Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. Big data analytics applications enable data scientists, predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data, plus other forms of data that are often left untapped by conventional business intelligence (BI) and analytics programs. For example, internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile-phone call-detail records and machine data captured by sensors connected to the internet of things.
Potential pitfalls that can trip up organizations on big data analytics initiatives include a lack of internal analytics skills and the high cost of hiring experienced data scientists and data engineers to fill the gaps. The amount of data that's typically involved, and its variety, can cause data management issues in areas including data quality, consistency and governance; also, data silos can result from the use of different platforms and data stores in a big data architecture. In addition, integrating Hadoop, Spark and other big data tools into a cohesive architecture that meets an organization's big data analytics needs is a challenging proposition for many IT and analytics teams, which have to identify the right mix of technologies and then put the pieces together.
Intelligent technologies are used to develop machines that can substitute for humans. It is believed that the main factors involved in "intelligence" are the capabilities of autonomously learning and adapting to the environment. Robots have been invented to substitute humans in performing a lot of tasks involving repetitive and laborious functions, for example, pick-and-place operations in manufacturing plants. However, robots that are operated based on a programmed manner and in a fully controlled environment are not considered as intelligent machines. Such robots will easily fail when the application and/or the environment contain some uncertain condition. Robots/machines that can react to changes in their surrounding are very much needed. As a result, systems have to be equipped with "intelligence" so that they can be more useful and usable when operating in uncertain environments as they autonomously learn to adapt to changes. Big data and its role in developing intelligent machines is the current buzz. Together, they make the world a better place to live.