Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making. And better decisions can mean greater operational efficiency, cost reduction and reduced risk.
Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless sensor networks. The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.
Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires “massively parallel software running on tens, hundreds, or even thousands of servers”. What is considered “big data” varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered “big” one year becomes ordinary later. 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 is a set of techniques and technologies that require new forms of integration to uncover large hidden values from large datasets that are diverse, complex, and of a massive scale
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”. 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