Introduction
In the modern context of a rapidly growing amount of information, the term Big Data was originally used as the opposite to conventional information storage (Provost and Fawcett 55). Various contemporary information carriers serve as a means to contain necessary data, and for the ordinary pieces of information, they are of a sufficient size. However, the Big Data refers to the information of such size that cannot be stored or used via traditional means.
It is also important to note that storing such information is crucial for the management of a particular company. In other words, if data is collected on separate carriers, it is impossible to operate it, neither can one analyze or evaluate it quickly enough. For that reason, the Big Data manifests itself in the framework that is entirely different from traditional data storage, analysis, and maintenance.
The Big Data: methods of collection and developments
The Big Data is a way of storing and operating large sets of information. In a broader sense, “data science is a set of fundamental principles that support and guide the principled extraction of information and knowledge from data” (Provost and Fawcett 56).The Big Data functions within the complex systems, it strongly influenced scientific research in various disciplines because of its complexity and possibilities.
With the ability to analyze the Big Data, there is no more need to restructure complex information and compare the data obtained from a number of different sources of such information, but only to concentrate on the integrated complex system of Big Data. Moreover, the data exchange between two complex systems facilitated various kinds of interdisciplinary research, especially in the fields of linguistics, computer science, etc. It can be used in any field that requires operations with the big data sets, including the fact that there is a possibility of utilizing the Big Data techniques for the purposes of resolving various political and governmental issues.
It is important to mention that the Big Data can be collected by using a number of means and techniques, depending on the field of knowledge. However, the primary requirements for the data sets to be rendered as the Big Data is that it cannot be calculated or perceive by the regular means. In such a way, the large sets of data that constitute the Big Data are collected empirically, with consideration for the further analysis.
In the framework of the Big Data, the variations and other structural elements in the data sets manifest its complexity. Therefore, it is collected and calculated by using various testing algorithms that define the disposition and character of the data collected in an empirical way. Additionally, it is important to remember that the Big Data does not restrict itself to “data-mining algorithms” (Provost and Fawcett 55). Moreover, claims that “successful data scientists must be able to view business problems from a data perspective, and there is a fundamental structure to data-analytic thinking, and basic principles that should be understood” (Provost and Fawcett 55).
In such a way, although the data-related science is attractive to a variety of field who insist on theoretical rather than practical research. The fundamental objective of the Big Data, in such a situation, is to define causal analysis must be understood.
In terms of the most recent technological developments will lead to more Big Data, there are different opinions. On one hand, operations with data largely depend on the sphere in which the method is used. For example, “national and international projects such as the Large Hadron Collider (LHC) at CERN, Europe’s particle-physics laboratory near Geneva in Switzerland, or the Large Synoptic Survey Telescope planned for northern Chile, are frequently cited for the way they will challenge the state of the art in computation, networking and data storage” (Lynch 28).
However, on the other hand, it is important to consider the specifics of the techniques itself. From this point of view, among the biggest technological developments will lead to more Big Data in the years ahead, there are emerging means of applying the Big Data in the fields of cyber security, as well as among corporate organizations. For example, “IBM InfoSphere BigInsights (Big Data at rest) and IBM InfoSphere Streams (Big Data in motion) technologies” that provide a sufficient amount of security within the company (Zikopoulos and Eaton 18).
Also, there is an increase in using the Big Data techniques in order to test a level of reliability of different media and large-scale systems, including those of the infrastructure of the separate branches or utilities, as well as the systematic data sets concerning a certain country on the whole. One of such examples is the US with the initiative of using the Big Data for cyber security, namely, for the purposes of incorporating systemic elements of the organizational structure and increasing cyber security in states that require it the most.
One of the implications of such growth is that “the Big Data era has quietly descended on many communities, from governments and e-commerce to health organizations” (Chen, Chiang, and Storey 1168).Therefore, on many levels, there are some advantages of using the Big Data is an artificial intelligence technology analog.
Collecting the Big Data
The Big Data is often used for the purposes of systematic data analysis, commerce, science and research, business planning, and public security. Therefore, it is important to know that it is should have an account of a number of conditions. One of the most commonly spread methods is the data-driven approach. It is majorly used by the governmental agencies, security offices, and various projects, whose main objective is to take account of the maximum number of people.
