Azure-Powered Cognitive Decision Systems for Big Data Analytics
DOI:
https://doi.org/10.5281/zenodo.22874762Keywords:
AI Decision Pipelines, Intelligent Decision Systems, Big Data Analytics, Decision Quality Metrics, Data Ingestion Systems, Data Governance in AI, Secure Data Pipelines, Compliance in AI Systems, Resilient Data Architectures, Azure Data Cloud, Cloud Data Engineering, Credit Scoring Models, Risk Assessment Analytics, Banking Decision Systems, Data Preprocessing Pipelines, Scalable Data Management, AI Pipeline Architecture, Decision Theory in AI, Enterprise Data Pipelines, Data-Driven Decision Making.Abstract
Artificial intelligence (AI) pipelines are central to building intelligent decision systems that leverage big data analytics to improve decision quality. The effectiveness of such systems offers a practical lens for evaluating the underlying AI pipelines themselves. Because decision quality depends heavily on the quality of extracted and preprocessed data, careful attention to data governance, security, compliance, and resilience is essential throughout the pipeline. This study presents a case study using Azure Data Cloud to demonstrate big data ingestion and management capabilities, offering practical guidance for constructing AI pipelines on Azure. The approach is further validated through a credit scoring and risk assessment analysis in bank-based decision-making.
A pipeline is defined here as a computer-based implementation of an intelligent decision system that harnesses AI methods and big data analytics to enhance decision quality, evaluated through the lens of decision theory. Decision quality is treated as a multidimensional measure closely tied to the pipeline's processing flow. Automated, scalable data ingestion and management are critical to realizing the potential of such systems, as data quality remains the most decisive factor in decision quality assessment. The case study illustrates how Azure Data Cloud supports large-scale data ingestion and management for bank-based decision-making, specifically in the context of credit scoring and risk assessment.
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