Open Access

Data Architecture Maturity as A Predictor of Enterprise AI Success in Regulated Industries

https://doi.org/10.55640/ijaair-v03i04-03

PDF

Mohammed Arbaaz Shareef ¹ ,

⁴ Lead Data Engineer at Anblicks

Abstract

Objective: To operationalize data-architecture maturity as a predictor of enterprise AI success in regulated industries, where auditability, traceability, and lifecycle risk controls constrain deployment at scale.

Methods: A structured comparative synthesis of ten recent peer-reviewed and standards sources on data governance and quality, maturity modeling, lakehouse/platform capabilities, MLOps, and AI risk management was performed, followed by construct-to-indicator mapping that links maturity domains to observable operational signals.

Results: The synthesis yields a five-domain predictor framework—(1) governance maturity, (2) data quality maturity, (3) platform architecture maturity, (4) MLOps maturity, and (5) responsible-AI maturity—paired with an evidence-linked mapping to success mechanisms (reproducibility, controllability, deployment stability, auditable decision records, and sustained performance under drift). The framework is made assessable pre-deployment through a concise signal set: versioned datasets and reconstructable training snapshots, transformation lineage bound to metadata, continuous quality metrics with documented thresholds, automated drift monitoring with traceable alerts, and explicit lifecycle risk controls aligned with regulatory expectations.

Implications: The model supports feasibility screening and targeted remediation planning before scaling AI in compliance-constrained environments.

Keywords

data architecture maturity, enterprise AI, regulated industries, data governance, data quality, metadata and lineage

References

Alexakis, T., Adamopoulou, E., Peppes, N., Daskalakis, E., & Ntouskas, G. (2025). Evaluating data quality: Comparative insights on standards, methodologies, and modern software tools. Electronics, 14(15), 3038. https://doi.org/10.3390/electronics14153038

Bernardo, B. M. V., Mamede, H. S., Barroso, J. M. P., & Santos, V. M. P. D. D. (2024). Data governance & quality management—Innovation and breakthroughs across different fields. Journal of Innovation & Knowledge, 9(4), 100598. https://doi.org/10.1016/j.jik.2024.100598

Choowan, P., & Daovisan, H. (2026). Artificial intelligence in data governance for financial decision-making: A systematic review. Big Data and Cognitive Computing, 10(1), 8. https://doi.org/10.3390/bdcc10010008

Eken, B., Pallewatta, S., Tran, N., Tosun, A., & Babar, M. A. (2025). A multivocal review of MLOps practices, challenges and open issues. ACM Computing Surveys, 58(2), 1–35.

Ferreira, R. M. F. D., Grilo, A., & Maia, M. (2025). Piloting a maturity model for responsible artificial intelligence: A Portuguese case study. Journal of Responsible Technology, 22, 100117. https://doi.org/10.1016/j.jrt.2025.100117

Harby, A. A., & Zulkernine, F. (2025). Data lakehouse: A survey and experimental study. Information Systems, 127, 102460. https://doi.org/10.1016/j.is.2024.102460

Mazumdar, D., Hughes, J., & Onofre, J. B. (2023). The data lakehouse: Data warehousing and more. arXiv. https://arxiv.org/abs/2310.08697

National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf

Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2022). Challenges in deploying machine learning: A survey of case studies. ACM Computing Surveys, 55(6), Article 1, 29. https://doi.org/10.1145/3533378

Pörtner, L., Riel, A., Schmidt, B., Leclaire, M., & Möske, R. (2025). Data management maturity model—Process dimensions and capabilities to leverage data-driven organizations towards Industry 5.0. Applied System Innovation, 8(2), 41. https://doi.org/10.3390/asi8020041

Most read articles by the same author(s)

<< < 4 5 6 7 8 9 10 11 > >>

International Journal of Advanced Artificial Intelligence Research

Data Architecture Maturity as A Predictor of Enterprise AI Success in Regulated Industries

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles