Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data "size" is a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data. Big data requires a set of techniques and technologies with new forms of integration to reveal insights from data sets that are diverse, complex, and of a massive scale.

In a 2001 research report and related lectures, META Group (now Gartner) analyst Doug Laney defined data growth challenges and opportunities as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources). Gartner, and now much of the industry, continue to use this "3Vs" model for describing big data.In 2012, Gartner updated its definition as follows: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization." Gartner's definition of the 3Vs is still widely used, and in agreement with a consensual definition that states that "Big Data represents the Information assets characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value". Additionally, a new V "Veracity" is added by some organizations to describe it,revisionism challenged by some industry authorities.The 3Vs have been expanded to other complementary characteristics of big data.

- Volume: big data doesn't sample; it just observes and tracks what happens
- Velocity: big data is often available in real-time
- Variety: big data draws from text, images, audio, video; plus it completes missing pieces through data fusion
- Machine Learning: big data often doesn't ask why and simply detects patterns
- Digital footprint: big data is often a cost-free byproduct of digital interaction

The growing maturity of the concept more starkly delineates the difference between big data and Business Intelligence

- Business Intelligence uses descriptive statistics with data with high information density to measure things, detect trends, etc..
- Big data uses inductive statistics and concepts from nonlinear system identification
^{[24]}to infer laws (regressions, nonlinear relationships, and causal effects) from large sets of data with low information density to reveal relationships and dependencies, or to perform predictions of outcomes and behaviors.

In a popular tutorial article published in IEEE Access Journal,the authors classified existing definitions of big data into three categories: Attribute Definition, Comparative Definition and Architectural Definition. The authors also presented a big-data technology map that illustrates its key technological evolution.

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 actually 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.

- Velocity
- In this context, 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.

- Variability
- Inconsistency of the data set can hamper processes to handle and manage it.

- Veracity
- The quality of captured data can vary greatly, affecting accurate analysis.

Factory work and Cyber-physical systems may have a 6C system:

- Connection (sensor and networks)
- Cloud (computing and data on demand)
- Cyber (model and memory)
- Content/context (meaning and correlation)
- Community (sharing and collaboration)
- Customization (personalization and value)

Data must be processed with advanced tools (analytic s and algorithms) to reveal meaningful information. For example, to manage a factory one must consider both visible and invisible issues with various components. Information generation algorithms must detect and address invisible issues such as machine degradation, component wear, etc. on the factory floor.

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