Humans of Data

“The things that made me interested in data twenty-five years ago are the same things that make me interested in it now.  It’s the way structures and narratives are going to define our culture and who we are.  I was interested initially in the historical perspective: how is data going to change the subtlety about how we understand past cultures?  And how future generations are going to access data, manipulate it and study it.  Very few people were interested in that, and that was exciting.  Now I’m a bit scared of it.  It’s increasingly clear that we can use data in a contemporary context for social evils.

Data – and the systems that store it – can be ugly, but they can also be beautiful.  Some day, people are going to be interested in the beauty of system architectures and the beauty of database design – or the ugliness.  It’s like studying a Gothic cathedral or a contemporary city: the architecture defines how we feel and the narrative that plays out in our lives.

I see emerging possibilities in machine learning, in the blockchain and in other areas.  I share an understanding of the risks of data.  And I believe that what kind of research you actually do – and how you enable others to be creative to do things – these are equally important.  More focus needs to go on developing the community and helping our colleagues to progress and excel.  The ways forward in digital curation and data presentation are very much going to come from human collaboration and not from one lone person who’s thought of a technical solution that no-one has thought of before.  This is not the field of lone scholars – this is the field of general community effort.”

“I’m a data scientist. One needs to be aware that data is important to do science. But it comes with lots of issues, to make good quality results. It’s not just about collecting data and using it. Any data-driven solutions you try to develop, you need to understand the users and their different roles. A lot of work on the data is not just about the technology. It’s also about the social aspects. It’s not just about setting up a system and saying, ‘The scientists will use it.’

What I learned from my computing science experience is that every domain is different. If you want to develop a computing solution for a domain, you need to get familiar with the user environment, workflows, best practices, language and so on, and it’s important to get familiar with this before coming up with a solution. Every domain I have worked in, I had to get familiar with the practice, so the users will see the computing solution as integrated, and they will use it.”

“I‘m an ethnographer.  Well, I’m not only an ethnographer, but the heftiest part of my dissertation work was ethnography.  And as one who also has rigorous training in engineering and other natural science fields – I was surprised at how much of my research design required transformational change.

While I did the research – as I collected data – I changed what my hypotheses were, what I’d use the data for and what outcomes I’d obtain from analysing the data.  It was simultaneously confusing and exciting, but eventually I was much prouder of the outcomes than I would have been had I stuck to the plan.

It was a lesson in valuing methodological adaptation and change.  As researchers, we don’t know everything, and more people in science should have, and value, that experience.”