Information visualization evaluation in large companies: Challenges, experiences and recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examine the implications of evaluating data analysis processes and information visualization tools in a large company setting. While several researchers have addressed the difficulties of evaluating information visualizations with regards to changing data, tasks, and visual encodings, considerably less work has been published on the difficulties of evaluation within specific work contexts. We specifically focus on the challenges, which arise in the context of large companies with several thousand employees. Based on our own experience from a 3.5-year collaboration within a large automotive company, we first present a collection of nine information visualization evaluation challenges. We then discuss these challenges by means of two concrete visualization case studies from our own work. We finally derive a set of 16 recommendations for planning and conducting evaluations in large company settings. The set of challenges and recommendations and the discussion of our experience are meant to provide practical guidance to other researchers and practitioners, who plan to study information visualization in large company settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.020 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it