Buying a car is one of the retail experiences most fraught with friction. As consumers have more and more innovative retail experiences like North Face's Watson powered shopping assistant, Amazon Prime, and Bonobos, they'll have less and less tolerance for the archaic way automotive retail is done. In this webinar, I define the problem and offers some solutions. Spoiler alert: Falcon can help with that :)
My professional background is in print design and data visualization. For many years, I did print production for the World Bank and the UN, as a design contractor. My interest in data visualization came from that experience, but I was also searching for work with more tangible outcomes. This research project started from my interest in data visualization as a practice, but as you’ll see, it’s a few steps removed.
My project is about designing a user experience for big data. If you keep up with tech news, it seems like every other article is about the internet of things. Every 6 months, there’s another leap in processing speeds, allowing us to process the data from those sensors at ever faster speeds. What this means is that we’ll no longer be capturing data and storing it for later, like a really large Excel file. This means that we’re now able to make use of big data in real-time--theoretically. You can’t understand a terabyte of data as it flashes past your eyes, so we need some new way to manage it.
This problem that I just identified also identifies my role here. I started my research thinking of myself as a data visualization designer. That’s a relatively low level role on the value chain, and there are hundreds, maybe thousands of really good data viz designers out there. My role here is as a designer of the process and experience of working with big data.
There’s a significant case to be made for spending quality time on UX for big data. We know that a happy user is more effective, more efficient, more creative, and better at solving problems. With the complexity that big data brings, a happy user is a high priority.
Here is an updated post it array.
I took out less important items, and added a few new things. One element that I've added in is from a topic that turned on a few lightbulbs for me, thanks to Stephen P. Anderson.
The idea is from Montessori method education: that programs or systems can be either a path or a sandbox. A path is a program that forces you down a certain path, and a sandbox is one that invites you to explore and discover. The path approach treats users or students as a vessel to be filled, and a sandbox sees them as a fire to be kindled. It's the difference between 'buy me because I'm the coolest' and 'buy this because I want you to be the coolest'.
One practical application is to under-specify what certain features are for, so that users can define for themselves what that feature means. For example, does a star mean "i'm flagging this for later" or "kudos to you"? He also talks about the ancient Chinese game Go, because it sets up some simple rules and within that framework there are nearly infinite possibilities. A game like Candy Land, on the other hand, has defined start and end points, and everything in between is strictly stage managed. This relates to my desire as a user that an experience give me the tools to make my own experience.
I was pleased to take part in Big data and the City (link to Hebrew page), a workshop hosted by Bezalel's Department of Architecture. The workshop was led by Rooly Eliezerov, President at Gigya. After coming up with a list of potential big data sources (NFC tags, bus pass data, public bike rentals, etc), we were asked to develop a project based on urban-sourced data. Rooly asked that our project be related to the human experience of the city, and not simply an engineering solution.
My partner, Efrat, and I started out by investigating different ways to characterize the neighborhood we both live in, thinking that we could perhaps find some insight in a combination of data sources. We had difficulty getting access to real data sets, so we sent out a questionnaire to our network, as a way of prototyping the kind of data we'd like to access. We came up with data on travel habits, work schedules, family status, food and music preferences, and leisure activities, and mapped a few of these categories. This exercise wasn't particularly successful, and we were left with a pretty map without much interest.
Because we are both practical thinkers, Efrat and I had a hard time resetting our minds toward 'experience' and not just technical solutions. By the middle of the third day, though, we hit upon an interesting idea, and were able to work steadily to build it out by the fourth day.
This follows what I experienced as a painter, and what I suspect every designer/creative experiences during the process of ideation and creation. Every painting I ever did went like this:
Stage 1: I have an exciting idea that I'd like to investigate.
Stage 2. I'm building the foundation for this idea.
Stage 3: I'm a horrible painter and this is a disgusting painting and I should probably quit.
Stage 4: This painting is a tiny bit less horrible than yesterday.
Stage 5: This might even be worthwhile
Stage 6: Triumph and pride and relief
Success is often about just showing up everyday--which is not a small task. I wasn't a successful painter, because I couldn't stand to show up every day and struggle. Design isn't any easier for me, but I'm motivated by a more tangible sense of value at the end of the rainbow.
Let’s agree that interaction is essential to exploratory data analysis. But big data imposes huge technical challenges for real-time response rates. This is a problem for programmers.
Visualizing every data point leads to over-plotting and visual clutter, which can overwhelm users’ cognitive capacity. This is a problem for the designer. What are options to resolve this problem?
Reducing the data to smaller, derived data, makes it more manageable. Strategies include:
Source: Heer, Jeffrey, Biye Jiang and Zhicheng Liu. “imMens: Realtime Visual Querying of Big Data.”
I'm a Washington, DC native, and I live in Jerusalem, Israel with my husband. I started out as a painter, but craved more practical pursuits, so I found my way to data visualization design. For many years, I designed and produced statistical publications for NGOs like the UN and World Bank. I'm currently in the M.Des Design Management program at Bezalel Academy of Art and Design.