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Companies currently have to deal with profound changes in the way they manage their business, their customers and their business models, since they are overrun by a data-driven revolution in management. This revolution is due to the wide availability of big data and the fast evolution of big data technologies. Big data is recognized as one of the most important areas of future technology, and is fast gaining the attention of many industries, since it can provide high value to companies. This article investigates the adoption levels of big data technologies in companies, and the big data sources used by them. This article also points out the most frequently recognized strategic, transactional, transformational and informational benefits and risks related to the usage of big data technologies by companies. In order to achieve these aims, the paper looks at the differences that exist among companies of different sizes, by comparing medium-sized and large companies, and the differences among companies of different industrial sectors. It provides evidence that only in a few cases these differences are significant. This study could serve as a reference for managers who wish to initiate an evaluation cycle on the adoption and usage of big data technologies.
The benefits and the risks of big data technologies should not be underestimated by companies that decide to make investments in big data technologies. The risks and benefits of the use of IT in organizations have received a great deal of attention from academics and practitioners, but there is still scant empirical evidence on the risks and benefits of big data technologies chosen by companies and on evaluating whether differences exist according to the size and the industrial sector of the companies. Previous studies investigated two main aspects related to big data. First, they looked at the financial impacts of big data in companies (Ji-fan Ren et al., 2016; Tambe, 2014). Second, prior work was conducted to provide insight into big data challenges and opportunities in a qualitative and aggregated way (Chen, Chiang, & Storey, 2012; Gandomi & Haider, 2015), thus demonstrating that there is still a lack of empirical evidence on these issues. Considering this research gap, the present research offers an opportunity to empirically understand more about the adoption of big data technologies in companies, and about their benefits and risks. This has been achieved by investigating whether any differences exist according to the size and the industrial sector. The results shed light on the adoption level of big data technologies and demonstrate that the most diffused technologies are visual analytics technologies, scripting languages, and in-memory analytics software. The big data sources that companies use the most are online portal contents, POS data and smart meter data. This result highlights that companies are still more likely to use big data that are proprietary than to buy other data on customers from third parties, such as those that can be produced by the social media.