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Miriam Quick is an expert researcher for information visualization. Over the years Miriam’s interest in the world of facts and data has led her to produce a substantial body of work, including research work for BBC, British Council and the famous data journalist David McCandless (Information is Beautiful).
Miriam’s strength lies in her ability to find a compelling story through complex data primed for visual format. She came into data visualization from a somewhat unconventional background: musicology. While working on her PhD thesis on Musicology at King’s College London, she started to analyzed data from sound recordings and visualized results using software, which led her down a professional path of information visualization research ever since.
When not immersed in her research work or pursuing her interest in music and synaesthesia, Miriam tweets from @miriamquick. We interviewed Miriam for her unique perspective on how to conduct research for information visualization.
1. There is so much public data out there to choose it’s simply overwhelming. How do you determine what types of data or information is the most relevant to showcase? Or how do you selectively highlight data that has a story to tell?
Yes, there is a lot of public data out there – although still not always the data you want or need. Choosing the right data to use is about juggling many different priorities depending on the piece you’re researching. You’re asking questions like, firstly, what data is available on this topic? Which data is most relevant, recent, authoritative, trustworthy? And what will make the biggest impact on the reader? What will surprise, enlighten or give the biggest insight?
Also, you choose data that works best within the visual format. Am I working on a narrative-driven piece such as an infographic? In these cases, you’re often looking for a standout statistic or single insight that will frame the story. Or am I working on a data visualisation where the point is to give the reader ways to explore the data for themselves? In these cases it’s all about having an interesting, multifaceted dataset.
2). How do you structure qualitative data?
Good question. One way is to find a quantitative angle on it, for example coding things along a numeric scale and organizing the information that way.
One example of this is an interactive piece called Snake Oil that I worked on with David McCandless of Information is Beautiful. We were trying to sort out which nutritional supplements are actually worth taking for particular health conditions and which are basically a waste of money.
The actual scientific research on this is pretty complex, so to provide an easy way in for the reader we arranged each supplement on a 0-6 scale, where 6 was very strong evidence, 1 was no evidence and 0 was supplements which were actively harmful. (Most of the supplements came out at a1 or 2 by the way – there’s usually very little point in taking them unless you’re deficient in that vitamin or mineral.)
Another way is to find an illustrative device or visual metaphor to hang the piece together. Or simply showing the relationships between things can be a way of structuring qualitative information – think of a Venn diagram, concept map or flowchart. The problem with qualitative data is that it can get wordy, so any way you can find to present it more visually is good. You have to work closely with the designer to make this a success.
3. Do you think about how your data/information will be visually presented as you go through the research? Does that impact how you organize your data?
I do definitely think about it, but in more abstract terms involving structure, hierarchy and amount of information to include rather than thinking ‘this will be an icon’, ‘this will be pink’ etc.
Those are design decisions. You don’t want to over determine the result and restrict the possibilities open to the designer by being too tight with your research. At the same time, it does help to narrow down the sphere of possibilities to some extent at the research stage.
Often I might suggest visualization devices or produce rough sketches. A lot depends on the individual designer you are working with. Some designers like to be presented with lots of raw data and want to make structural decisions themselves. Others really hate it and want you to do the selection and shaping for them! Both are fine with me, really.
Turning boring information into beautiful visuals doesn’t have to be a daunting task. At Visme, we help you use the drag-and-drop visual content tool with the best help content.
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