The data-drivendecision-making process is widely under recognition now these days, and theterm `Big Data’ is on the top most level for running these kind of data-drivendecision-making process.
Big data can be derived from multiple sources, and mayhave its multiple formats. It is a high-volume, high-velocity and/orhigh-variety information assets that demand cost-effective, innovative forms ofinformation processing that results insight enhancement, decision-making, and makethe process automatic. High-performanceanalytics lets you do things you never thought about before because the datavolumes were just way too big. For instance, you can get timely insights tomake decisions about fleeting opportunities, get precise answers forhard-to-solve problems and uncover new growth opportunities – all while usingIT resources more effectively.Despite the much laudedpotential, using big data has brought huge challenges in terms of dataacquisition, management, process, storage and analysis. Big data is generallyan unstructured data, unlike traditional data in its characteristics ofhigh-volume, high velocity, high-variety of sources and the requirement tointegrate all of it for analysis.
Analytical systems and traditional datamanagement are based on relational and structured database systems. Thosesystems are not designed for the huge volume and heterogeneity of big data. Technical Institutions areoperating in an increasingly complex and competitive environment.
This paperidentifies contemporary challenges being faced in technical institutions globallyand describes the potential of Big Data in defining these challenges. The paperthen explores a number of opportunities and challenges associated with theimplementation of Big Data in the context of higher education at global level.The paper concludes byoutlining future directions relating to the development and implementation ofan institutional project on Big Data.