AI-Driven Cloud Data Orchestration for Real-Time Enterprise Decision Intelligence Systems

Nareddy Abhireddy

doi:10.15662/IJRAI.2024.0706040

Authors

Nareddy Abhireddy Independent Researcher, India Author

DOI:

https://doi.org/10.15662/IJRAI.2024.0706040

Keywords:

AI, analytics, artificial intelligence, cloud; cloud computing, confidence scoring, data engineering, data governance, data in motion, data quality, decision intelligence, decision modeling, event processing, event-driven architecture, event-stream, fault tolerance, frontend protection, hybrid cloud, identity management, Infrastructure as a code, latency, low-code, MLOps, model monitoring, model reliability, model security, observability, Pipelines management, privacy, provenance, real-time, reliability, replicated data, role management, security, software development, Software as a Service, zero trust management

Abstract

Decision Intelligence goes beyond traditional Business Intelligence by providing organizations with all the data they need in a timely manner. Decision Intelligence Systems are built on Streaming Analytics and Event-Driven Architectures, ensuring that data is available for timely analysis and action. Recent data processing advancements are helping to develop the required Decision Intelligence Data Layer and its Supporting Resources and Services. First, data ingestion, normalization, and enrichment are being built at scale, applying Data Quality Frameworks for automated sanitization, Data Confidence Scoring systems for Intelligent Quality Control, and provenance information for interpretability. Next, patterns of orchestration for real-time pipelines are classified, Techniquestog and Techniquestog parameters supporting resilient handling of dynamic situations, thus aiding the Definition, Management, and Scheduling of Event Processing Pipelines. Finally, investigations are taking the first steps towards automatic provisioning of resources and orchestration of complex streaming convergences.

These developments are helping to complement the current step-wise, batch-oriented Operation Models of Data-Powered Enterprises with more responsive systems in charge of Dynamic Decision Intelligence, targeting niche applications such as Operational Intelligence, Situational Awareness, or Anomaly Detection Enablement. Decision Intelligence goes beyond traditional Business Intelligence by providing organizations with all the data they need in a timely manner. Decision Intelligence Systems are built on Streaming Analytics and Event-Driven Architectures, ensuring that data is available for timely analysis and action. Recent data processing advancements are helping to develop the required Decision Intelligence Data Layer and its Supporting Resources and Services. First, data ingestion, normalization, and enrichment are being built at scale, applying Data Quality Frameworks for automated sanitization, Data Confidence Scoring systems for Intelligent Quality Control, and provenance information for interpretability.

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