INTERVIEW WITH NADIEH BREMER

Nadieh Bremer (NL) is a data visualisation designer. Her focus is on the more creative and experimental aspects of data visualisation. Examples of her work can be found on her website. She received the Rising Star award at the Information is Beautiful Awards in 2016 and is a popular speaker at international conferences about data visualisation. Nadieh: “it’s very important to understand data in itself and to know how to handle and work with data”.

Graphic Hunters: What is the power of a good visualization? In what way can a visualization help to understand or to communicate information?
Nadieh Bremer: Giving a person new insights; to highlight trends, to lay bear complex interactions and networks, to change opinions and open up previously hidden worlds. Either in a fraction of the time that reading a text, trying to explain it, would take, or even to give the insights in the first place if that person would not have want to “read” about it.

I have read that someone has to understand a visualization within seconds, others say visualizations should leave something to explore. What is your opinion on this?
I think that it depends on the setting, goal and audience. If it’s a visual that a manager looks at daily to see if all is OK, then yes a visual that can be interpreted in 5 seconds would be very helpful. But if you want to show your audience the complexity and diversity of a topic, and this can be any topic, such as olympic medal winners, royal family trees or the winds across our Earth, I’m all for the visualizations that might take a little more time to understand. But once you do a whole world of potential stories, insights and facts can be found from these visuals that can keep the interested reader occupied for half an hour easily. Personally, I’m much more interested in creating visuals of the latter kind.

What is the first most important question one should ask before starting visualizing the data?
I always try to understand the goal of the visualization. Should the audience learn something from this visual? If yes, what exactly should they learn? Or should they be acted to do something? Or something else? Don’t just visualize data for visualizations sake, you don’t start with the data, but with an understanding of the goal.

What tools are important to learn when you want to work with visualizations?  
I feel that it’s very important to understand data in itself and to know how to handle and work with data. You need to be able to wrangle your data into different shapes, by pivotting, aggregating, filtering and more, so you can investigate different ‘sides’ of the same data, to see what insights might be hidden. I personally use R as my go-to data preparation tool. But, depending on the size of the data, Excel can work just fine. Or SQL, Python, SAS, whatever lets you turn the data into different forms. In terms of visualization itself, I try to think more tool agnostic and say that it’s more important to understand best practices; not to use a rainbow scale for continuous data, not to make your gridlines overly visible, such things. Once you know these ways to best visualize the data, you’d be surprised how many tools can be ‘hacked’/used in inventive ways to get the visual form you want. But for the truly custom made visuals, you probably need to learn a tool that gives you very low-level control on what gets placed on the screen, such as d3.js, Processing or Adobe Illustrator.

What is the role of design in a visualization?
Design can help in a myriad of ways, but my favorites are: Design can help to understand how to get the insights across most efficiently, how to subdue non-data parts and highlight the important sections for example. Design can help to make the overall hierarchy, layout of the visual more apparent, that it intuitively helps the audience to digest the information in the right order. And design can help to make the overal visual appear more attractive and engaging which will make more readers interested and intrigued to investigate the visual.

What’s the biggest mistake often made in visualizations?
Just using the first chart form that the creator could think off, without considering if that would be the most effective way to visualize the insights. Or to even consider if that first chart form conveys the insights at all.

If you could choose one visualization that you can put in a frame and hang in your livingroom, which visualization would you choose and why?
O, this is a difficult one! I would prefer to get a subscription to the “data visualization art gallery” and get a rotating selection of practically anything that Jan Willem Tulp, Nicholas Rougeux, Stefanie Posavec, Inconvergent and Matt DesLauriers make! I’m a big sucker for anything that moves towards generative/data art.

What lessons do you want the participants to take home for the training?
I really hope that the next time that the participants will create a visualization, they will sit back and really think about what is needed for the goal of the visual and the data. To be guided by that and not what options the tool gives them. Not to go straight for the bar chart, but to really consider the most effective chart form (and that they have learned how to go beyond these “standard” charts and find different and more effective chart forms).