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Big Data and Its Use in Analytics Essay

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Updated: Jul 4th, 2020

The definition of big data includes data sets, whose size exceed the ability of frequently used software to contain, manage, process, and operate in an appropriate period. The size of big data is constantly growing, and it can grow in three dimensions, i.e. volume (its quantity), variety (types and sources of data), and velocity (Pettey and Goasduff par. 6-8). Big data does not present any samples, it is mere evidence of the events that occur, and it is a byproduct of those events. It can be text, video, audio or images. Big data is stored in real time. Among the most common examples of big data are such things as the constant stream of messages in social webs, meteorological data, the data on the location of the subscribers of mobile webs, the data of the remote sensing of the Earth, etc. (McKinsey Global Institute 7-8).

Big data can be driven from a wide variety of sources. According to Mark van Rijmenam’s infographic, among the main sources are the following. Archives: archives of scanned documents, statements, insurance forms, medical record, customer correspondence, and stream print files. Docs: PDF, XLS, HTML and HTML5, CSV, JSON, XML, plain text, etc. Media: images, videos, audio, podcasts, live streams, etc. Data storage: Hadoop, SQL, NoSQL, file systems, doc repository, etc. Business applications: project management, human resources management, marketing control systems, productivity, talent management, disbursement management, etc. Social media: Tumblr, Twitter, Facebook, Linkedin, blogs, Google+, SlideShare, Instagram, YouTube, etc. Machine log data: audit logs, server data, event logs, application logs, call detail records, business process logs, mobile app usage, mobile location, etc. Sensor data: the data collected by devices, such as satellites, traffic cameras, etc. Public web: official sources, such as government, meteorological services, traffic, health care system, finance, World Bank, Wikipedia, etc. (see Table 3).

The measurement of big data can be performed by calculating the amount of transactions, records, files or tables in a particular set of data. With the increase of the given data in volume, the technology of counting should advance (“Data Equity” 15). To quantify the big data, it is necessary to use the units of measurements commonly used to count information, i.e. bits and bytes. However, the difference between regular data and big data is the enhanced volume of the latter, and that requires the use of big units. While the units used to count regular data range from a bit to a terabyte, for big data we may use petabytes (10005 bytes), exabytes (10006 bytes), zettabytes (10007 bytes), and yottabytes (10008 bytes).

Big data are used for a number of purposes. Businesses use it to find out the preferences of their potential customers, to study their positions in the market, and to develop their future marketing strategy (Brown, Chui, and Manyika par. 5). They are also used by the government to explore the needs of the population and the current state of the population, and in the time previous to the election big data are used as well. Security agencies use big data for their work. In Dubai, big data are widely used to conduct analytics related to the oil and gas production in order to ensure certain revenue, predicts risks, and improve safety (“Big Data” par. 2). In the new Dubai airport, it is planned to use big data to assign gates more efficiently (“Big Data and Analytics” par. 3).

Works Cited

2015. Web.

Big Data. Analytics for Oil & Gas 2015. Web.

Brown, Brad, Chui, Michael, and James Manyika. McKinsey Quarterly, 2011. Web.

. Executive Summary 2012. Web.

McKinsey Global Institute. Big data: The next frontier for innovation, competition,and productivity. New York City, New York: McKinsey & Company, 2007. Print.

Pettey, Christy and Laurence Goasduff. 2011. . Web.

Van Rijmenam, Mark. n.d. . Web.

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