A High-Level Architecture for Intelligent, Enterprise-Scale Data Quality Enforcement Article Swipe
Modern enterprise data ecosystems operate across distributed cloud platforms, multi-layered warehouses, ingestion pipelines, analytical workspaces, regulatory reporting systems, and AI-driven decision environments. As organizations increasingly rely on data for mission-critical processes, the challenge of ensuring accuracy, consistency, interpretability, and trustworthiness has grown exponentially. Traditional data quality solutions, primarily designed around isolated rule checks and manual oversight, are not equipped to address the semantic, temporal, and metadata-rich complexity of today’s digital landscapes. This white paper presents the AI-Driven Automated Data Quality Framework (ADQF), a high-level architectural approach conceptualized and authored by Aniruddh Tiwari. ADQF introduces an original, industry-agnostic vision for intelligent data quality enforcement, combining metadata awareness, contextual interpretation, multi-state validation perspectives, temporal reasoning, and agentic autonomy at a conceptual level. First envisioned during early deployments within pharmaceutical and financial ecosystems, ADQF has since matured into a widely adaptable framework that addresses enterprise challenges such as semantic misalignment, late-arriving information, evolving business definitions, cross-system dependencies, and quality assurance for AI and analytics workloads. ADQF’s contribution lies in reframing data quality as a dynamic and context-dependent discipline rather than a static rules-based exercise. Its conceptual layers — including metadata synchronization, hybrid requirement interpretation, intelligence-driven insight discovery, multi-state evaluation, bi-temporal correctness assessment, and high-level autonomous decision support — provide organizations with a scalable blueprint for governing data reliability across complex environments. The framework has been referenced, adopted, and extended across multiple industries, demonstrating significant impact on operational readiness, auditability, and data-driven transformation programs. This paper offers a publication-safe overview of the architectural principles behind ADQF. The description intentionally excludes implementation details and focuses on the conceptual innovations, enterprise significance, and cross-industry applicability that position ADQF as a meaningful original contribution to the field of data quality, AI-driven governance, and enterprise data engineering.
Related Topics
Concepts
Computer science
Data governance
Metadata
Data quality
Data science
Cloud computing
Data integrity
Data warehouse
Blueprint
Data sharing
Quality (philosophy)
Analytics
Knowledge management
Process management
Data architecture
Data modeling
Architecture framework
Software engineering
Digital transformation
Scalability
Reference architecture
Big data
Conceptual architecture
Quality assurance
Correctness
Data virtualization
Data transformation
Architecture
Data access
Computer security
Conceptual framework
Metadata
- Type
- report
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.17882144
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7114769662
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W7114769662Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.17882144Digital Object Identifier
- Title
-
A High-Level Architecture for Intelligent, Enterprise-Scale Data Quality EnforcementWork title
- Type
-
reportOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-12-10Full publication date if available
- Authors
-
Tiwari, AniruddhList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.17882144Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.17882144Direct OA link when available
- Concepts
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Computer science, Data governance, Metadata, Data quality, Data science, Cloud computing, Data integrity, Data warehouse, Blueprint, Data sharing, Quality (philosophy), Analytics, Knowledge management, Process management, Data architecture, Data modeling, Architecture framework, Software engineering, Digital transformation, Scalability, Reference architecture, Big data, Conceptual architecture, Quality assurance, Correctness, Data virtualization, Data transformation, Architecture, Data access, Computer security, Conceptual frameworkTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.framework | 138, 219 |
| abstract_inverted_index.governing | 212 |
| abstract_inverted_index.including | 185 |
