I came across a clever demonstration of Analytics from a Business Intelligence software provider called QlikView, and thought it worth sharing. Note- In an indirect way, Latigent competed with QlikView and I always admired their clever marketing campaigns. In this application of their analytics tool, they dumped radio airplay data from MediaGuide.com into an OLAP cube and overlayed the QlikView front end (demo found here).
Originally I just wanted to play around with the data, so I filtered based on the greatest band of all time, Van Halen.
Unexpectedly, a couple of things struck me about the results:
- The top two most played songs both appear on the album 1984, but 1984 is the second most played album: Seems a bit counter intuitive at first, but upon closer inspection we discover that the album Van Halen’s songs may be played fewer times, but there are more of them. The aggregate is greater by more than 300 song plays. There is a lesson to be learned here about product distribution, and a pretty good example of Chris Anderson’s Long Tail theory in action.
- Why the big disparity between the “David Lee Roth Van Halen” and the “Sammy Hagar Van Halen”? My assumption is that this is most likely because Van Halen is on tour right now with David Lee Roth. No doubt radio stations are heavily promoting this tour by spinning the vintage favorites. My hypothesis though is one that is impossible to prove given the current data set. One would “just need to know” that these guys are on tour to draw that conclusion. Without this information, what conclusions would you draw? Would they be accurate? Is mine accurate?
Now, an interesting exercise would be to take the chart below that displays where these songs are being played and overlay the tour schedule. Also, the data is only available from Feb 24, 2007 to current. What would a wider data set show us? Is the distribution reversed when they’re on tour with Sammy Hagar? What about when they’re not on tour?
This example demonstrates that the unlocked power of analytics is not just about spotting trends that you otherwise would not have, but its often in finding and qualifying external (and sometimes non-structured) data points and quantifying their impact. It also causes you to ask questions and seek answers that you otherwise wouldn’t: What long tails are hiding in your data? How can you leverage them? What external events influence your business? How do you qualify them, and quantify their impact?
What else do you not know you need to know?