Enterprise transformation through a Zachman-Bayesian framework to improve efficiency & productivity
Although organizations are aware of the benefits of enterprise architectures models, organizations have difficulties determining deficiencies in their business architecture. Organizational assessment tools often do not achieve expectations as sold to management. Performance measures often fail to properly represent necessary holistic measurements. Methods for capturing deficiencies in current business models and deterring improvements to optimize business processes are rare. This thesis develops a new experimental architecture modeling approach for enterprise transformations. The approach is efficient in capturing deficiencies and finding correlations which can become targeted goals for improvements. The Zachman enterprise architecture framework provides a holistic view of organizational models and Bayesian methods provide an efficient way to experiment with impact translations in order to find and utilize correlations among major organizational aspects. This work provides a new direction for organizations to model impacts or benefits obtained when investments are made in the technology, business, operational and systems areas.
Gona, Ramakanth, "Enterprise transformation through a Zachman-Bayesian framework to improve efficiency & productivity" (2011). ETD Collection for University of Texas, El Paso. AAI1503720.