A collaborative model for data governance: enhancing integration across multi-line businesses
Bibliographic record
Abstract
In today's increasingly data-driven business environment, organizations with multiple lines of business face significant challenges in managing data effectively. Fragmented and siloed data governance models can hinder decision-making, reduce data quality, and create inefficiencies across business units. This review explores the development of a collaborative data governance model designed to enhance integration across multi-line businesses. By unifying data governance frameworks, fostering cross-functional collaboration, and standardizing data policies, the proposed model aims to break down silos and create a more cohesive approach to data management. Key components include the establishment of data governance councils, the appointment of data stewards in each business unit, and the adoption of advanced data technologies that facilitate seamless integration. The collaborative model encourages interdepartmental communication and shared objectives, ensuring that data governance aligns with broader organizational goals. It also emphasizes the importance of maintaining data security and privacy while enabling data sharing across departments. Case studies of successful implementations in various industries are presented, highlighting best practices and lessons learned. Additionally, the review identifies potential challenges, such as cultural resistance, technical barriers, and resource allocation issues, offering strategies for mitigation. By adopting a collaborative data governance approach, multi-line businesses can improve data quality, enhance operational efficiency, and ensure better regulatory compliance. The review concludes with a forward-looking view on the scalability of this model and the role of emerging technologies, such as artificial intelligence, in automating and enhancing data governance processes in the future. Keywords: Collaborative model, Data governance, Multi-line, Review.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".