Developments in Practice XX - Digital Dashboards: Keep Your Eyes on the Road
Bibliographic record
Abstract
The authors convened a focus group of senior IT managers from a number of leading edge organizations to explore the topic of digital dashboards - defined as "electronic interfaces (typically portals) that provide employees with timely, personalized information to enable them to monitor and analyze the performance of the organization". Whereas earlier executive-based systems were not only "hand-tooled" exclusively for executives, they were also designed for (and based on the anticipation of) executives performing "what-if" analyses. In contrast, today's digital dashboards appear to be much more focused on providing information (i.e., access) and much less focused on supporting the analysis of the information provided. While this difference appears nuanced, it represents a profound difference in terms of the how the management function is supported by information technology. An analysis of the focus group's dashboards found three different categories: performance-based to display the basic mix of financial and non-financial results broken out by current versus previous period, actual versus target, project-based which relate primarily to status reporting where the only comparative data is "actual to budget", and opportunity-based where the goal is to guide employees towards new opportunities for enhancing the business. While each category is for an express purpose, it is possible to group the benefits of all dashboards into the following four categories: alignment with strategy and accountabilities, enhanced decision making support and analysis, improved integrity and timeliness of data, and operational efficiencies. Furthermore, these benefits are no longer only for the senior executives. The availability of digital dashboards changed Executive Information Systems so that they are everyone's information system. The paper concludes with suggested strategies for implementing digital dashboards successfully to reap these benefits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.027 | 0.028 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".