3 research outputs found

    Toward a new Framework of Strategic Alignment of Big Data projects: literature review

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    The notion of strategic alignment is a permanent issue for enterprises, that consists of redesigning a new architecture in order to reach a perfect harmony between the business architecture and the information technology architecture. The strategic alignment is therefore an old issue, that was first cited by Henderson and Venkatraman, in the 70’s, many contributions came along since then by the scientific community. With the emergence of big Data, many scientists focused on how to reach the strategic alignment, based on the new technologies provided by big Data, thus in our contribution, we started to define three main categories that can be used. Those categories can be summarized as reaching strategic alignment either through the big data ecosystem, or through big data analytics capability and finally through the big data transformation. We focused more on the last one, and we managed to propose five bricks that a company can use in order to reach the perfect harmony by doing some efforts on their strategy and business model, on their culture and organization, on their strategy of marketing based on the client experience, and also on their technology choices and their IT infrastructure and finally through the treatment of the data gathered and the establishment of measures.

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

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    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
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