Discovering Insights within a Blue Ocean Based on Business Intelligence

Abstract

At this moment we are seeing a confluence of practices and technologies into smarter computing capabilities that enable organizations to accomplish intelligent actions to address time-sensitive business processes and benefit from analytics. Business Intelligence processes within companies allow obtaining blue oceans where a strong growth and high business benefits are pursued through the creation of a new demand in an unknown strategic space, instead of facing competitors pursued to achieve customers in an already developed activity. This article describes a case study where the behavior of virtual visitors from the BRIC (Brazil, China, India and Russia) market into one of the most important country websites focusing its activity on tourism. Through the study of web analytics on the website, it will be showcased different interests identified via digital footprint left by the virtual visitors and depending on their country of origin. This fact will allow harmonizing the content and supply of tourism resources of the web site with regard to virtual visitors from these markets. Ultimately, it will allow to direct actions of specific emarketing to the BRIC market.

Algorithms for frequent itemset mining: a literature review

“…When the company stakeholders are able to make fact-based decision using the capability of data analytics, the company will have a greater likelihood of increased revenue, cost-cutting, and improved competitive advantage. To date, data analytics has been widely adopted to support the operations in many businesses or industries like healthcare (McGlothlin and Khan 2013), electricity supply (Qiu et al 2013), manufacturing (Jesus and Bernardino 2014), railway safety management (Lira et al 2014), financial service (Chang 2014), tourism (Rebón et al 2015), education (Haupt et al 2015), monitoring of quality for web services , monitoring for quality-of-experience in high-density wireless network (Qaiyum et al 2016) and even not-for-profit organizations (Oakley et al 2015). Figure 1 depicts the entire implementation process of a typical data analytics practice (Gullo 2015).…”