Dynamic Data Orchestration Using AI in Real-Time Decision Systems

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September 23, 2021

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Dynamic data orchestration comes into the forefront of strategic value to facilitate real-time decision systems in supporting effectiveness in complex and uncertain contexts. Through AI implementation into data orchestration, organizations can easily handle HVD because AI is capable of handling big data characterization at high velocity, volume, and variety. These are systems that utilize predictive analytics, machine learning and the integration of real-time data pipelines to perform critical decisions, minimize latencies and under optimal utilization of resources.

In this paper, the author investigates the extent of change enabled by the AI Data management in real-time decision-making system across different sectors including finance, healthcare, supply chain, and energy sectors. These are neural network to support automatic prediction, reinforcement learning to support reactive decision making, and edge computing to support on-field data processing. Further, the study includes specific infrastructures, for example, hybrid cloud solutions and event stream processing platforms on which companies can base their growth.

The work also examines issues such as data isolation, integration with previous systems, and ethics for artificial intelligence decision-making. Real-life examples show that AI-driven orchestration pays off in terms of efficiency, response and decision-making time, accuracy and so on. Highlighting the current limitations of real-time decision systems that apply AI and discussing future trends, this work emphasizes the centrality of AI in designing processes for the following generation

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