International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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The Overlooked Key to AI Success: Why Clean, Reliable Data Outperforms Bigger Models

Author(s) Mr. Ali Azghar Hussain Syed Abbas
Country India
Abstract As organizations pursue ever-larger artificial intelligence models, this paper argues that the true foundation of AI success lies in clean, reliable, and well-governed data. We present a data-first perspective, demonstrating that investments in data quality—accuracy, completeness, consistency, timeliness, representativeness, and provenance—consistently yield greater improvements in model accuracy, robustness, explainability, and operational efficiency than architectural innovation alone. Common data defects such as label noise, schema inconsistencies, and stale features are shown to impose hard limits on model performance and drive up operational costs. The proposed Data-First AI framework integrates continuous data profiling, automated validation, semantic standardization, and end-to-end lineage into the AI development lifecycle. Through empirical evaluation across domains including healthcare, smart infrastructure, and marketing, we show that targeted data interventions—profiling, semantic harmonization, freshness monitoring, and smart-sizing—deliver measurable gains in calibration, generalization, and business outcomes. The paper concludes that treating data as a product capability, with explicit contracts and stewardship, is essential for trustworthy, cost-effective, and resilient AI systems
Keywords Data Quality, Artificial Intelligence, Data Governance, Master Data Management (MDM), Data-First AI, Model Robustness, Semantic Standardization, Data Lineage, Smart-Sizing, Label Noise, Machine Learning Operations (MLOps), Data Provenance, Model Explainability, Operational Efficiency, Trustworthy AI
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-25
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.61533
Short DOI https://doi.org/hbcnvv
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