A Technique for Annotating EA Information Models with Goals

Abstract. Many of today’s enterprises experience the need to establish and conduct management processes to ensure closely alignment between business and IT. Enterprise architecture (EA) management provides a model-based approach to understand and evolve the complex dependencies between the enterprise constituents, as e.g. business processes and business applications. In recent years the understanding of EA management in literature and in practice has converged, but up to this point no commonly accepted standard information model for EA management nor a standard set of goals verifying the overall objective of business/ITalignment have been devised. Grounded in indications that such models and goals are highly enterprise-specific, this paper presents a method for flexible combining EA-relevant goals and EA information models to optimally support EA management in a using company

Leveraging business-IT alignment through enterprise architecture—an empirical study to estimate the extents

Achieving business-IT alignment (BITA) as a long-term and appraising management issue can be accomplished in a few ways, enterprise architecture (EA) being one of them. This paper attempts to give a critical understanding of the effects of performing EA on different aspects of BITA maturity through a global survey. A total of 236 respondents from 60 countries, a relatively large response for a survey, were selected. The main purpose of the research is to examine these impacts and to identify directions for innovative practices in the future, the unique contributions of this work. A questionnaire designed on the Luftman’s maturity model as well as various other statistical methods, including PLS path modeling, Wilcoxon matched-pairs signed-ranks test and Mann–Whitney U test, are applied to understand how the EA can deliver benefits. The implications of our findings in this study as well as its limitations are discussed from different viewpoints to enable both academics and practitioners to detect the flaws in the existing EA frameworks and propose improvements