In order to sprinkle some star dust into the contents of my book I've been doing a few interviews with various professionals from data visualisation and related fields. These people span the spectrum of industries, backgrounds, roles and perspectives. I gave each interviewee a selection of questions from which to choose six to respond. This latest interview is with Santiago Ortiz, Head at Moebio Labs. Thank you, Santiago!


Q1 | What was your entry point into the field: From what education/career background did you transition into the world of data visualisation/infographics?

A1 | As a kid (at least since I was 5) I had a deep interest in maps. I tried to create my own maps of imagined places. My parents report I said “I want to be a cartographer” when I was 5. Also as a kid, but later, I started coding. As many, I developed my own simple video games, but also played just with graphs driven by simple algorithms. Later fractals and cellular automata blew my mind, and then genetic algorithms. I studied mathematics while continue using code as creative tool. My math thesis was a model for evolution and genetic algorithms. I have to say that at that time I kind of hated applied maths, so statistics didn’t interested me on the least. That came later.

In 1999 I co-founded a web design company in Colombia, called Moebio. Although websites tended to be very conventional, I continued being interesting in the creative capabilities of the digital medium, specially the hyper connected medium of internet. Soon, I started using data to drive visual outcomes. In 2003, while living in Spain, working in collaboration with an awesome Protein Laboratory in Madrid, ran by Alfonso Valencia that was doing pioneer research on data crawling and data crowdsourcing for molecular biology data, we created Gnom, the first serious (although pretty much experimental) data visualization project I was involved.

In 2005 I co-founded Bestiario, a company devoted to interactive data visualization (I left the company in 2012). Now I lead Moebio Labs, a small data consultancy team. We combine interactive data visualization with data science.

Q2 | What is the single best piece of advice you have been given, have heard or have formed yourself that you would be keen to pass on to someone getting started in a data visualisation/infographics-related discipline?

A2 | You should pursuit a career on data visualization only if you’re more interested in what you visualize than in data visualization itself. As a corollary, for each datavis book you read, you should read other 9 about a variety of other subjects such as psychology, economics, mathematics, genetics, sociology, statistics… (my personal rate is actually 1/30).

Q3 | When you begin working on a visualisation task/project, typically, what is the first thing you do?

A3 | I do two things in parallel: I explore the data focusing on structure, with a domain-agnostic approach; and I also talk with the client a lot: I make lots of questions, I try to understand client’s landscape of pains and opportunities, its expectations towards the data and project outcomes, the client's general major problems and challenges.

Q4 | At the start of a design process we are often consumed by different ideas and mental concepts about what a project ‘could’ look like. How do you maintain the discipline to recognise when a concept is not fit for purpose (for the data, analysis or subject you are ultimately pursuing)? How should one balance experimentation with the pragmatism of what is a data driven and often statistically influenced process?

A4 | “At the start of a design process we are often consumed by different ideas and mental concepts about what a project ‘could’ look like”... Actually I don’t let that happen. As mentioned before I focus on structural exploration in one side and on the reality and the landscape of opportunities in the other. Then we start playing (it’s usually me, a data scientist and a visualization designer+developer), building fast prototypes (we create our own tools for that). By combining and exploring options, forking, pivoting, trying… we end up with good candidates, that we execute and wrap into something we can send to the client. That’s the result of the first iteration. Then it comes lot of conversations with the client and then the tests… tests by us and the client. We talk again, we take into consideration client’s feedback guided by questions. We find what is useful in the first iteration, what is really starting to give some insights to the client, what helps it to make faster and better decisions, etc… Next iteration will then expand what works, remove what doesn’t, and add more experimental approaches to be tested. Typically in the fourth iteration or so the client has a set of tools, some training and gained knowledge that allows it to take real advantage of the data. So, again, we try not to impose any early ideas of what the result will look like, because that will emerge from the process. In a nutshell we first activate data curiosity, client curiosity, and then visual imagination in parallel with experimentation.

Q5 | Beyond the world of infographics/visualisation what other disciplines/subject areas/hobbies/interests do you feel introduce valuable new ingredients and inspire ongoing refinement of your techniques?

A5 | These are some other fields of knowledge that we consider very important, because they provide ideas and tools for the stuff we build, or because they are typical sources of information we might end up analyzing and visualizing... in most of the cases it’s both! We also have to deal often with experts on those fields.

  • general culture and cosmopolitan culture (having lived in different countries, know languages, know different types of industries, knowing diverse people… are great tools to be fast and sensible when it comes to understand client’s reality, motivations, necessities and opportunities)
  • scientific culture, including knowing the past and being aware of the present developments and trends...specially genetics, the queen of data among sciences
  • data science (data science inspires our data visualization approach and methodology more than data visualization does)
  • computer science, good coding
  • network theory
  • creative coding
  • product development
  • psychology, specially cognitive
  • math, specially geometry, linear algebra, topology, calculus and yes, statistics
  • UX, usability
  • storytelling
  • sustainability (economy, energy, geopolitics, ecology...)

Of course neither of us has deep knowledge on all those fields. We are dilettantes, but we all spend a big deal of time studying and reading.

Q6 | In your experience, what has proven to be the most valuable approach to evaluating your work (post completion) or what methods have you seen others taken that you felt were especially smart? There will always be a balance between effort and reward but very keen to learn of any specific effective tactics.

A6 | Evaluation of work is key for us, as previously explained. Evaluation means improving, it’s a constructive concept not a passive one. Evaluation is also, in our case, a collaborative process, something with do along with the client. We use concepts and methodologies borrowed from data science. We test against reality, we lean towards real return. Although the perceptual, psychological and usability aspects are certainly important and are also assessed, we don’t share the academic general approach to evaluation, that tend to focus on those reductive aspects. We have a holistic approach in which being able to read or memorize values from a chart is really not so important; instead we aim to sense making, insight, complexity of tasks, capability to inform when it comes to make decisions, capability to provide vision, and, in certain projects that might contain more sophisticated analysis, including prediction, the accuracy and value of specific answers.


Header image taken from Santiago's portfolio of incredible work.