Big Data and Knowledge Resource Centre

Defining Big Data

Big data is a hot concept in recent years. Big data is an abstract term and there are different definitions for different researchers due to different concerns. According to McKinsey, big data is used to refer to datasets whose size is large and exceed the capability of existing data analytics tools to mine, collect, store, process and analyze within a specific amount of time. Gantz and Reinsel (2011)defined big data as big data technologies which describe a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery and analysis. Chenet al. (2014) noted that big data is used to describe an unprecedented amount of data and it includes masses of unstructured data and semi-structured data that need to spend more real-time to conduct the analysis. Chen and Zhang (2014) indicated that big data is a bundle of huge data sets that come from different sources and with various formats so that it is difficult to use the traditional data analytical systems, tools and techniques to conduct big data processing and analysis. Pawar (2016) noted that big data is typically a data with large volume and it is difficult to mine, collect, maintain and manage by analysis tools or techniques. Roughly speaking big data typically refers to more or too much data than what by conventional means as a basis for useful information or reliable knowledge. The adjective big in Big Data implies a dynamic and shifting meaning, depending on possibly highly individual circumstances, advancing technology available storage capacity, processing and other cultural or historical contingencies.