Datasets or combinations of datasets whose size (volume), complexity (variability) and speed of growth (velocity) make it difficult to capture, manage, process or analyse them using conventional technologies and tools, such as relational databases and conventional statistics or visualization packages, within the time necessary to make them useful.
Veracity
Veracity refers to the quality of the data that is being analyzed. High veracity data has many records that are valuable to analyze and that contribute in a meaningful way to the overall results. Low veracity data, on the other hand, contains a high percentage of meaningless data. The non-valuable in these data sets is referred to as noise.
Example
Data from a medical experiment
Variety
Variety makes Big Data really big. Big Data comes from a great variety of sources and generally is one out of three types: structured, semi structured and unstructured data. The variety in data types frequently requires distinct processing capabilities and specialist algorithms.
Example
CCTV audio and video files that are generated at various locations in a city.
Velocity
Velocity refers to the speed with which data is generated. High velocity data is generated with such a pace that it requires distinct (distributed) processing techniques.
Example
Twitter messages or Facebook posts.
Volume
The volume of data refers to the size of the data sets that need to be analyzed and processed, which are now frequently larger than terabytes and petabytes. The sheer volume of the data requires distinct and different processing technologies than traditional storage and processing capabilities. In other words, this means that the data sets in Big Data are too large to process with a regular laptop or desktop processor
Example
credit card transactions on a day within Europe.