Introduction
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
What is Big data?
Defination
As noted above , Big Data that is a collection of data capacity in excess of those assumed applications and traditional tools . Size of Big Data is increasing day by day , and by 2012 , it size was estimated around a few dozen terabytes to multiple petabytes ( 1 petabyte = 1024 terabytes ) only for a set of data only.
In 2001 , Doug Laney analyst firm META Group ( now the research company Gartner ) has said that the challenges and opportunities in data growth can be described in three dimensions : increased the amount ( volume ) , increasing the speed ( velocity ) and increased in variety ( variety ) . Now Gartner , along with many other companies and organizations in the field of information technology continue to use the " 3V " to be defined Big Data . By 2012 , Gartner added that apart from Big Data on the remaining three properties to " require new forms of treatment to help to make decisions , to explore deep into things / events and optimize the workflow " .
We can take the experiments of large particle accelerator ( LHC ) in Europe as an example of Bi...
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...rces of Big Data . This huge amount of data possible for researchers to know the consumer behavior of customers , thereby refining the Internet of Things devices more suitable , we began serving daily lives of us more effectively . It can also be used for the production , thereby reducing human involvement . In the words of Daniel Kaufman predicted it " will do little more human " by Big Data .
Conclusion
In short, the Big Data challenges for organizations and enterprises in today's digital age. Once mastered big data, they will have greater chances of success in today's competitive environment, the world would benefit more from the extracted information more accurately, more useful lower costs. Still the criticism revolves around Big Data, however, the field is still very new and we'll see in future Big Data will evolve like.
References
As the technology developing, everything becomes computable. And when people realizing the importance of the Internet of Things, more and more data is collected. Analyzing such amount of data becomes a big challenge for modern people. As a very important component of our life, internet becomes indispensable. Data sharing between multiple users becomes more popular. It seems our life will stop if without the internet. The user devices becomes much lighter, most computing and data storage are separated with remote operations. Distributed system becomes more and more useful for our life.
Sallam, Rita L; Tapadinhas, Joao; Parenteau, Josh; Yuen, Daniel;Hostman, Bill (2014, February 20). Magic quadrant for business intelligence and analytics platforms. Retrieved from http://www.gartner.com/technology/reprints.do?id=1-1QLGACN&ct=140210&st=sb
One of the biggest problems that affect everyone is data aggregation. The more the technology develop, the powerful and dangerous it gets. Today there are many companies that aggregate a lot of information about us. Those companies gathering our data from different sources, which create a detailed record about us. Since all services have been computerized whether it is handled directly or indirectly through computers, there is no way to hide your information. We used computers, because they are faster, better, and accurate more that any human being. It solved many problems; however, it created new ones. Data does not means anything if it stands alone, because it is only recoded facts and figure, yet when it organized and sorted, it become information. These transformed information. Data aggregation raises many questions such as, who is benefiting from data aggregation? What is the impact on us (the users)? In this paper I will discuses data aggregation and the ethics and legal issues that affect us.
Information technology has advanced in multiple ways in society, where organizations has implement the structure into their work environment. Industries have outsource their manufacturing to other places in the world and rely on telecommunication to keep the marketing. The geographic distribution has changed significantly by reducing the distance it takes to complete an operation, due to information technology. These are just a couple of examples of how this advanced technology has reshape our society and continuing.
CERN- Quote: “The world’s largest scientific research facility- Switzerland’s Conseil Europeen pour la Recherche Nucleaire (CERN)- recently succeeded in producing the first particles of antimatter.” [Brown FACT].
The Internet, a tool used by most of the population every day, collects its data thanks to the recompilation of data provided by servers and transmitted by satellites. Some of this information is provided by NASA funded projects. According to the NASA Spin Off page, NASA’s Earth Observing System Data and Information System collects and archives information of the Earth’s atmosphere, oceans and vegetation on a daily basis. The massive amount of data accumulated has reached 4.5 petabytes. That’s equivalent to completely filling 90 million four-drawer file cabinets with paper.
This essay will explore the varied criteria attached to the definition of Big Science. With such a vast array of opinions on the subject, an attempt will be made to simplify and rationalise a specific definition. Examples of The Manhattan Project and the research conducted at CERN will be investigated to this end, and the former will be examined for its perceived effect on Big Science.
If auditors can look at a complete population, they may not have a great defense if they missed a “smoking gun” since they looked at all the data (Alles and Glen). However, this data may not be valid which raises the importance of the auditor understanding where the data came from and how reliable it is. Not only this, it will be interesting to see how standards consider big data evidence. While it most likely will not be as reliable as confirmations, it would be a challenge to figure out how much the auditors could rely on it. Furthermore, higher education would most likely play a role in helping their graduates understand data and how to use technology to be not only more efficient but also ensure they are able to use sound professional judgement while using big data.
You may ask what big data analytics is. Well according to SAS, the leading company in business analytics software and services describes big data analytics as “the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.” As the goal of many companies which is to seek insights into the massive amount of structured, unstructured, and binary data at their disposal to improve business decisions and outcomes, it is evident why big data analytics is a big deal. “Big data differs from traditional data gathering due to that it captures, manages, and processes the data with low-latency. It also one or more of the listed characteristics: high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, web, and social media which much of it is generated in real time and in a very large scale.”(IBM) In other words, companies moving towards big data analytics are able to see faster results but it continues to reach exceptional levels moving faster than the average person can maintain.
Currently the world has a wealth of data, stored all over the planet (the Internet and Web are prime examples), but it is needed to be understand that data. It has been stated that the amount of data doubles approximately
“ In 2010 the amount of digital information created and replicated worldwide was nearly 1,203 exabytes, (an exabyte is billion gigabytes or 1018 bytes)” IDC [1]
...ch Reips. ““Big Data”: Big Gaps of Knowledge in the Field of Internet Science.” International Journal of Internet Science 7.1 (2012): n. pag. Web. 16 Mar. 2014.
Big data will then be defined as large collections of complex data which can either be structured or unstructured. Big data is difficult to notate and process due to its size and raw nature. The nature of this data makes it important for analyses of information or business functions and it creates value. According to Manyika, Chui et al. (2011: 1), “Big data is not defined by its capacity in terms of terabytes but it’s assumed that as technology progresses, the size of datasets that are considered as big data will increase”.
Companies have transformed technology from a supporting tool into a strategic weapon.”(Davenport, 2006) In business research, technology has become an essential means that many organizations use in their daily operations. According to the article, Analytics is a major technological tool used. It is described as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions."(Davenport, 2006) Data is compiled to enhance business practices. When samples are taken, they are used to examine research and understand how to solve problems or why situations are as they are. Furthermore, in this article, Thomas Davenport discusses analytics from a business standpoint. He refers to organizations that have been successful in their usage of data and statistical analysis. In addition, he also discusses how data and statistics can be vital in the efforts to improve the operations of businesses.
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?