Big data (BD) and data analytics (DA) are increasingly trying to make their way into public policymaking, as well as there are demands for systematic evaluations and research agendas concentrating on the effects of BD and analytics on policy formation. Because of the rapid expansion of such data, various methods for extracting essential information and values from big data sets must be developed. Additionally, decision-makers must've been able to derive some meaningful insight from such huge and continuously changing data, which varies from ordinary transactions to customer interactions, including data from social networking sites. It is possible to achieve a such vision through the application of Big Data Analytics, that is deployment of Advanced Analytics Methods to massive volumes of data. The dilemma today is how to generate a large infrastructure for effectively analyzing large data and how to construct a suitable mining technique to extract relevant information from huge data. This paper continues with a quick overview of BD & then moves on to topics about large data analytics. Some critical outstanding challenges and future research paths for BDA will also be highlighted. This paper also attempts to investigate some of disparate analytics methodologies and tools that may be applied to BD, &the value given by BDA applications in various decision domains.

Big data, Neural network (NN), machine learning (ML) techniques, data analytics, data mining (DM), Deep Learning (DL)