Having a dynamic enterprise system looks to many organizations as a far-fetched target. Owing to the vast amounts of factors that need to be controlled across the implementation lifecycle; especially during usage and maintenance phases. On the other hand, advanced analytics techniques, machine learning and data mining, have been proofing strong presence through academic as well as industrial arenas through robust classification and prediction. Correspondingly, this session is set out to address a methodological approach that works on tackling the implementation lifecycle challenges by employing advanced analytics techniques in order to detect business process problems, find and recommend a solution to it, and confirm the solution. The objective is to make enterprise systems smarter & self-moderated by reducing the reliance on vendor support replacing it with intelligent built-in support. The session will profile an advanced analytics engine architecture fitted on top of an enterprise system to demonstrate the approach.
Questions:
- Employing deep learning algorithms together with the enabling components on typical analytics architectures have the potential to support post-live enterprise systems lifecycle
- Correspondingly, this would command for the development of Artificially Intelligent agents that works on analyzing enterprise systems data to prevent them from failure
- The advanced analytics engine will enable enterprises to rely less on external support and hence enhance their ability to sustain their core systems
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