Big Data has long been on everyone’s lips and is the subject of heated debate. While its supporters emphasize the great usefulness of this big data that has emerged with digitalization, criticisms focus on the confidentiality of information.
However, since the espionage practices disclosed by whistleblower Edward Snowden, users have become increasingly concerned for their data. What citizens can hear and read about the Big Data phenomenon is often negative. However, the notion is much more complex than it seems, and this is why a definition of Big Data is essential.
Big Data, literally “big data,” is also called image data or even big data. This data is so complex that software or a conventional hard drive cannot process it. In addition, the notion of Big Data is vague since it can also refer to relatively innocuous amounts of data coming from research. Knowing that the data collected relates to Internet users’ consumption or communication behaviour, the notion is poorly understood. Critics see this data collection as an infringement of their privacy rights.
The notion of Big Data designates sets of data that do not have a properly defined size. In practice, Big Data is often synonymous with a large volume of data because even the gigabit unit of measurement is not enough to measure it.
The volume of digital data has grown considerably. Ten minutes in 2014 were enough to generate as much data as humanity has created since 2002. According to forecasts, this mountain of data is growing steadily and has doubled in the space of two years. Its flow is due to increasing digitization in all areas of the Web. Big Data was born through the fusion of various data sources such as:
Big Data does not only refer to data but also its analysis and use. We try to find models and what connects them to place them in a natural context. The challenge is represented by the large volume of data, processing speed, and the diversity of information. The flow is continuous within unstructured data. They are collected, stored and processed if possible in real-time. A significant data infrastructure is, therefore, necessary to be able to read and relate it correctly.
According to the definition of Big Data, the volumes of this data are so large that conventional software cannot process them. By processing this big data, the program has certain technical requirements imposed on it. Only specific Frameworks can analyze them. The software must work on several data lines at once and make sure that it can import this large volume of data as quickly as possible. In addition, the software must make the data available to users in real-time and, if possible, respond to several database requests at the same time.
Hadoop is a well-known open-source solution. Its implementation is complex and cannot be done without the help of experts, the famous “data scientists.” Other solutions from the cloud are possible. Here is an article that will allow you to see more clearly about Big Data tools.
Big Data is applied in all areas related to the Web. An example of a Big Data tool in e-commerce is the famous phrase “those who bought product X also bought…”. These recommendations are born from the evaluation of millions of purchase data from other customers.
Here are the other areas that benefit from Big Data:
Most of the criticism concerns data protection. Large databases allow companies and brands to tailor their marketing strategies better. However, it is also possible to establish precise user-profiles thanks to the data used for targeting. Those responsible for data protection see this as an invasion of the privacy of Internet users. Whoever works with Big Data must inform customers and users of his site about his data use policy.
Another criticism is the “data dictatorship.” Indeed, the field of big data faces what we call in English, the “big players.” These are companies working with data for several years and making a profit (like Google and other search engines). So these companies have a data monopoly. This sovereignty is often criticized and characterized as a large-scale invasion of privacy. Indeed, suppose no clear rule on protecting personal data is established and taking into account the anonymization of this recovered information. In that case, it is not surprising that abusive use of the data of Internet users is possible.
Despite all the criticisms of Big Data, its use is nonetheless relevant, provided that its technology is used correctly. Certain scientific advances such as cancer research, for example, would never have been possible without resorting to Big Data. This also applies to the supply of energy and traffic forecasts that are regularly optimized and allow us essential safety daily. However, despite the opportunities in these areas, many ethical questions remain. Indeed, it is, for example, possible to predict the contraction of a disease, and this generates concerns for many. The population remains reserved and feared more and more sites are known as “data-hungry octopuses.”
Faced with these societal questions, public authorities are also concerned about the problem of Big Data. They consider that the trust and transparency of web players are currently central. However, understanding computer codes is so complex that the “know it all” policy is irrelevant. The real question today rests on educating citizens so that they can respond intelligently to Big Data.
Also Read : What Does Big Data Mean, And What Are The Advantages Of Big Data?
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