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.
Subject
Using in-memory ComputingRights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
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