Using in-memory Computing to Provide Real-time and Actionable Sales Insights

Abstract

The adoption of enterprise resource planning systems in both the public and private sectors aims at having business transactions effectively captured, processed and stored. However, for most businesses, running real-time models on transactional and historical data is time-consuming, often happening overnight to prevent system contention. In order to gain a competitive edge, instant information is key to organizations. It empowers decision-making and improves the quality of the decisions made. This research implemented an analytics dashboard prototype that that uses inmemory computing in the cloud foundry environment to leverage the parallel computing offered by the cloud environment. The prototype was simulated in a multicore environment with 16 and 32 core processing unit cores. The response times for 1, 2, 4, 16, 64 and 128 cores were calculated using Amdahl's law of response times. A survey on the effectiveness of the IMC-based analytics dashboard in the business context was conducted with 20 key business users. The research findings revealed that save for when concurrent load is required and the CPU bandwidth to the memory bus notwithstanding, organizations intending to use in-memory computing technology such as HANA do not need to spend in core processing units acquisition of more than 16 cores. This research also established that real-time analytics and reporting is realized by in-memory technology’s high-end computing performance. Real time information ensures continuous business transparency. It also empowers decision-making at strategic and operational levels as well as improves the quality of the decisions made.