Skiing and Big Data

This year a new free app by Dolomiti Superski allows checking your own performance after a day of skiing and establishing your ‘wellbeing factor’.

Once you have inserted the code on your skipass ticket, the app will show you a short animation with all the lifts you have taken. Briefly, you can reconstruct your skiing day based on when you have checked in at a lift: the only data needed is your skipass number, because the system checks your skipass ID with the data collected at the barriers. Obviously therefore, it won’t tell the exact slopes you’ve done: it is impossible for the system to know whether you were skiing on the right or left of the lift, which slopes in succession have you done (especially given the wide range of possibility offered by all the Dolomiti Superski carousels).

For a customer, this is a fun way to record km skied, numbers of lifts taken, etc. – which is indeed great if you are competing with friends! (no, I’m not suggesting this is the mature things to do…but as a teenager I would have appreciated it, especially right before ski-competitions days!) For a customer more interested in her/his wellbeing factor, this is of course equally interesting.

However, how could this collection of data help the tourism industry in the area? Surely, Dolomiti Superski was already collecting data on the most used lifts and we know now that it could easily follow also each skipass holder throughout the day, letting perhaps emerge patterns on the most used routes and sequences of lifts.

What would happen if these data were to be released to third parties, which at the moment is not happening apparently (when signing up for the service, you have to agree on the privacy terms&conditions, and I haven’t seen anything about third parties)?

These type of data will probably help local restaurant and service points: knowing the sequence in which lifts are mostly taken, could indeed be useful, but again probably these enterpreneurs have already noticed the most convenient spots for a new venture. Similarly, these data might support the planning of new car parks or shopping area, but again planners have already been relying on this type of data for years.


What is indeed different is the collection of these data: if it is possible to record paths and passages through ticket offices and lifts automatically, rather than through observations and numbers of passages in each spot loosely connected to the whole system (i.e. knowing how many people passed through barrier A, how many through barrier B, and how many parked in area C, but not knowing how these overlap and relate to each other). This is undoubtedly an advantage offered by this type of Big Data.

What if something like that happened for museums? Could we connect visitor numbers within each institution to broader patterns of cultural consumptions? And why would we want that?

I wonder if some of the touristic card allowing visiting multiple museums within one city/area (e.g. FirenzeCard, a unique pass for 67 museums in Florence) could allow collecting similar data – theoretically, this should be possible, and it will help understanding tourists’ interests and favourites museums, beyond the Uffizi Gallery.

Within a museum instead a similar system, sort of check-points based on mobile data tracking, has been used to create maps of the flow of visitors: see the examples here, particularly that of the British Museum. Though the application of big data to visitor tracking is indeed interesting and on the raise, with subsequent privacy concerns, it would be interesting to see it to investigate also to track ‘visitorship’ beyond a single institution, connecting it with data from other institutions and sources (see this paper by 
Rose Paquet Kinsley and Jason Portenoy  on Perspectives of Emerging Museum Professionals on the Role of Big Data in
Museums ,


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