Generating Recommendation Insights as Part of the Software Upgrade and Decommissions Life Cycle in the Information Technology Industry

Abstract

As part of Digital Transformation needs, the Organizations are investing more and more in the
Technology and Infrastructure like software upgrades, software renewals, software replacements,
Cloud migrations etc., apart from investment in Business, People, and Processes. In this context, it is
not an easy task for stakeholders to decide whether to go for a software upgrade or to replace it with
another software. There is no unified approach or solution available today which integrates key
system assets data such as Software Versions, Platform Compatibility, Dependent Software versions,
Investment and Operational Costs, Open defects and fixes, Software Performance Metrics and Service
level objectives. Due to this, the so-called decision making is a tedious process that takes time and
effort. This research paper proposes Software Upgrade and Decommissions Life Cycle and outlines
the requirements, design and build approach for generating recommendation insights. This also
proposes using Data Mining and Machine Learning models on the input data sets that are needed to
take a decision on software upgrade or software decommissioning.