Big Data refers to "a massive, complex collection of data that can't be extracted, stored, searched, ****enjoyed, analyzed, and processed with existing software tools." The industry usually uses the 4 Vs (i.e. Volume, Variety, Value, Velocity) to summarize the characteristics of Big Data.

One is the huge volume of data (Volume). As of today, the volume of data for all printed material produced by humans is 200PB (1PB=210TB), while the volume of data for all words spoken by all humans throughout history is about 5EB (1EB=210PB). Currently, the capacity of a typical personal computer hard disk is on the order of terabytes, while some large organizations have data volumes approaching the order of EB.

Second, there is a wide variety of data types (Variety). This variety of types also allows data to be divided into structured and unstructured data. Compared to the text-based structured data that used to be easy to store, unstructured data is becoming more and more common, including web logs, audio, video, pictures, geolocation information, etc. These multiple types of data put higher demands on data processing capabilities.

Third, low value density (Value). The value density is inversely proportional to the size of the total amount of data. Take the video as an example, a 1 hour video, in continuous uninterrupted monitoring, useful data may be only one or two seconds. How to complete the data value "purification" more quickly through powerful machine algorithms has become an urgent problem in the context of the current big data.

Fourth, processing speed (Velocity). This is the most significant feature of Big Data that distinguishes it from traditional data mining. According to IDC's "Digital Universe" report, it is expected that by 2020, the global data usage will reach 35.2 ZB. In the face of such a huge amount of data, the efficiency of processing data is the life of the enterprise.