The phrase ‘big data’ describes large volume of data, and in the last decades it came to refer to data of a very large size, whether structured or unstructured, collected from a significant amount of sources, to be further processed and analysed by machines, to draw out and better understand trends and patterns.
For example, in the world of business, ‘Big Data’ is synonymous to ‘business analytics’, yet on a larger scale and with more technology involved. Bid Data is most often stored in computer databases and analysed using software specifically designed to handle complex data sets. And it is the turning of raw data into useful information, the so-called ‘data mining’, to produce the insights that are at the heart of modern big business.
The term Big Data is to be first met in print in the 1980s’ The Old New Social History and The New Old Social History working paper by Charles Tilly, University of Michigan, where in a keynote address to a Conference on New Directions in History referring to analyses of fertility conducted across the USA with the huge collection of machine-readable evidence, the author states that: “the American reputation for Big Data and bigger research teams has been greatly exaggerated.”
It took nearly 20 years the term to make it to a headline, when John Masey presents at a USENIX meeting a paper titled “Big Data… and the Next Wave of Infrastress” (the 1998 presentation could be viewed here) and the coining of the term is indeed most often credited to have originated in the lunch-table conversations at Silicon Graphics, where Masey was a Chief Scientist.
A panel, titled Automation or interaction: what’s best for big data? and presented at the IEEE 1999 San Francisco conference on Visualization was followed by a paper titled ’Big Data’ Dynamic Factor Models for Macroeconomic Measurement and Forecasting, where the term was presented to the Eighth World Congress of the Econometric Society and defined by Francis X. Diebold as: “Big Data refers to the explosion in the quantity (and sometimes, quality) of available and potentially relevant data, largely the result of recent and unprecedented advancements in data recording and storage technology”
And two years later, in Critical Questions for Big Data, the definition came as: “a cultural, technological, and scholarly phenomenon that rests on the interplay of:
(1) Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets.
(2) Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims.”
Big Data is, indeed, a phenomenon, but it is not the amount of information that is important, but what business and society do with it. In the realm of business, Big Data analysis has the potential to enhance the work of every department in a company. It enables corporations to collect better market and customer intelligence, by insights on customers’ behaviour and hence better strategic moves to improve and customize their products and services and the customer experience. Big Data improves internal efficiency and operations, supports decision making, ensures better data safety and etc.
And the phenomenon is expanding rapidly, with the “Worldwide Big Data and Business Analytics Market” or BDA, poised to grow from $130.1 billion this year to over $203 billion in 2020.
