Driving Public Cloud Adoption through Qualitative and Quantitative Modeling

Mike Egbert, Pace University

Abstract

CIO’s and IT managers alike are looking at cloud technologies as a way to reduce IT costs, add flexibility, and reduce their overall IT landscape. Public cloud platforms at leading cloud service provider’s, including Microsoft, AWS, and IBM, offer an attractive cost model that promises to reduce IT costs. However, there are issues that concern CIO’s and IT managers regarding the risks that a public cloud platform may inherently have. There are assumptions and general guidelines concerning public cloud adoption (e.g., security, data leaks, etc.) that are used as determining factors to migrate to a public cloud. Many of the guidelines recommend not migrating critical IT assets to a public cloud for a number of reasons, most relating to risk and security. As a result, IT organizations tend to stay away from migrating critical IT assets to a public cloud; possibly losing the maximum economic benefits that a public cloud can offer. When using assumption and general guidelines to decide public cloud migration strategies, risk and reward is not fully analyzed in a comparative model (i.e., on-premise and cloud service provider comparison), which may result in lost opportunity to migrate critical IT assets to a public cloud and reduce IT costs. The lack of qualitative and quantitative modeling leaves (de facto) assumption and generally accepted guidelines as the decision-making model. This research focuses on enabling IT managers to do quantitative and qualitative analysis through modeling, where IT assets can be analyzed through comparative and sensitivity analysis to understand risk and reward, in an effort to drive public cloud adoption, and enable more economic benefits for reducing IT costs.

Subject Area

Computer science|Business administration

Recommended Citation

Egbert, Mike, "Driving Public Cloud Adoption through Qualitative and Quantitative Modeling" (2015). ETD Collection for Pace University. AAI13905034.
https://digitalcommons.pace.edu/dissertations/AAI13905034

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