Auto-BI Frameworks Powered by Generative Artificial Intelligence for Scalable Self-Service Data Analytics in Large Organizations
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P119Keywords:
Generative Artificial Intelligence (Genai), Automated Business Intelligence (Auto-BI), Self-Service Data Analytics, Large Language Models (Llms), Natural Language Query Processing, Predictive AnalyticsAbstract
The exponential growth of enterprise data has created significant challenges for organizations seeking to derive timely and actionable insights from complex datasets. The conventional Business Intelligence (BI) systems are typically based on expert analysts, formal query languages, and ad hoc dashboard development, which may reduce accessibility and reduce the speed of decision-making. The current developments in Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) offer fresh possibilities to make the traditional BI systems automated, intelligent, and easy to use. The technologies make it possible to interact with data naturally, create insights automatically and dynamically visualizing data, which considerably enhance analytical efficiency. The proposed research paper suggests a Self-service Data analytics-driven Auto-BI framework, which is driven by Generative Artificial Intelligence, to assist in scaling self-service data analytics in large organizations. The proposed architecture will incorporate several layers, such as data sources, data integration pipelines, generative AI analytics engine, BI service elements, and user interaction interfaces. Through the framework, users can enter queries that are in natural language, which are automatically converted to an analytical query via AI-based Natural Language to SQL systems. The system also includes automated dashboard creation, smart data exploration and predictive analytics capabilities to improve the analytical functions of enterprise BI environments. Experimental results and industrial standards show that AI-based generative analytics engines can be of substantial benefit in terms of analytical performance: the query-to-insight latency can be reduced, reporting efficiency can be maximized, and the productivity of the decision-making process can be boosted. The suggested Auto-BI architecture shows how generative AI technologies can make the data more democratic and less reliant on the expertise of the individual technical team, and how an analytical solution can scale to the needs of the modern data-oriented organizations.
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