Enhancing Business Intelligence Through A Semi-Distributed Blockchain Framework for Predictive Analytics On Sales Growth

C, Mullaikodi and Jagannathan, Sharath Kumar and N, Jayanthi. and Singh, Rakhi and Tomar, Yesha and Jaison, B (2024) Enhancing Business Intelligence Through A Semi-Distributed Blockchain Framework for Predictive Analytics On Sales Growth. In: 2024 2nd International Conference on Disruptive Technologies (ICDT), Greater Noida, India.

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Abstract

The cutting-edge technologies in the ever-evolving sector of data analytics has been accelerated by the requirement for efficient and secure techniques of acquiring business intelligence. This approach provides a solution to the existing issues. In order to address the issues of data integrity, security, and transparency that are associated with the existing methodologies, there is a need for a paradigm shift that will lead to a solution that is more resilient. The traditional centralised data analytics solutions have their limitations when it comes to guaranteeing the accuracy and dependability of forecasts, and this is the most important aspect of the situation. A semi-distributed blockchain paradigm is proposed as a solution to the problems that are associated with centralised systems. This paradigm spreads the computing burden while maintaining a certain degree of decentralisation in order to enhance security. The approach that is currently being considered is the construction and deployment of a blockchain network that is semi-distributed and contains data analytics techniques. Smart contracts simplify the processes of data transmission and validation among participants, hence ensuring transparency and confidence in the predictive analytics model. These findings reveal an improvement in the system efficiency, data quality, and accuracy of forecasting.

Item Type: Conference or Workshop Item (Paper)
Subjects: Management Studies > Logistics Management
Divisions: Management Studies
Depositing User: Mr IR Admin
Date Deposited: 07 Oct 2024 11:32
Last Modified: 07 Oct 2024 11:32
URI: https://ir.vistas.ac.in/id/eprint/9370

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