“Data science allows us to reveal the beauty of the world around us.”
An interview with shiftN colleague Santiago Ortiz
We are launching a new series of contributions to our shiftN Papers. Short, informal conversations with members of our team. Here goes Santiago Ortiz, data scientist. We have been collaborating with Santiago for over ten years. Of Colombian descent, he recently moved with his family from the Bay Area to Europe. The geographical proximity makes it even easier to work together on data-rich projects.
Santiago, perhaps we can start by retracing how you came to be a data scientist, if that is how you like to be presented?
Yes, I like presenting myself as a data scientist. Data are a window on our world and data science allow us to reveal patterns we may not be able to detect with the naked eye. Early on I remember working on two data sets in parallel: one one interactions between genes, and another related to religious festivals in Colombia. Two snapshots from very different realities which I ended up visualising in a similar way, as networks. That made me fall in love with data science. It’s all about algorithms, patterns, aesthetics. It’s about building beautiful things. As a young kid I loved writing code that made the computer draw things. Later I studied mathematics. I wasn’t interested in applied maths, though. I was attracted by the beauty of logical structures. But through data I learned to love applied mathematics as well.
Is it a real science?
Good question. As data scientists we focus on localised, bounded problems in which we try to find patterns, which we also might extrapolate to the future for predictions. Our focus is pragmatic; we don’t have the universalist aspirations of scientists to find out ‘the truth’. Data science is perhaps more a technology than a science. Having said that, we approach these problems in a rigorous way. Other people can take our work, replicate it and build on it, and that seems to me to be fundamental for science. I like the beauty of familiarising ourselves with a very particular problems through the filter of data. Data are everywhere, pervade every aspect of our lives. There is a little book Problem Solving 101, written by Ken Watanabe as a primer to data science for kids. He starts from very mundane problems. A group of friends form a band and they are asking themselves what day would be best to organise a concert. So let’s gather some data, let’s use some numbers to figure this out! And that’s it, that’s data science. And yet, the more of these localised puzzles one studies, the more one sees connections and patterns. And so you start to learn about the universe. It’s like Gabriel Garcia Marquez’ great novel One Hundred Years of Solitude. A story about a small community lost in the jungle. And yet at the same time it reflect a universal message. It’s the story about modernity. Data science is similar. We explore what is under our nose and yet we learn about and interact with the universe. So let’s say data science is science at a local level.
Is there anything distinctively South American in your approach?
Ha, that’s an interesting question. I think that I connect particularly well to the story-telling vibe that is very present in South American culture. Data are raw materials for storytelling. We make sense of the world through stories. And stories are by definition heterogeneous mixtures of all kinds of things. I believe that due to my background I am temperamentally predisposed to like that kind of tropical mix and nonlinearity.
Who or what has been particularly inspirational for you?
I mix diverse influences. In the particular field of data I would single out people like Claude Shannon and Umberto Maturana, who proposed influential ideas in information theory and cybernetics. In mathematics I like John Conway, because he plays, and while playing builds beautiful things. His most popular creation is The Game of Life. Although he personally wasn’t very fond of his own creation, for me it has been a real inspiration. Talking about life: I really love the work of Lynn Margulis, who demonstrated the relevance of cooperation and symbiosis in biology and its role in the genesis of animal and vegetal cells. And then there is Jorge Luis Borges, whom I like because he wrote so imaginatively about paradoxes and singularities.
Is there any project or assignment you have been particularly fond of?
I still like the Ross Spiral, developed as an interactive environment to study the curriculum of the Ross School, a private school on the US East Coast. I enjoyed the team effort, that brought in many different perspectives. I also love that it is available online, for everyone to see. It has been there for many years and continues to draw a lot of visitors: students, parents, administrators, geeks. A bit unusual was that we had to start from a strong geometric archetype: the spiral. So we had to fit the available data to this geometry. Usually we do it the other way around and we let the data suggest the geometry in which they may be visualised.
What makes working with shiftN worthwhile?
Here I also would like to point to the quality of the collaboration. It’s always quality time when working with shiftN. Our conversations are good and deep. There is a feeling of mutual respect. Perhaps it even goes further than that. There is a sense of humanity. Humans and human relationships are primary in the collaboration. Everyone gets to contribute, there is no clear hierarchy. Team members bring in a lot of interesting knowledge and experiences. It’s a learning opportunity. So the quality of collaboration is fantastic.
And then you have the challenges on which we collaborate. shiftN has this strong background in humanities and systems thinking. The projects all make sense, they touch on things that are so critical for our times. Whether it’s food production, integrated healthcare, or community resilience. The focus is always on societal challenges that invite new ways of thinking. It’s a pleasure and privilege to work on this.
Have a look at Santiago’s digital Wunderkammer at moebio.com.
