Tech+Art Podcast: Shirley Wu, Software Engineer & Data Visualization Freelancer


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Welcome to the new Tech+Art Podcast!

Join us on this adventure as we meet & speak with: artists, makers, researchers, designers and creators from all background and fields.

Our objective is to understand their creative perspective, dive into their workflow & creative process, be inspired by new ideas and their work – and stay one step ahead of cutting-edge industry developments.

"I think there’s a typical design process that we have in our minds [...] because datasets can be huge, it’s really hard to design without that data. "


In this episode, we’re chatting with Shirley Wu, an award-winning creative focused on data-driven art and visualizations.

Shirley joins us to share her story, her insight into the data visualization process, how the industry has evolved and been shaped as it continues to grow, her collaboration on the DataSketch|es project, and much more!

Question 1: How have you seen the types of the projects that you were able to do with some of this technology or that you are tackling? How has that changed or evolved?

[ 7:14 ] – A lot of what we were trying to do was just figure out the technology. […] I personally would get a lot of enjoyment from just the technical achievement of implementing something new on top of that or something new beyond what the libraries provided. And I think a lot of my peers – I saw a lot of my peers be impressed with any projects that pushed the boundaries of the technology.

[ 8:00 ] – So I think a lot of the focus used to be a lot on the technology and charting […] and then, I think maybe it reached maturity […] around 2015/2016 […] because by then, there had been so much progress that had been made, that it became less and less about the technical impressiveness of a project or of a visualization and it became more about how we paid attention to the end user.

[ 8:39 ] – It sort of became like, even if it’s a very simple visualization, maybe it’s not technically impressive or pushing any boundaries – does it convey a clear, moving message to the end user or the end reader? And I think it became more about trying to figure out the design of it, the story of it, or how it fits into different industries.

Question 2: What is your creative process or workflow like?

[ 11:02 ] – We typically tend to explain Data Visualization projects in these two buckets, or a linear scale between on one end, I think there’s what I described as Business Analytics and that’s maybe a little bit more dashboard-y, it’s very exploratory, and the intent of it is for some group of stakeholders usually within the business to explore the dataset and help them make informed decisions – and I feel like that’s on one end. The other end is the data-driven journalism. It’s usually like a more static dataset that the journalists have already explored and they want to tell a very specific story with it and they want to tell it to a very general audience. I think it’s a gradient along there, but I think those are the two opposite sides.

[ 12:05 ] – The way I would approach both would be very different. […] And this has taken me years to figure out what kind of works for me because I think Data Visualization can be quite hard in that to create a full project you need to have a lot of different skillsets. So you need to be able to do the data analysis, you need to be able to do the design and the prototyping – and the design is usually beyond what a design major or a curriculum might teach […] a lot of times the traditional design classes might not have as much on information design, and Data Visualization is all about information design. And then there’s the coding part of it and that could be for the web or for…. And then you need to figure out how to write and tell a good story. So it’s like a lot of different skillsets […]

[ 13:02 ] – And so over the years, I’ve kind of figured out a process that helps me – it’s not a very linear process, but usually I start with the data analysis. And for me, that usually involves looking at the dataset, trying to formulate some questions of my curiosities, and then putting that into simple charting libraries to visually see if my hypotheses are correct. And then once I have some good, interesting things pulled out I’ll kind of try to figure out how to bring that into a design that makes sense.

[ 13:35 ] – […] Because it’s very hard to design something for data, in like a Photoshop/Illustrator mockup or sort of thing, so I’ll usually design with code. So I’ll start prototyping and coding and I’ll start working on the story at the same time…

Question 3: Can you tell us a little bit about that process, or that part of the process after you’ve done the data analysis, what are the tools and how do you do the prototyping at that stage?

[ 16:35 ] – I think there’s a typical design process that we have in our minds that like maybe a designer, working on a website, they might like open up Sketch or they might open up any of the other tools that then lays out different UI components […] because datasets can be huge, it’s really hard to design without that data.

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Question 4: How have you seen the industry evolve?

[ 18:31 ] – […] because Data Visualization is quite a new field, and because making Data Visualization for the web is an even newer field […] almost everybody so far has come from a different field. And only now in the last few years have we seen undergrad and graduate programs pop-up that’s specifically to do with data visualization and in the last few years people can graduate with a masters in data visualization. They have very formal training in data visualization.

[ 19:41 ] – So I actually have this description of Data Visualization, versus Data Art, versus Generative Art. In the sense that Data Visualization has to be very practical. You’re trying to convey information and just like statistics […] it’s very easy to mislead and misinform someone. So Visualization is very practical and I don’t want to say strict – there’s a lot of responsibility to make sure that you’re communicating as accurately as possible. Whereas Data Art is the practice of taking a dataset and using that to generate something, but for an artistic purpose – and then you don’t have to worry as much about making sure that everything is correct. It’s more for the beauty of it. And then Generative Art, I think of as very similar, but instead of using a specific dataset, you’re using randomly generated numbers.

Question 5: Can you tell us what’s next for you? Are there any projects or focuses that you’re able to share?

[ 23:24 ] – For the last few years, I’ve been really obsessed with trying to figure out how to bring my visualizations out into the physical world. […] I’ve been dreaming of installations that people can walk through and be immersed in and really have it be a lasting memory.

[ 25:02 ] – And I think that’s a really big, new challenge for me, not so much on the technical or creative side, but on the business side. In the sense that, I’ve been, for the last four years of freelancing, I’ve been working solo; because I know how to manage myself. And I’ve been very scared about forming a team to work with […] and I’ve been slowly trying to get over that anxiety. And so my goal for the next few years, is to get to a point where I have an awesome team and partners to work with on bigger scale productions. That’s not next year goal – that’s the next 5 or 10 years sort of goals.

Question 6: What’s a piece of advice that you would share with a younger version of yourself?

[ 27:20 ] – I think the final one is a very sappy one – just believe in myself. In the sense that, I think I’ve been kind of raised to be like ‘oh, I’m just one person, what can I even do? This data visualization, this little thing I do it’s just a hobby of mine, it’s really not a big deal’. And I think in the last few years, I’ve really worked on that and been like: ‘No. I might have grown up with society telling me that as a girl, I’m not going to amount to much. But I’m a very skilled woman, with something really powerful I can offer and I should really believe in that’.