For my course, Business Informatics, we have an assignment based on excel skills that attempts to develop, not only the ability to use a spreadsheet, but to understand some business processes.
For the last few semesters, we have used the idea of an online, affiliate marketing/advertising type of analysis, where students had to decide which items to remove from the listing based on observable patterns in a very small data set. The idea is to test whether they can create a formula as well as whether they paid attention to any of the lecture material on new business models, etc. Mostly they didn’t, but some always got it. They had to do things like add GST, multiply number sold by price and subtract cost, sort according to income, really basic stuff.
This year, we’ve decided to be a bit more topical, because, let’s face it, selling online is old. One of the things that comes up frequently is the use of social networking sites and how they ‘rob’ time from employers and other bizarre notions. We want students to analyse time spent at different tasks (mostly online) and determine whether these sites should be blocked, because Management have ‘expressed concern’ about usage patterns.
This was partly suggested by a student from last semester who mentioned, in the lecture on social networking, that her company had decided to block FaceBook because in the last month, people had spent 500 hours on the site. Of course, they panicked about that, leading them to block it. We discussed the issue for a while and I eventually asked how many people worked at her place of employ. Well, it was about 1000. So, on average, each employe spent half an hour PER MONTH on FaceBook. This is obviously (note the sarcasm) a Big Problem. Or not.
So, to assist future managers when confronted by such big numbers with making a decision, we are going to get them to make a decision based on some not entirely real data[1]. The basic problem I’m having is coming up with sites that people who actually work would actually visit for their actual work.
This is the list so far.
www.facebook.com, www.youtube.com, www.techdirt.com, scholar.google.com, news.com.au, gmail.com, www.bom.gov.au, www.comsec.com.au, orkut.com, macheist.com, realestate.com.au, digg.com, yahoo.com.au, seek.com.au, twitter.com, bne.com.au, icanhascheezburger, boingboing.com, microsoft.com, apple.com.au, bank.com.au, abc.net.au, ning.com, squidoo.com, flickr.com, imdb.com, paypal.com.au, dominos.com.au, huffingtonpost.com, goldencasket.com, whitepages.com.au, blogger.com, thinkgeek.com, articulate.com, wordpress.com, yellow.com.au, picnik.com, itunes.com, iinet.net.au, lifehacker.com, tvguide.com.au, delicious.com, pcworld.idg.com.au, ted.com, instructables.com, virginmobile.com.au, ourbrisbane.com.au, learningrails.com, www.griffith.edu.au, www.cqu.edu.au, www.uq.edu.au, www.qut.edu.au, www.ebay.com.au
But it doesn’t seem too real, because there has to be things that people not working in academia would possibly visit in the course of their work and, let’s face it, with the exception of a few, most of them are fairly academic or, a bit on the geeky side.
The second problem I have is to actually work out how such a data set would look, and it will probably be large. The students will still have to do averages, some basic calculations, the pivot table and a couple of graphs.
I wouldn’t mind adding a second set of data about how much individuals achieve in a month and fudge that data to show that the people who actually achieve the most, also spent the most time on social networking, because that’s what we do!
So, any network enginers out there? Do you have any ideas how this kind of data set would look?
- but when have universities been concerned with *real data* – oops more sarcasm [↩]
