Assessment of Big Data Ecosystem in Iran with Metaphor of Millennium Ecosystem Assessment Model

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Research/Original Article (دارای رتبه معتبر)
Abstract:

With introduction of big data technologies into business sector and developments that have been made in this area, it is necessary to conduct studies on the evaluation of big data ecosystem in the country. Since there is no big data ecosystem assessment model so far, the present paper seeks to present a model of big data ecosystem assessment in Iran using metaphor of the natural ecosystem assessment model. The model for natural ecosystem assessment in this research is the Millennium Ecosystem Assessment model. Using this model, the ecosystem of services offered in big data area in the country has been evaluated with the aim of meeting the needs of the end user. To this end, the Millennium ecosystem assessment model has first been studied. By identifying the key dimensions of the model, the big data ecosystem assessment model has been developed using the concept of metaphor. Then the evaluation steps are described using the developed model. Finally, by examining the industry of big data in the country, the results of the big data ecosystem assessment in the country have been obtained and solutions have been proposed to improve its status. According to the results, it can be said that the largest number of active companies in big data market are the companies that provide infrastructure and analysis services. Therefore, it is necessary to provide the required support for development of transmission, storage and analysis of infrastructures in the country. The most influential driver for providing the big data services in the country is the emergence of new technologies. Technology will affect the performance of the companies and therefore its services, and so the familiarity of the companies with the new technologies and the use of new technological solutions to provide better services to customers can affect the performance of companies

Language:
Persian
Published:
Journal of Information Processing and Management, Volume:34 Issue: 4, 2019
Pages:
1613 to 1642
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