| abstract_inverted_index.ingestion | 11 |
| abstract_inverted_index.original, | 95 |
| abstract_inverted_index.primarily | 47 |
| abstract_inverted_index.programs. | 239 |
| abstract_inverted_index.reframing | 166 |
| abstract_inverted_index.reporting | 16 |
| abstract_inverted_index.semantic, | 62 |
| abstract_inverted_index.temporal, | 63 |
| abstract_inverted_index.today’s | 68 |
| abstract_inverted_index.analytical | 13 |
| abstract_inverted_index.autonomous | 201 |
| abstract_inverted_index.awareness, | 105 |
| abstract_inverted_index.challenges | 142 |
| abstract_inverted_index.complexity | 66 |
| abstract_inverted_index.conceptual | 118, 182, 262 |
| abstract_inverted_index.contextual | 106 |
| abstract_inverted_index.discipline | 174 |
| abstract_inverted_index.discovery, | 193 |
| abstract_inverted_index.ecosystems | 3 |
| abstract_inverted_index.enterprise | 1, 141, 264, 286 |
| abstract_inverted_index.envisioned | 121 |
| abstract_inverted_index.high-level | 83, 200 |
| abstract_inverted_index.introduces | 93 |
| abstract_inverted_index.meaningful | 274 |
| abstract_inverted_index.oversight, | 55 |
| abstract_inverted_index.pipelines, | 12 |
| abstract_inverted_index.platforms, | 8 |
| abstract_inverted_index.principles | 249 |
| abstract_inverted_index.processes, | 30 |
| abstract_inverted_index.readiness, | 234 |
| abstract_inverted_index.reasoning, | 112 |
| abstract_inverted_index.regulatory | 15 |
| abstract_inverted_index.solutions, | 46 |
| abstract_inverted_index.validation | 109 |
| abstract_inverted_index.workloads. | 161 |
| abstract_inverted_index.Traditional | 43 |
| abstract_inverted_index.assessment, | 198 |
| abstract_inverted_index.bi-temporal | 196 |
| abstract_inverted_index.correctness | 197 |
| abstract_inverted_index.data-driven | 237 |
| abstract_inverted_index.deployments | 124 |
| abstract_inverted_index.description | 253 |
| abstract_inverted_index.distributed | 6 |
| abstract_inverted_index.ecosystems, | 129 |
| abstract_inverted_index.evaluation, | 195 |
| abstract_inverted_index.governance, | 284 |
| abstract_inverted_index.industries, | 228 |
| abstract_inverted_index.intelligent | 99 |
| abstract_inverted_index.landscapes. | 70 |
| abstract_inverted_index.multi-state | 108, 194 |
| abstract_inverted_index.operational | 233 |
| abstract_inverted_index.referenced, | 222 |
| abstract_inverted_index.reliability | 214 |
| abstract_inverted_index.requirement | 189 |
| abstract_inverted_index.rules-based | 179 |
| abstract_inverted_index.significant | 230 |
| abstract_inverted_index.warehouses, | 10 |
| abstract_inverted_index.workspaces, | 14 |
| abstract_inverted_index.consistency, | 36 |
| abstract_inverted_index.contribution | 163, 276 |
| abstract_inverted_index.cross-system | 152 |
| abstract_inverted_index.definitions, | 151 |
| abstract_inverted_index.enforcement, | 102 |
| abstract_inverted_index.engineering. | 288 |
| abstract_inverted_index.increasingly | 24 |
| abstract_inverted_index.information, | 148 |
| abstract_inverted_index.innovations, | 263 |
| abstract_inverted_index.applicability | 268 |
| abstract_inverted_index.architectural | 84, 248 |
| abstract_inverted_index.auditability, | 235 |
| abstract_inverted_index.demonstrating | 229 |
| abstract_inverted_index.dependencies, | 153 |
| abstract_inverted_index.environments. | 21, 217 |
| abstract_inverted_index.intentionally | 254 |
| abstract_inverted_index.late-arriving | 147 |
| abstract_inverted_index.metadata-rich | 65 |
| abstract_inverted_index.misalignment, | 146 |
| abstract_inverted_index.multi-layered | 9 |
| abstract_inverted_index.organizations | 23, 206 |
| abstract_inverted_index.perspectives, | 110 |
| abstract_inverted_index.significance, | 265 |
| abstract_inverted_index.conceptualized | 86 |
| abstract_inverted_index.cross-industry | 267 |
| abstract_inverted_index.exponentially. | 42 |
| abstract_inverted_index.implementation | 256 |
| abstract_inverted_index.pharmaceutical | 126 |
| abstract_inverted_index.transformation | 238 |
| abstract_inverted_index.interpretation, | 107, 190 |
| abstract_inverted_index.trustworthiness | 39 |
| abstract_inverted_index.mission-critical | 29 |
| abstract_inverted_index.publication-safe | 244 |
| abstract_inverted_index.synchronization, | 187 |
| abstract_inverted_index.context-dependent | 173 |
| abstract_inverted_index.industry-agnostic | 96 |
| abstract_inverted_index.interpretability, | 37 |
| abstract_inverted_index.intelligence-driven | 191 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile |