Behind the Scenes of “The Future of Data Science”: An Interview with Ciera Martinez, Nightingale

Behind the Scenes of “The Future of Data Science”: An Interview with Ciera Martinez, Nightingale

A few months ago I had the pleasure of sitting down with Ciera Martinez to discuss the founding of the project, Data Science by Design, and what they’ve been up to recently. We discussed the process of creating the anthology as well as the role data science plays in society today. 

1. How did the concept behind Data Science x Design come about?

DSxD really began from sending fun Slack messages. In 2020, Sara Stoudt, Valeri Vasquez, and I were working together at the Berkeley Institute for Data Science as researchers. There, we had endless conversations about why we love data work and a lot of the reasons were based on creativity, design thinking, using data as a tool to do good, and connecting with people. We were constantly sending each other Slack messages of links to zines, inspiring data work, and people we admire. What we shared was quite different from the academic and industry view of data science — efficiency, automation, data as oil, and so on — which unfortunately is how most people see the field. We saw the creative side of data science and observed this in the zeitgeist of data topics on the internet, like Twitter. We just wanted to collect and amplify all these voices and to build and create things with them. To elevate the less jargon-y, less academic side of data science. We were fortunate enough to get two grants that supported this vision. We reached out to other like minded people to what eventually became the Leadership team (Sara Stoudt, Valeri Vasquez, Tim Schoof, Lauren Renaud, Natalie O’Shea and I). This all led to the creation of the book, our mini-grant program, and events / gatherings. So now, DSxD is largely shaped by the people who gravitate towards it. People interested in DSxD and the anthologies come from a variety of backgrounds, but I think an underlying thread is that most consider themselves part of multiple disciplines – artist AND researcher, or designer AND scientist.  

2. What is your goal/mission as an organization and for the anthology in particular?

We celebrate the fundamental creativity of data science. We support those who leverage creative mediums, design thinking, and storytelling to convey the practice and insights of data science. We also aim to establish a community dedicated to developing a more open, ethical, and inclusive future for the field.

Our aim is to re-brand data science, so ultimately it attracts more diversity in the types of people who work with data. Data science should be thought about more broadly, bringing society, art, and process into how we view data work.

3. What was the overall process for creating the anthology?

It is outlined pretty well in this infographic we created below. We hold events to get feedback, connect, and inspire. Then the leadership team functions like a yearbook committee putting the book together. We also have mini grants to help people develop their ideas and execute their projects. From there, we get illustrators and designers involved to beautify what everyone submits.

4. Who was involved in creating the anthology? How did you get such a great collection of people together?

At the first event, Creator Conf, the vision for the anthology was refined through discussions at the conference sessions. The people who joined us really shaped the scope of the anthology, from the type of subject matter we were looking for to the type of people to invite to contribute.

As for the contributors, it was a mix of people from the DSxD community and us reaching out to people we were all fan-girling about. We strove to collect unique voices around how data is used and spent the rest of the time elevating and beautifying their vision.

5. What gaps does this anthology address in the conversations happening around data science?

We strive to highlight the design process involved in data work. While aesthetic design principles are one aspect of data work, we explore other design principles and guidelines, like those concerning ethics and function. An underlying concept of everything we work on is “show your work.” We expect transparency with the people behind the data work, not only because there are ethical concerns inhiding the people behind data work (see Data Feminism chapters “Numbers don’t speak for themselves” and “Show your work“), but also because exposing the steps that lead up to the final results is always interesting and inspiring, and many times beautiful.

6. In “Writing a modelers MaNifesto,” the author says, “what I’ve realized is that modeling is also political.” How, if at all, is data science as a field political and why is that something we should keep in mind?

This is an ongoing conversation within our community that strives to articulate this for ourselves and within our work. In our book club, we discuss this regularly in the context of defining what data is in our society. Data is ever present and we are unable to opt out.

Therefore, the omnipresence of data in everyone’s lives forces data science to be political. Where there are people, there is politics.

The issues that society has are reflected in how we handle data and our society. Data is always an abstraction, highly filtered through the people who collect and analyze it. Being a data practitioner requires you to delve into power dynamics in society, because understanding the intricacies of people’ roles in society makes you a better data scientist. Understanding how the data has been touched by people and where your data is coming from greatly informs the quality of your data work and helps you find more accurate patterns. 

7. Word around town is that you are all working on another book, what can you tell us about that?

Yes!!! We are ramping up for another cycle and we are so excited. This next theme is “Our Environment” which is a way for us to delve into understanding the many worlds we occupy and how data fits into these worlds. From our natural world to virtual reality. We are now accepting submissions!

You can buyVolume 1. The Future of Data Science, here.

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