Experimental approach
DigiKAR was planned and executed as a laboratory, hence with a strong experimental character.
Why experimental?
Certainly, it is nice to have a finished product, but the process of getting there is equally important. As clichee as it sounds – we wanted to enjoy the process and take out as much as possible. We believe that when creating visualizations we usually spend a lot of time on details. And we are conviced that these details matter. However, taking care of them is very time-consuming. With this experimental approach we wanted to give ourselves the freedom – and above all the time – to try things out without the pressure to get it right. Even more – with experiments there’s always the likely possibility of failing.
The experimental approach allows us to learn from our mistakes and to improve our methods and tools. Moreover, it also allows us to be more flexible, adapting to new insights and requirements along the way.
Keeping the target group in mind
With all its freedom the experimental approach still needs a clear structure. We tried to keep the focus by looking at things from the perspective of our target group: the visualizations in the DigiKAR project are aimed at an expert audience of historians and DH professionals. This audience includes both, users skilled with digital data processing, and users with less experience in this field. We wanted to create visualizations that are easy to understand and that can be used by both groups. At the same time we did not want to compromise on the complexity of the data and the visualizations.
Challenges
Working with an experimental approach also comes with its challenges. We identified two main challenges:
- working on visualizations with preliminary data while data processing is ongoing
- different requirements for different stakeholders
1. Cartographic visualizations and preliminary data
As data is never ready (there are always things need to be refined) the visualization team needs to find a way to work with preliminary data. This is a challenge we struggled with throught different phases of the project. However, we believe that this is not at all a problem specific to DigiKAR. More agile methods may help to migitate this problem.
2. Different requirements for different stakeholders
Different stakeholders have different requirements. For example, some may be interested in showing the historical context of the data and its vagueness, while others may be more interested in the overall picture and trying to identify trends in the data from the visualizations. We tried to find a balance between these requirements. However, this is not always easy and requires a lot of communication and coordination.