page_title: Big data
section_title: Characteristics
hierarchical_section_title: Big data / Characteristics
caption_attribution_description: English: Shows the growth of data from a single source towards big data and the associated volume, velocity, and variety associated with it.
context_page_description: Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. When we handle big data, we may not sample but simply observe and track what happens. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value.
  Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.
context_section_description: Big data can be described by the following characteristics:
  Volume
  The quantity of generated and stored data. The size of the data determines the value and potential insight, and whether it can be considered big data or not.
  Variety
  The type and nature of the data. This helps people who analyze it to effectively use the resulting insight. Big data draws from text, images, audio, video; plus it completes missing pieces through data fusion.
  Velocity
  The speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Big data is often available in real-time. Compared to small data, big data are produced more continually. Two kinds of velocity related to big data are the frequency of generation and the frequency of handling, recording, and publishing.
  Veracity
  It is the extended definition for big data, which refers to the data quality and the data value. The data quality of captured data can vary greatly, affecting the accurate analysis.
  Other important characteristics of Big Data are:
  Exhaustive
  Whether the entire system (i.e.,  =all) is captured or recorded or not.
  Fine-grained and uniquely lexical
  Respectively, the proportion of specific data of each element per element collected and if the element and its characteristics are properly indexed or identified.
  Relational
  If the data collected contains common fields that would enable a conjoining, or meta-analysis, of different data sets.
  Extensional
  If new fields in each element of the data collected can be added or changed easily.
  Scalability
  If the size of the data can expand rapidly.
  Value
  The utility that can be extracted from the data.
  Variability
  It refers to data whose value or other characteristics are shifting in relation to the context in which they are being generated.
caption: Shows the growth of big data's primary characteristics of volume, velocity, and variety
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