Which types of data visualisation do you work with?
I use advanced visualisations, which are often interactive, animated graphics that I develop in my own program, to carry out research into large volumes of data, understand AI models and communicate insights and opportunities. At Seerefine we are working with web- and smartphone-based visualisation services that demonstrate interactively how a business works. This makes it possible to take measures and follow up on them and to increase awareness and introduce improvements for all employees. We also create a lot of targeted visualisations and animations for specific purposes, often to arouse interest and curiosity.
What challenges do you think are involved in the visualisation of data?
The main challenge today is to exploit the strengths of visualisation. If you talk about data visualisation, many people think of dashboards with diagrams, click functions and tables. At the same time, we have brains that can easily process hugely complex visual connections and computers with graphics cards that can generate highly advanced images and animations from large volumes of data. There is often no link between these two things – how can we make information attractive, easily intelligible and relevant? I think that there are a lot of good examples to be found in the games industry of what we should expect when we think of modern data visualisation.
In addition, there are, of course, all the classic challenges relating to communication: trustworthiness, interest, objectivity, emotions etc. Visualisation often reinforces the effects. You just need to take a look at all the maps being produced at the moment showing the spread of the coronavirus. What are people aiming to achieve by using the colour red on a dark background? I am thinking among other things of the Johns Hopkins Covid-19 map.
What type of opportunities can visualisation open up?
I believe that visualisation, if used correctly, can give us better insights into large complex connections. Our senses and our brains are extremely good at understanding high levels of complexity. In a world where big data has become widely accepted, I think that visualisation will continue to develop and become the most powerful tool we have for investigating new opportunities.
If you had to divide data visualisation into different categories, what would they be?
Visualisation is obviously a separate area in itself and there are already several well-established approaches. I think an important distinction, and also a question you should ask yourself when you look at a visualisation, is whether the purpose of the visualisation is to put forward a thesis and present the arguments for a particular conclusion or to open up new understanding and discussion. In the world of science and research, I often use visualisations to better understand whether there are interesting patterns in the data that lead to an ongoing statistical evaluation. In other words, this involves developing hypotheses. In the media, where data journalism has emerged as a new and more advanced area, visualisations are often used to emphasise a point of view or drive an agenda. It is essential to have an insight into the purpose in order to be able to experience the visualisation in the right way. Other fundamental distinctions include interactive or locked and animated or static visualisations, the channels or technologies that are used and the level of graphical complexity.
Why do you want to become involved in Visual Arena?
I want to make it easier for experts in this area to work together, encourage greater participation and support local developments. Visualisations are powerful and informative and by spreading awareness and knowledge they can be developed and used more widely.
What do you believe is the potential of Visual Arena’s initiative in this area?
We will finally have an effective local forum where we can share experiences, develop joint commitment and work together to increase our insights and skills. To put it simply, we can drive development in this important area.