Data Visualisation Tools and Trends to Watch: An Interview with Datavisualization.ch
Datavisualization.ch has grown to be one of the most relevant and popular websites in Europe dedicated to developments in the field of data visualisation. Following trends and reviewing technologies, it is a useful information source both for those interested in entering the field of data journalism, and for those data journalists who want to be up to speed with developments in the field of data visualisation. We interviewed Benjamin Wiederkehr, founding partner of datavisualization.ch and of the design studio Interactive Things, to pick his brains about everything a data journalist should know about tools, trends, dos and don’ts in data visualisation.
How was datavisualization.ch born?
I started the website while writing my bachelor’s thesis in interactive design at Zurich University, back in 2008. Together with Christian Siegrist [co-founder of datavisualization.ch and Interactive Things, M.B.], with whom I studied, we wanted to have a place to document both our dialectical progress and our applied or practical progress. By the end of 2008, Christian wanted to broaden the website’s scope and give it a wider reach. It would no longer be only about our research, but it would also serve to document work that we see being published by other practitioners, and to highlight other things, which we consider important for the community. It would also become a space where we'd gather our own thoughts, while actually learning about information visualization and allowing others to follow, comment, and discuss.
What has changed in data visualisation since you started the website?
Generally speaking, the greatest change concerns the very popularity of it, the breadth of the topic, and the number of people involved. There is a far greater number of businesses and industries that use data visualisation as a way to analyse, evaluate, and communicate their information. When we started, the publication frequency of new material was quite low, while, nowadays, new data visualisation projects are published every other day.
Data journalism, in particular, has seen a very steep curve of increased attention and output. This is particularly true in the case of interactive data visualisation, I should say, such as the work done by The Guardian or by The New York Times, which are fully engaging interactive media as a way to support their articles, as well as to attract increased interest from the general public.
The team at datavisualization.ch and Interactive Things
What are the hottest trends in data visualisation today?
A few things come to mind. Technologically speaking, this is a very interesting time for data visualisation, because the tools that allow people to create visualisations, communicate information, and start telling stories with data, have changed dramatically over the past years. There are a few tools available that allow you to publish data in a very engaging way, while also doing justice to the data itself. I’m thinking about tools like Tableau and Tableau Public, but also the inclusion of charting possibilities within Google Docs - a development that has helped a lot of people to get started without a lot of programming knowledge.
There are also new trends from a visualisation perspective, and by that I refer to the work done with real-time data, a data feed that is ever changing as a source for data visualisation, like a Twitter feed, for example.
Another trend involves letting the users create visualisations and share their customised view on a dataset with their peers or friends. The involvement, the dialogue, between the data and the creator of the visualisation, on the one hand, and the users themselves, on the other hand, is gaining popularity.
What are your favourite data visualisation tools for journalists at the moment?
My absolute favourite is the D3 library, which I mentioned earlier. It is a flexible tool and the community around it is very active, which leads me to believe it is here to stay. But there are other tools that build on D3, like Datawrapper. It is actually useful for journalists who want to publish visualisations based on data they already have, making it easy for them to create D3 visualisations.
Tableau is another, rather sophisticated way to publish visualisations, especially if you put together a collection of visualisations. The results can be very nice. We rely heavily on Tableau in our studio, as a means to help us understand the data at the very early stages of our work, to explore it, before we move into more customised productions.
We sometimes use Google Refine, now called Open Refine. It’s not a visualisation tool, but often visualisation work involves refining and understanding the data. Open Refine helps us to get a sense of the texture and structure of the datasets we are working with.
IBM created a visualisation tool called Many Eyes. It is a web application that allows you to upload a dataset and to explore it with many different pre-built visualisation techniques. It goes a bit beyond the possibilities given by Tableau, but it is also a little bit more restricted when it comes to combining different charts into full-fledged dashboards. But still, it is definitely interesting.
Quadrigram is another recent, interesting tool. It is the new version of a tool that has been created a few years back, called Impure. It is an application built in Flash, and maybe for that reason it was not received as enthusiastically as it should have been. But the tool itself is very interesting. It is a sort of prototyping tool, or a production tool, for working with data. In a broader sense, it allows you to connect data sources of different kinds, enabling you to transform the data and to visualise it in the same application. You can do it in a very sophisticated way, without programming code. It is a very interesting tool, I highly recommend giving it a try.
Which tools are more suitable for beginners, and which for veterans?
The best ones for beginners are Tableau Public and Many Eyes. These are straightforward tools to start playing with. And Datawrapper, definitely - it guides you through the process, and that is something I find interesting.
Another one is MapBox - a mapping application. Mapping applications are very popular. With a very simple system and a simple user interface, MapBox allows users to upload data, to map it in many different ways, and to create maps that tell the story. It is a very accessible tool for beginners.
As far as the more advanced tools are concerned, D3 is currently the framework that allows you to do very customised and sophisticated things for the web. There is also Processing, which is still commonly used, though not so much for the web. But if we work on projections, or on touch screen interfaces that are large scale, we may use Processing, because it is built with Java and therefore has a lot of advantages over regular web technology.
And then there’s also Gephi, which I would also recommend for advanced users, specifically for networked visualisations - social networks, networks within companies, or between companies.
What distinguishes good data visualisation from bad data visualisation in journalism?
Good visualisation really supports the story. A bad visualisation lives on its own, outside the narration and the context that it originated from. If the story is pre-written and the visualisation is sort of separate, it is not put to good use.
I would like to see deeply interconnected journalism, where the written word, the interactive visualisations, images, and other multimedia materials are put together to form a narrative that really creates a piece of journalism. I would love to see more of this interplay between different kinds of media. If we manage to make a guided interactive experience for users, we will be able to tell very interesting and very engaging stories.
Do you think the popularity of data visualisation will ensure a more prominent role for graphic designers in the newsroom?
I would expect to see more interdisciplinary teams that include journalists, designers, and developers, like a hybrid of two or three disciplines. There are tasks in the workflow of data journalism that require bringing together different types of expertise to create something of high quality. I hope, therefore, to see a closer collaboration between journalists, designers and developers.
In the traditional approach, a journalist does the research and writes the story, to which a few images are later added. If you want to work on interesting visualisations that are fully embedded in the story, you need to talk early on with the visualisation designers or developers, discuss with them the visualisation method, how to represent the data, and how to combine it with the textual parts of the article.
We are still far from having a fully satisfying method for reaching an optimal interaction. A lot of people are currently exploring the means to get there, and how to best engage and attract users towards stories that are told through interactive visualisation.
Who are trendsetters we should monitor in the future?
The usual suspects immediately come to mind: The New York Times constantly comes up with good examples, which amaze me, especially when I get a peek at the work done behind the scenes and their setup. They have built a fascinating team of very competent people that seem to collaborate perfectly.
The Guardian also does an incredible job quickly producing visualisations that support their stories. I also recommend looking at the Malofiej Awards that reward the best people in information design. Interactive visualisation for journalistic purposes is always a part of it. So looking at what Malofiej rewards is a good starting point for finding the next best thing. It is also a good place to watch last year’s recap of data visualisation production.
I also like to check conferences, like the see conference that will take place in Germany, in April, or the Eyeo festival in Minneapolis, among others. I like to attend, when I can, and hear the speakers talk about how they work. Visualisation projects are published and are easy to find. But learning about the process that led to these results - this is what I’m most passionate about.
This interview was edited and condensed for clarity.