Other researchers consider business intelligence as an umbrella that combines: architectures, tools, data bases, applications, practices, and methodologies (Turban, Aronson, Liang, & Sharda, 2007; Cody, Kreulen, Krishna, & Spangler, 2002; Rouhani, Asgari, & Mirhosseini, 2012). Weiss et al. 2003 define business intelligence as the “combination of data mining, data warehousing, knowledge management, and traditional decision support systems” (Weiss, Buckley, Kapoor, & Damgaard, 2003). Business intelligence systems can have multiple benefits including: faster access to information, particularly big data complexes, increasing revenue, better customer satisfaction and generating or improving competitiveness of enterprises (Brinkmann, 2015).
Intelligence is “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal” (Alnoukari, 2012). The main challenge in any business intelligence solution is in its intelligence ability. This can be found in the post data mining phase where the system has to interpret its data mining results using a visual environment (Alnoukari, 2012). The capability of any business intelligence (BI) solution can be measured by its ability to derive knowledge from data (Azevedo & Santos, 2009). The challenge in any BI solution is to meet with the ability to identify patterns, trends, rules, and relationships from volumes of information which are too large to be processed by human analysis alone (Alnoukari, 2012).
Flexible organization is based on IT alignment with business strategy. As a result of acceleration in the rate of innovation and technological changes, markets evolve rapidly, products’ life cycles get shorter and innovation becomes the main source of competitive advantage (Järvinen, 2014).