Continuous Data Quality Improvement in Enterprise Data Governance: A Model for Best Practices and Implementation
Bibliographic record
Abstract
Continuous Data Quality Improvement (CDQI) is essential for maintaining the integrity, accuracy, and reliability of enterprise data. In today's data-driven organizations, ensuring high-quality data across various systems and departments is critical for decision-making, operational efficiency, and regulatory compliance. This review presents a model for CDQI within the framework of enterprise data governance, outlining best practices and implementation strategies for sustained improvements in data quality. The proposed model integrates key components such as data quality assessment, improvement strategies, automation tools, and the alignment of governance policies with data quality objectives. It emphasizes the importance of establishing clear data standards, roles, and responsibilities, including the role of data stewards in maintaining quality over time. By leveraging technologies such as AI and real-time monitoring tools, organizations can automate data cleansing, detect anomalies, and provide actionable insights through continuous feedback loops. Best practices for CDQI include fostering a data-driven culture, conducting regular audits, enabling cross-functional collaboration, and integrating data quality metrics into governance policies. The implementation strategy is designed to be phased, starting with pilot programs and scalable to larger enterprise systems. Additionally, the model addresses challenges such as organizational resistance, balancing privacy concerns, and managing complex data environments. By adopting this model, organizations can ensure ongoing data quality improvements, leading to more accurate insights, better compliance with regulations, and enhanced business outcomes. This abstract provides a foundation for organizations aiming to enhance their data governance frameworks through continuous improvement.
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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 | 0.089 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.029 | 0.034 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".