<b>Data Quality as a Service (DQaaS): A Paradigm Shift in </b> <b>Enterprise Data Management </b>
Bibliographic record
Abstract
The data environments found in enterprises continue to be plagued by incompleteness, inconsistency, duplication, staleness, and distributional drift, which directly impact decision-making, the performance of machine learning, and regulatory compliance. Conventional data-quality strategies that may be as narrow as semi-static rules or as inefficient as manually cleaning data cannot address the speed and variety of modern pipelines. This paper suggests Data Quality as a Service (DQaaS). This paradigm shift redefines quality as a provision-managed, cloud-native capability that provides through APIs, contracts, and measurable service level objectives (SLOs). DQaaS incorporates declarative rules, statistical anomaly detectors, and machine learning models under a common multi-tenant platform and delivers round-the-clock monitoring, lineage-enabled diagnostics, and remediation as a service. The contributions that this work has can be classified in four ways. It is first to present a reference architecture that has control and data planes in both the streaming and batch pipelines. It formalizes measures of service level indicators (SLIs), SLOs, and error budgets on the critical dimensions of completeness, validity, timeliness, and accuracy. It makes contracts and schema evolution operational in CI/CD pipelines, in a compatible and accountable way between producers and consumers. It also tests DQaaS using enterprise datasets across ERP, CRM, and streaming data, clearly highlighting improvements in reliability, incident recovery times, and business performance with very little latency overhead. The results show how DQaaS can turn ad hoc quality activities into a scalable and bureaucratically enforceable service that is economically sustainable, with technical assurance, governance, and organizational objectives.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".