From Data Governance to Data Intelligence Governance: Transforming Enterprise-Level Data Asset Management
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
In the digital era, traditional data governance frameworks centered on rule - enforcement are struggling to meet the dynamic demands of modern enterprises, which restricts the efficiency of data utilization. This paper introduces a novel "data intelligence governance" framework. By integrating AI technologies with systematic governance practices, it creates a symbiotic relationship between governance and intelligent analytics. Empirical evidence shows remarkable improvements in data accessibility, analysis efficiency, and decision - making agility. Case studies demonstrate efficiency gains of up to 40%. This approach paves the way for scalable, self - service data asset management, enabling enterprises to transform into agile, data - driven organizations.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.023 |
| Open science | 0.016 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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