A Review on Business Intelligence and Big Data <s> Challenges of Big Data <s> The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future. <s> BIB001 </s> A Review on Business Intelligence and Big Data <s> Challenges of Big Data <s> BI technologies are essential to running today's businesses and this technology is going through sea changes. <s> BIB002 </s> A Review on Business Intelligence and Big Data <s> Challenges of Big Data <s> The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of "Big Data," including the recent announcement from the White House about new funding initiatives across different agencies, that target research for Big Data. While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is no clear consensus on what is Big Data. In fact, there have been many controversial statements about Big Data, such as "Size is the only thing that matters." In this panel we will try to explore the controversies and debunk the myths surrounding Big Data. <s> BIB003 </s> A Review on Business Intelligence and Big Data <s> Challenges of Big Data <s> It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore’s Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as dataintensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing. <s> BIB004
Although big data offers many opportunities, those who want to benefit from it have to confront its challenges. Traditional database systems, mostly rely on relational database management system (RDBMS), exploit structural data which is easier to handle than unstructured and semi-structured. Moreover, RDBMS has matured over years. But hardware requirement for RDBMS is getting more expensive to handle increasing performance expectations and growing datasets. RDBMS seems restricted in terms of data structure, volume, and heterogeneity of big data. Since traditional databases are restricted and come to an edge of hardware technology, both academia and industry sought to find new ways and paradigms to meet big data requirements. Use of big data's full potential requires some issues to handle such as data policy, technology and techniques, organizational change and talent, access to data, and industry structure BIB001 . Big data challenges can be listed as following  BIB002 BIB004 BIB003 