Empowering small businesses with the force of big data analytics and AI: A technological integration for enhanced business management

Abstract

Small and medium-sized businesses (SMEs) in developing economies still face a number of obstacles that prevent them from adopting digital technologies. In contrast, SMEs have achieved greater success in emerging markets. Due to its potential benefits for numerous businesses, machine learning (ML) has become a hot topic in recent years. Particularly a few major organizations, for example, Amazon, Google and Microsoft have shown a few effective cases on coordinating simulated intelligence capacity in their own organizations. This research suggests a fresh method for bettering private companies by combining large-scale data analysis with artificial intelligence and enhanced safety measures. Here, cloud edge administration with task planning using a dynamic joined real channel Kubernetes obstruction task scheduler improves business for the executives. Then, the organization's security is bolstered by a form of differential encryption on the blockchain that takes into account the need for security. We also propose assigning new jobs to the load node with the lightest workload. Experiments show that our strategy shortens job completion times and distributes work evenly across edge nodes. The experimental investigation is conducted in terms of latency, quality of service, energy efficiency, data integrity, and scalability. The proposed technique attained latency of 0.8354, QoS of 0.9395, energy efficiency of 0.9879, data integrity of 0.1189, scalability of 0.8400.