Integrating AI into Enterprise Data Warehouses for Enhanced Operational Intelligence and Decision-Making
DOI:
https://doi.org/10.32628/CSEIT2511155Keywords:
Artificial Intelligence, Enterprise Data Warehouse, Operational Intelligence, Decision-Making, Machine Learning, Predictive Analytics, Business Intelligence, Real-Time Data Processing, Data Integration, Intelligent SystemsAbstract
As businesses gradually shift to using real-time, data-driven decision-making, the application of Artificial Intelligence (AI) in the context of Enterprise Data Warehouses (EDWs) will revolutionize operational intelligence. Originally engineered to store and analyze static, structured data and report on past data, EDWs are being redesigned to cope with complexity, velocity, and volume of contemporary enterprise data. The given research explores the possibility of integrating AI technologies, such as machine learning, natural language processing, and predictive analytics, into EDW architectures to streamline data workflows and create real-time insights, as well as opportunities for predictive business strategies. A combination of an extensive literature review and a multi-case exploratory study of large-scale enterprises allows theoretical underpinnings and empirical support of the research. The study stands out among the past works because it pays close attention to the aspect of technological feasibility and organizational readiness, as well as resolving the following questions: data silos, system latency, infrastructure modernization, and adherence to changing regulatory demands. Importantly, it embraces ethical impact in business transparency and AI algorithms and the governance systems that ought to be instituted to allow for responsible implementation of the AI. The results indicate that the AI-empowered EDWs are significantly more responsive in terms of their queries, their data pipelines can be simplified, they allow identifying the early signs of anomalies, and they are more effective in modelling the forecasts, which results in smaller latency of decisions to be made and increases the overall business agility. Also, the paper has put forward a responsible integration framework to help enterprises incorporate AI strategies with ethical concepts and operational requirements. Discussing the advantages of AI-driven EDWs as well as the ethical demands on the same, this paper can guide decision makers, researchers, and IT practitioners on how to ensure the future-proofing of their data infrastructure through intelligent automation and governance-aware transformation.
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