Augenmusik

The recent scandal about apple’s iOS location data collection has caused – not surprisingly – a huge uproar. Yet, some people (myself included) found themselves even jealous of iOS people that had this amazingly detailed data set of their own movements and could play around with and visualize it. So, where does this completely different outlook on personal data come from and what implications does it have for society?

First, I have to admit to having a somewhat bleak outlook on the future of personal data: Privacy and self-restrictions to collecting only as little data as necessary are wonderful ideas, but simply no longer enforceable. We’re moving towards a future where extensive data collection using all kinds of sensors and devices is commonplace. Reasons for that are that it’s a regulatory nightmare to keep companies from storing this information and enforcing their deleting it. This becomes worse when every single device collects data instead of large websites and services only. Also, if you argue that this data shouldn’t be collected in the first place, it is often necessary to provide interesting features and services. Finally, a lot of people simply don’t care – if you don’t believe me just look at the stuff they post on Facebook.

The point where this development could have been stopped is long gone, so we just have to live with this permanent data collection that will become rather worse than better. But we still have two brave new worlds this could lead to: Number one is the Orwellian one (with a dash of Kafka) where faceless governments and corporations hold your data and know more about yourself than you do. Your access to it is heavily restricted, if you have one at all. You might not even be aware of any type of data collection going on.
World #2 still has these huge amounts of personal data but they’re the people’s. They belong to the producers and not the collectors and each person has free access to and control over their own data. They created them after all, so they’re their intellectual property. Companies might want to use them but have to license them first.

I think that the development towards permanent data collection cannot be stopped, but we should at least strive to reach a future that’s more like world #2 than #1. I think that the reactions to the iOS scandal give us an idea how to go about that. So, why were most people shocked and some giddy about this whole thing?

People care deeply about their data, which is a good sign and not a given in a world where seemingly every bowel movement is documented on Facebook. But this care is in a very abstract sense: People want this involuntary data collection to stop and want to see the data deleted once they see something about it on TV. Most people don’t care about having access to it, they just want to keep it from happening (which might be hard as we’ve seen). Interestingly enough, they often don’t mind this collecting when they get something from it: Some companies add another service layer to this data to recommend items (Amazon) or build playlists (Apple Genius) which suddenly makes permanent tracking OK. Also, access to the ‘raw’ data is then no longer necessary.
Yet, this demand for raw data is the only thing that is keeping us from a future where all personal data is in the hands of the state or private companies. It is therefore necessary that a lot of people demand access to that, not only a couple of geeks (sorry), as these stakeholders will only listen to large demand (and maybe even make a business out of it). The main problem is that regular people are usually not tech-savvy enough to do something with data which is why they don’t care for it and which is completely understandable: Imagine having access to the most expensive sports car but no idea how to drive it. The techie crowd, however, became quite excited about the iOS data and even started collecting these location histories when the latest Apple patch deleted them.

Therefore (and here’s my call to arms): We as researchers and practitioners have to create this demand and need in people for their personal data. We have to give them the tools to actually do something interesting with their data and be able to do that without learning to code. Visualizations for personal data and the various lifelogging services are first steps into this direction. And even Facebook lets you by now download your complete profile.
But I want more: I want to download my reading history with timestamps when I turned the virtual page from my Kindle, I want to see how much time I spent at every single webpage I visited (and where these weird recommendations come from, Amazon), I want to know why my credit card company thinks i wouldn’t buy this or that and, yes, I want my smartphone to collect information on my whereabouts and I want it to give it to me!

All of that will only happen if there’s enough demand for raw data. So keep on building these amazing visualizations and tools and give them to the people. Make them as convenient to use as a TV and as beautiful as a coffee-table book. Let people learn about themselves and use this data for fascinating new forms of communication. And make sure that the only future silo that keeps your most personal information belongs to you and no one else.

tl;dr: create demand for raw personal data by giving people neat tools for it.

In order to make sense of huge collections of music the first idea that comes to people’s minds is often: Display all of these songs by using some minimalistic graphical abstraction (so they can fit on the screen). This approach is not only commonly taken in research but is by now also available in commercial products and apps where maps are commonly used as a way to one-up the boring old lists.

	Oooh, so pretty!

Just to be clear: I’m not against music maps per se – they’re great for discovery. The recent Aweditorium for iPad, for example, lets you find new indie music in a playful way and is really, really neat. The problems start, however, once you decide to use a map for anything beyond discovery: Organizing music collections, building playlists and so on.

My first problem with this approach is how music is represented in one’s mind: Maps are often used for visualizing personal music collections that, as the name implies, also contain all kinds of invisible personal stories stuck to the songs. While James Yuill and The Airborne Toxic Event might not be very similar music-wise, they have a certain connection in my mind as I kept thrashing their albums in the same summer. However, the layout of such a map is usually based on similarity, either content- or metadata-wise, and these algorithms can’t reflect that. Using a personal listening history as basis for the map could, for example, work much better (full disclosure: LastHistory was developed at our research group. Here’s the research paper (PDF)).

The second problem is that of visual representation of songs. Finding a specific artist or even title on a map is often close to impossible as there are mostly no clues for navigation (musical genre and sometimes even similarity are vague and often based on personal taste). So the only option for finding something is visually scanning the whole map. And depending on the representation of songs that can take a while. This scanning becomes more and more difficult the more abstract the representation and the more interaction is required: While it’s possible to find a song when all titles are shown with their album cover art or a photo of the artist, locating a song in a field of colored circles requires much more effort and finding it on a musical “terrain” is practically impossible. Aside from the visual representation, interactivity can also hinder this effort: requiring people to tap on circles to learn about their true nature (a.k.a. title and artist) will ensure that you only keep the most obsessive-compulsive part of your audience.

	Quick! Find Madonna!

Such a search is finally made even more frustrating when the app has no capabilities for searching or filtering. While text-based direct search might not be the perfect way to look for music (Query by humming comes to mind) it’s better than visually scanning the whole map. Having a way to filter this information by user-defined criteria (genre, lyrics, tags, release date and the rest of all that meta-data) is even better to let people find what they are looking for.

The most important rule (which is true for all map-based representations): If you absolutely have to use this abstraction MAKE SURE THAT THE MAP DOES NOT CHANGE! Humans are very good at remembering spatial relationships (you could probably tell me without looking how all these things in your apartment are arranged. See also Roman Room memorization technique) and user interfaces that tap into this ability should also support it. Once the map changes all of these intricately learned relationships are useless. Point in case: Aweditorium. I remembered hearing interesting Danish experimental pop there (it was Oh No Ono by the way) which was somewhere in the upper right corner of the map. But of course, once I restarted the app the whole map got reshuffled and I couldn’t find it. Why have a map if the spatial relationships are meaningless anyway? I have similar gripes with the actually pretty great Kindle app for the iPad, that re-calculates the layout of the text – no, not only every time the font size is changed – but every time the app is restarted! Again, remembering that some interesting tidbit of text is on the lower part of a page isn’t any use.

To sum up, here’s the list of map rules in short:

• Use easily memorizable/recognizable representations for songs. Cover art or artist photos work well. Try to think of visual landmarks to support visual search.
• Provide a text-based search for specific titles/artists and filters for genres.
• Never ever change the layout of the map! If you have to change it to add or remove songs try to keep it as close to the original as possible and inform the user about it using animations.