Organisational Sustainability Modelling for Return on Investment (ROI): Case Studies Presented by a National Health Service (NHS) Trust UK

Victor Chang, David De Roure, Gary Wills, Robert John Walters, Timothy Barry

Journal of Computing and Information Technology · 2011 · 48 citations · 19 references

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Concepts

TL;DR

Organisational Sustainability Modelling is a new quantitative method for measuring cloud business performance. The paper presents a structured ROI methodology for cloud computing and a QA process to ensure data analysis quality. The methodology combines statistical computation, 3D visualisation, and a structured QA process, illustrated through two case studies. The case studies demonstrate that cloud adoption improves efficiency, raises concurrent task benchmarks, and yields incremental improvements in NHS bioinformatics projects with reduced development time.

Abstract

Normal 0 false false false MicrosoftInternetExplorer4 Organisational Sustainability Modelling is a new way to measure Cloud business performance quantitatively and accurately. It combines statistical computation and 3D Visualisation to present the Return on Investment arising from the adoption of Cloud Computing by organisations. The Cloud Return On Investment methodology described in this paper makes use of a highly structured and organised process to review and evaluate Cloud business performance. We illustrate its use with two case studies. The first case study concerns a National Health Service (NHS) Trust UK Infrastructure and confirms that using Cloud infrastructures can improve efficiency. It also results in raising the benchmark, the minimum acceptance level to complete concurrent tasks. The second case study shows 3D Visualisation being used to confirm incremental improvements to an NHS Bioinformatics project. The low risk-free rate may imply code development that allows reduced time to complete, and objective is clearly met and project delivery is straightforward. We introduce a structured Quality Assurance process, and demonstrate how to ensure the quality of our data analysis, which other researchers miss out.

References

19