Architecture Framework of Big Data Analytics and Challenges in Big Data Analytics Towards Higher Education System

The term Big Data coined by Roger Magoulas refers to a wide range of large data sets almost impossible to manage and process using traditional data management tools not only due to their size, but also their complexity. Big Data can be seen in the retail, finance and business where enormous data from stock exchange, banking, online and onsite purchasing data flows through computerized systems every day and are then captured and stored for inventory monitoring, customer behaviour and market behaviour. Companies introducing innovative and cutting edge technological solutions to Big Data analytics are increasing. The objective of this paper is to study the emergence of Big data and catalog its role in facilitating Business Intelligence and advanced analytics, where techniques such as predictive analytics, data mining, text analytics, statistics, and natural language processing help to understand the current state of the business and track evolving aspects such as customer behaviour to take productive and persuasive decisions. In addition to the underlying data processing and analytical technologies, Business Intelligence and Analytics includes business-centric practices and methodologies that can be applied to various high-impact applications such as e-commerce, market intelligence, e-government, higher education, and security. Big data analytics in higher education is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome. The paper provides a broad overview of big architecture of big data analytics and is challenges in higher education system.