AI-Driven Business Intelligence Automation: Integrating Data Engineering, Auto-BI, and Large Language Models
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I3P112Keywords:
Business Intelligence Automation, Data Engineering, Large Language Models, Auto-BI, AI-Driven Analytics, Decision IntelligenceAbstract
Business intelligence (BI) systems are important as they aid in assisting organizations to convert huge amounts of unprocessed data into insights that can be used to make strategic and operational decisions. Nevertheless, the conventional BI systems tend to employ manual data preparation, inert dashboards, and a set of analytical models, which restrict their capability to be adapted easily to the extensive and ever-evolving business contexts. With the continuous growth of enterprise data ecosystems in terms of volume, velocity, and variety, the need to process smart and automated analytics solutions has grown dramatically. The traditional BI systems also demand specialized technical skills in fields like data engineering, SQL queries and dashboards development which pose a barrier to the business users who require at the right time and in an accessible format the information to make the needed decisions.
This work suggests an artificial intelligence-based Business Intelligence automation system that combines the recently developed data engineering pipelines, automated Business Intelligence (Auto-BI) functionalities and Large Language Models (LLMs) to facilitate scalable and intelligent analytics. The suggested architecture will bring a multi-layers system of data ingestion and transformation, automated analytics, which would detect KPI and generate dashboard, and insight generation component powered by the LLM which provides the ability to query it in natural language and generate analytics. The diagram is tested with a billion scale analytical workloads to determine the enhanced efficiency of queries, automation, and insight generation accuracy compared to the traditional BI systems. The experimental results suggest that the suggested strategy decreases the effort of developing manual BI systems, speeds up the process of making insights, and improves accessibility with the help of a natural language interface. Altogether, the framework shows that the combination of AI, machine learning, and the LLM technologies can transform conventional BI infrastructures to become intelligent, automated decision-support systems in the modern ventures.
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