On the other hand, in the situation where large sets of data constitute a list of products or some other operational data, the approach can also be effective. The main challenges and risks that the Big Data needs to overcome in terms of this application of such technique include keeping the privacy of the information (in the situation with taking an account of people), and various challenges related to visualization and data curation (if the situation involves products or services). At present, some of the largest “firms such as Google, Amazon, and Facebook continue to lead the development of web analytics, cloud computing, and social media platforms” (Chen, Chiang, and Storey 1169).
The Internet of Things: its impact on the amount and type of the Big Data
The concept of the Internet of things refers to the Internet not as to its contents but as a physical presence. In other words, this framework studies physical aspects of the Internet, as well as it attempts to include all the implications connected with using various software and hardware, electronics, conductors, and channels in order to understand how it affects the data-related operations.
Of course, there are a couple of perspectives, in which the Internet of things and the Big Data can interact. First of all, considering that it is hard to estimate precisely the size of the Internet of things, it cannot be represented as a set of data itself. On the other hand, separate sets of data exist within the Internet of things, without particular manifestation since they are not physical objects, as it were.
According to McAfee et al., “more data cross the internet every second than were stored in the entire internet just 20 years ago” (62). Thus, one of the key elements connecting the Internet of things and the Big Data is their interdependence. While the physical system of the Internet of things cannot be embraced by the Big Data, there are also some challenges concerning the fact that it interacts, in this case, with the consumers’ technology.
In other words, one aspect is that it is important to filter and protect the personal data of the consumers, whereas the other side of the situation is that the Big Data is to capture all the important information.
For that reason, the Internet of things is an intermediate physical media through which the Big Data can manifest itself, and the companies engaged in the Big Data’s collections can reach out to their target audiences and customers. Overall, the Internet of things increased the amount of information at the operational capacity of the Big Data algorithms, which means that it affects an increasing dynamics of creating new data sets.
On the other hand, a growing amount of information is more difficult to capture, and as a result, it makes the Big Data algorithms become more effective in choosing relevant information and in protecting personal data. In particular, it concerns the sphere of media, where the systems need to develop and use more elements of artificial intelligence in order to maintain the standards of behavioral and target strategies related to displaying advertising on the Internet or other forms of predicting consumers’ behavior.
The Big Data’s impact
There are a number of pros and cons associated with the implementation of the Big Data at a global scale. First of all, with the rapidly emerging technologies and even more rapidly changing consumer demand, the Big Data gains an especially influential role in the spheres of e-commerce and business, when it is used for the purposes of the market analysis or consumer preferences, as well as for defining and targeting existing audiences (Mayer-Schönberger and Cukier 56).
However, on the other hand, the Big Data itself produces content that requires additional operational units. In such a way, despite the Big Data structures the information into data sets, information appears faster than it can do it. That is one of the principal challenges of the conception that poses especially high demands in front of application of the Big Data in the sphere of cyber security, alongside all the risks associated with losing track of personal data that the Big Data bears in this respect. Nevertheless, the growing demand for the Big Data technologies would provide working environment for the people interested in collecting, analyzing, and interpreting data.
Conclusion
In conclusion, the phenomenon of the Big Data helps to manage the large sets of data in various spheres. Overall, there are more positive than negative implications of applying the Big Data in different spheres of public life. However, considering all the risks and challenges of the Big Data technologies at their current stage of development, it is important to monitor more the spheres with the highest risks (such as cyber security), as well as introduce the changes gradually.
Works Cited
Chen, Hsinchun, Roger HL Chiang, and Veda C. Storey. “Business Intelligence and Analytics: From Big Data to Big Impact.” MIS Quarterly 36.4 (2012): 1165-1188. Print.
Lynch, Clifford. “Big Data: How Do your Data Grow?” Nature 455.7209 (2008): 28-29. Print.
Mayer-Schönberger, Viktor, and Kenneth Cukier. Big Data: A Revolution that Will Transform how We Live, Work, and Think. Boston, Massachusetts: Houghton Mifflin Harcourt, 2013. Print.
McAfee, Andrew, Erik Brynjolfsson, Thomas Davenport, David Patil, and Dominic Barton. “Big Data.” Harvard Business Review 90.10 (2012): 61-67. Print.
Provost, Foster, and Tom Fawcett. “Data Science and its Relationship to Big Data and Data-Driven Decision Making.” Big Data 1.1 (2013): 51-59. Print.
Zikopoulos, Paul, and Chris Eaton. Understanding Big Bata: Analytics for Enterprise Class Hadoop and Streaming Data. New York, New York: McGraw-Hill Osborne Media, 2011. Print.