Reconnoitring the Higher Education Systems using Big Data Analytics

Now-a-days large amounts of data are needed to be gathered due to the increased competition and thus many companies are having lots of terabytes of data to be stored and analysed. There is lots of data generated at high volume, velocity and variety based on the sources it is coming from. This is the scenario where we need complex analytics to deal with Big data. So far, most database management innovation has not kept pace. Performing ad hoc queries on such large data volumes does not work naturally for existing database management systems (DBMS), which use a row-oriented design for write-intensive transaction processing rather than for read-intensive analytics. The traditional view of big data is not enough. Rather than focusing exclusively on what technology big data brings, it has to be looked as to what value it can create. Big data is insignificant in a vacuum. Its potential is unlocked only when leveraged to drive decision making. To enable such evidence- based decision making, organizations need efficient processes to turn high volumes of fast- moving and diverse data into meaningful insights. Thus, the current research explores mainly on big data analytics, the main opportunities it gives rise to, and how big data should be expanded to support analytics. This research paper presents application of big data analytics on Higher Education aspects to choose more education models, which can enhance the performance of the instructors and students. In spite of the increasing opportunities for instructors and students, online learning also brings challenges due to the absence of direct human contact. Online environments allow the generation of large amounts of data related to learning/teaching processes, which offers the possibility of extracting valuable information that may be employed to improve students performance. This paper includes various analytics approaches and the educational data mining and learning analytics.