stops were for when “restless child in the back seat syndrome” kicked in.
When we first started these trips, our navigation tools were, as my kids would say, “analog”. Paper maps from AAA and tuning in to the local AM radio stations for the traffic reports so we could avoid any accidents are as high tech as it ever got. As the years have gone by, we have memorized the route so well, we don’t need any navigation tools, we just know which way to go.
One of the busiest sections of our route is navigating around New York City and crossing over the Hudson River. The two main bridge options are the George Washington Bridge (GWB) and the Tappan Zee Bridge. With each bridge seeing traffic volumes of around 4 million vehicles per month, they both are busy places.
Conventional wisdom among those living in the Philly area is, if you are not going in to NYC, take the Tappan Zee. It goes farther West of New York City and avoids a lot of the city traffic. For the last 25 years, we have always taken the Tappan Zee. Yes, you hit traffic but you still avoid the legendary delays of the GWB. Technology wasn’t informing this decision, it was formed by our own past experiences and general assumptions we have continued to have about which route is more crowded.
This year on our trip down there for Christmas, my wife put the route into Google Maps just to see what it was estimating for total travel time. We were both shocked – the recommendation was to take the GWB and not the Tappan Zee. Total travel time saved looked to be about a half hour- a sizable sum for a trip that typically takes 8 hours of driving. However, to me, this was crazy talk! Just because the data was telling us it was faster, it was really a stretch for me to get past my own assumptions and biases that you never take the GWB. Yes, the person who works for the data driven software company really wanted to ignore the data because well, I knew I was right.
We decided to bite the bullet and follow Google’s recommendations. Everything was looking good as we traveled over the GWB and the time savings was noticeable. As we neared the end of the bridge though, we saw the brake lights. A huge backup getting onto the New Jersey Turnpike. Google saw this too and routed us on a smaller state route that reconnected us to the New Jersey Turnpike and avoided the backup. Total travel time saved on the trip south: about 35 minutes.
On our trip home, Google again recommended the GWB, and even had us hug the coastline along Interstate 95 for longer than we normally would. Total travel time saved on the way home: 45 minutes.
This whole experience got me thinking about how we all use data and when we choose to ignore it because “we’ve always done it this way”. To an outsider, using Google to navigate the fastest route probably seems obvious but, being in the middle of it I wasn’t able to step back and let the data provide me the insights that were there. How many of us do this in other parts of our life or in our jobs? Are there processes you are following because instinct tells you they are right and you never have bothered to look at the data?
Conventional wisdom should never be ignored – but this travel experience was a good reminder to me that it’s always good to take the time to see what the data is saying so you can make a fully informed decision. In our case, this saved us over an hour of total travel time.
Are data insights always this obvious? In our next blog post, analyst James Cousins is going to dive into some year-end summary data from Goodreads and how one piece of erroneous data completely changed his end results.