Smart ERP: Scalable Data Engineering Frameworks Using Artificial Intelligence
Yuvaraj Kavala
2023· International Journal of Computational Mathematical Ideas· 0 citations
TL;DR
The findings advocate for the integration of AI-powered pipelines within ERP systems as a transformative approach to enable scalable, intelligent, and high-fidelity data processing, essential for next- generation enterprise software resilience and performance.
Abstract
Enterprise Resource Planning (ERP) systems are foundational to the functioning of large organizations, facilitating the seamless integration of diverse business functions such as finance, procurement, human resources, inventory, and sales. However, the exponential increase in volume, velocity, and variety of data generated from these modules has rendered traditional data engineering practices inadequate. This paper presents an AI-driven data engineering framework designed to address the challenges of data quality, scalability, and pipeline agility in modern ERP environments. The proposed framework incorporates machine learning models for intelligent metadata-aware ETL rule generation, unsupervised anomaly detection, and dynamic orchestration of end-to-end data pipelines. It also embeds core software engineering principles including modular architecture, CI/CD automation, observability, and testing to ensure reliability and maintainability. A proof-of-concept system was implemented using technologies such as Apache Airflow, Spark, AWS SageMaker, and Amundsen, and was evaluated on synthetic ERP data across procurement, payroll, and inventory modules. Experimental results reveal that the anomaly detection accuracy improved from 71% to 94% using the proposed framework, while ETL rule generation time decreased from two hours to just 15 minutes. Pipeline execution latency was reduced by 45%, and downstream model accuracy improved from 82% to 91% after automated data cleaning. These results confirm the framework’s effectiveness in improving data quality and operational efficiency. The findings advocate for the integration of AI-powered pipelines within ERP systems as a transformative approach to enable scalable, intelligent, and high-fidelity data processing, essential for next- generation enterprise software resilience and performance.
The study concludes that enterprise AI-driven data engineering represents a foundational element of next-generation digital transformation strategies and organizations adopting intelligent data platforms are better positioned to leverage data assets, support real-time analytics, and achieve sustainable competitive advantage in increasingly data-centric business environments.
Raja Ganesan· International Journal of Eme...· 0 citations
The findings indicate that organizations achieving successful digital transformation are those capable of integrating intelligent analytics with secure and scalable data infrastructures, and automation technologies significantly reduce operational complexity while improving responsiveness and business continuity.
A. L.· American International Journ...· 0 citations
The rapid evolution of enterprise architecture necessitates innovative approaches to manage the increasing complexities of digital ecosystems. This paper explores the transformative potential of Artificial Intelligence (AI)-driven cloud solutions in modernizing enterprise architecture, with a focus on integrating DevOps and DataOps methodologies to achieve scalability. AI-powered tools and frameworks in cloud computing offer unparalleled scalability, operational efficiency, and real-time adaptability, enabling enterprises to remain competitive in a data-driven economy. By combining DevOps' focus on streamlining software development and operations with DataOps' emphasis on agile and automated data pipeline management, organizations can optimize workflow automation, accelerate deployment cycles, and enhance decision-making processes. AI further augments this synergy by facilitating predictive analytics, anomaly detection, and intelligent resource allocation, which are critical for achieving scalability and reliability in dynamic business environments. Case studies highlight the successful application of these technologies across various industries, showcasing measurable improvements in performance and cost efficiency. The paper also addresses challenges in adopting AI-driven cloud solutions, including data privacy, compliance, and skill gaps, offering actionable recommendations for mitigating these obstacles. Emphasis is placed on the need for collaborative strategies between IT and business teams to maximize the potential of integrated DevOps and DataOps frameworks.
Fatou Diop· International Journal of Art...· 0 citations
Modern enterprises generate massive volumes of data from cloud platforms, IoT devices, enterprise applications, social media, and AI systems, creating challenges in data integration, governance, scalability, security, and real-time analytics. Traditional data management approaches often struggle to handle these complex and distributed environments. This paper proposes an Autonomous Data Fabric (ADF) architecture that combines AI/ML, metadata-driven automation, knowledge graphs, intelligent orchestration, and policy-based governance to enable seamless, self-managing enterprise data ecosystems. The framework supports automated data discovery, semantic integration, adaptive workflows, continuous monitoring, and intelligent resource optimization while ensuring data quality, security, and compliance. Experimental results demonstrate that the proposed ADF significantly improves data integration efficiency, governance, analytics performance, operational cost, and decision-making compared to conventional systems. Its scalable and self-adaptive design supports hybrid cloud, multi-cloud, edge, and on-premises environments, making it a robust solution for enterprise digital transformation and next-generation intelligent data management.
Narendra Karmarkar· International Journal of Dat...· 0 citations
Enterprise systems still struggle to move data reliably across cloud and legacy platforms while keeping costs, latency, and risk in check. The author presents an artificial intelligence-enhanced middleware pattern that augments existing integration stacks with telemetry, stream processing, and a lightweight learning loop to predict failures, automatically tune policies, and direct traffic in real time. The architecture couples an integration core comprising application programming interfaces (APIs), messaging, and event flows with a model-driven policy layer and feedback control. The approach is validated through implementations involving retail order orchestration, logistics tracking, and financial services, demonstrating reductions in mean time to resolution of 35% to 55%, message loss of 0.01%, and cloud egress costs of 8% to 12% under production-like loads. The author outlines governance and observability practices that make the pattern portable across TIBCO Software Inc. integration platforms, Apache Kafka, MuleSoft, and cloud-native services without vendor lock-in. The result provides a pragmatic route to resilient, compliant, and scalable integration that organizations can adopt incrementally at enterprise scale without rewriting critical systems.
Tejas Gajjar· International Journal of Inf...· 0 citations
Application Programming Interfaces (APIs) have evolved from technical integration mechanisms into the fundamental building blocks of digital enterprises. As organizations modernize legacy systems, adopt cloud-native architectures, and integrate artificial intelligence, APIs serve as the connective tissue enabling interoperability, scalability, and innovation velocity. However, enterprise-scale API management faces challenges: API sprawl, inconsistent governance, security vulnerabilities, and inadequate AI integration. This paper presents an AI-augmented architecture for intelligent API lifecycle management spanning design, development, deployment, monitoring, and retirement. The architecture introduces four components: (1) an AI-powered design assistant that generates OpenAPI 3.0 specifications from natural-language requirements with human-in-the-loop validation; (2) an intelligent traffic management engine combining machine-learning forecasting with anomaly detection for predictive auto-scaling and adaptive rate limiting; (3) an automated governance framework enforcing organizational standards through policy-as-code with continuous compliance verification; and (4) a semantic API discovery system enabling naturallanguage search via vector embeddings. Evaluation across 344 enterprise APIs spanning financial services, insurance, healthcare, and energy demonstrates 62% reduction in API design time, 91.3% anomaly-detection accuracy, 47% improvement in API reuse, and 99.95% governance compliance. Aligned with the AIML 2026 workshop theme of Proactive and Responsible Autonomy, the architecture incorporates human oversight, auditability, and explainability into every AI-augmented component, transforming API management from a reactive operational burden into a proactive, governable capability.
Venkata Gummadi· Annual International Compute...· 0 citations
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