Self-Evolving Data Ecosystems for Predictive Enterprise Clarity
DOI:
https://doi.org/10.5281/zenodo.21412527Keywords:
AI-powered data lakes,Cloud data architecture,Intelligent analytics platforms,Big data processing,Machine learning pipelines,Data lakehouse architecture,Real-time data analytics,Enterprise data management,Predictive analytics systems,Data governance and security,Scalable cloud storage,ETL/ELT automation,Business intelligence (BI) integration,Advanced data visualization,Automated reporting systems.Abstract
Two significant developments have converged: the emergence of cloud-based data lakes, conducive to cost-effective data storage and processing for intelligent enterprise applications, and the application of artificial intelligence (AI) to greatly improve the various processes required to build and maintain a data lake. Intelligent enterprise applications can be categorized as descriptive analytics and dashboards; advanced analytics and predictive modeling; and real-time analytics and streaming data. These categories map to typical application areas such as customer analytics and personalization; operational intelligence and asset monitoring; and supply chain optimization and risk management. The primary issues in building a cloud-based data lake include data privacy and sovereignty; fairness, bias and explainability in AI; performance management and cost optimization.The abstract now conforms with the rest of the paper.
Data governance and cataloging; security and compliance; architecture and components are addressed. Emerging AI technologies significantly enhance the intelligent enterprise applications in a cloud-based data lake, particularly data ingestion and feature engineering; automated metadata enrichment and lineage; and data quality and cleansing. Supporting both the descriptive and advanced analytics categories of intelligent enterprise applications, the Intelligent Enterprise Analytics and Reporting Framework comprises descriptive analytics, advanced analytics, real-time analytics and streaming data; integrating the domains of customer analytics and personalization; operational intelligence and asset monitoring; supply chain optimization and risk management.
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