Data Hunches: Incorporating Personal Knowledge into Visualizations

data-hunches screenshot

Abstract

The trouble with data is that often it provides only an imperfect representation of the phenomenon of interest. When reading and interpreting data, personal knowledge about the data plays an important role. Data visualization, however, has neither a concept defining personal knowledge about datasets, nor the methods or tools to robustly integrate them into an analysis process, thus hampering analysts' ability to express their personal knowledge about datasets, and others to learn from such knowledge. In this work, we define such personal knowledge about datasets as data hunches and elevate this knowledge to another form of data that can be externalized, visualized, and used for collaboration. We establish the implications of data hunches and provide a design space for externalizing and communicating data hunches through visualization techniques. We envision such a design space will empower users to externalize their personal knowledge and support the ability to learn from others' data hunches.

Citation

BibTeX

@article{2021_preprint_data-hunches,
  title = {Data Hunches: Incorporating Personal Knowledge into Visualizations},
  author = {Haihan Lin and Derya Akbaba and Miriah Meyer and Alexander Lex},
  booktitle = {Preprint},
  url = {https://arxiv.org/abs/2109.07035},
  year = {2021}
}

Note

Haihan Lin and Derya Akbaba contributed equally to this work.