Data Management and Data Administration
Bibliographic record
Abstract
Data management (DM) has existed in conjunction with software development and the management of the full set of information technology (IT)-related components. However, it has been more than two decades since research into DM as it is practiced has been published. In this paper, the authors compare aspects of DM across a quarter-century timeline, obtaining data using comparable sets of subject matter experts. Using this information to observe the profession’s evolution, the authors have updated the understanding of DM as it is practiced, giving additional insight into DM, including its current responsibilities, reporting structures, and perceptions of success, among other factors. The analysis indicates that successfully investing in DM presents current, real challenges to IT and organizations. Although DM is evolving away from purely operational responsibilities toward higher-level responsibilities, perceptions of success have fallen. This paper details the quarter-century comparison of DM practices, analyzes them, and draws conclusions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".