Big Data

Understanding Big Data

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 (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[2] Big data challenges include capturing datadata storagedata analysis, search, sharingtransfervisualizationquerying, updating, information privacy and data source. Big data was originallyassociated with three key concepts: volumevariety, 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 anacceptable time and value.

 

Currentusage of the term big data tendsto refer to the use of predictive analyticsuser behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.”[3] Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on.”[4] Scientists, business executives, medical practitioners, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searchesfintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorologygenomics,[5] connectomics, complex physics simulations, biology and environmental research.[6]

Data sets grow rapidly, to a certain extent because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[7][8] The world’s technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[9] as of 2012, every day 2.5 exabytes (2.5×260 bytes) of data are generated.[10] Based on an IDC report prediction, the global data volume was predicted to grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts there will be 163 zettabytes of data.[11] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[12]

Relational database management systems, desktop statistics[clarification needed] and software packages used to visualize data often have difficulty handling big data. The work may require “massively parallel software running on tens, hundreds, or even thousands of servers”.[13] What qualifies as being “big data” varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. “For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.”[14]

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