Building Scalable Data Engineering Platforms to Enhance AI-Driven Business Intelligence

Abstract

On the dawn of AI-driven BI, creating efficient data engineering platforms became the key to implementing the use of data-driven AI. The aforementioned platforms meet the increased requirement for immediate data processing, data integration, and the computation required by contemporary AI systems. When cloud native architectures, distributed computing, and best of ETL/ ETL techniques are used appropriately, the huge amount of data can be properly processed and analyzed and raw data can be converted into useful insights.

Drawing upon IT experience, this article focuses on the design and future development of large-scale data engineering platforms that improve AI-supported BI performance. It consisted in the application of modular structures, using machine learning pipelines in predictive analytics, as well as enhanced visualization in business decisions. Key success factors that have been evident from specific domains, including the retail, healthcare, and financial sectors include improved work flow, enhanced insight accuracy, and quicker decision making.

Some of the pros of the scalable platforms are obvious while others lie in areas like; challenge like costs of building infrastructure for large-scale applications, issues of data privacy, and how to integrate with large-scale pre-existing systems. Solutions like edge computing and combining with quantum technologies have bright future possibilities for the optimization of the advancements. By establishing the first article of this series, this article attempts to set out the frame of reference for creating data platforms that enable organizations to fully realize the benefits of AI-BI.