Abstract: Consumers are increasingly using internet applications for ecommerce, mobile, social and business computing. As a result, a large amount of usage data is being gathered and aggregated by ISP’s (Internet Service Providers). However, due to the high velocity, massive volume and highly dispersed nature of this “big data”, organizations need to adopt new distributed cloud based analytics tools to access and process these data sets. Organizational benefits can result from improved estimates of market demand, identification of client preferences and business trends. Several cloud providers, such as Google, Microsoft, SAP and IBM Watson are advancing analytics tools that can be used by organizations to utilize these big data sets. Most cloud based tools offer convenient, ubiquitous and on-demand access to data sets and services. However, typical challenges include information security, integration and availability, data veracity and the need to build new IT infrastructure and capabilities within the organization. This study applies the TOE framework to identify and rank the factors that impact the adoption of such cloud based analytics tools. The TOE framework identifies three determinants of IT system adoption at the organizational level – Technology, Organization and Environment. Using a survey of medium to large sized companies in a variety of industries, this study finds that having compatible IT infrastructure components and internal firm capabilities for the secure integration of cloud based analytics data and tools and vendor support strongly facilitates the adoption of cloud based analytics, while the lack of an analytics culture and management support can hinder it.
Keywords: Big data, Business analytics, Cloud computing, TOE Framework
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Recommended Citation: Ghosh, B., (2018). Exploratory Study of Organizational Adoption of Cloud based Big Data Analytics . Journal of Information Systems Applied Research, 11(3) pp 4-23. http://jisar.org/2018-11/ ISSN: 1946-1836. (A preliminary version appears in The Proceedings of CONISAR 2017)