How many pictures have you uploaded and how many words have you posted today? How many posts have you read and how many pictures have you looked through? I sometimes feel dizzy after hours spent online when simply looking for a good restaurant to dine out or a film to watch with my partner. Raise your hand those who feel there is too much information on the Internet.
The abundance of data has become a topic of debate over a decade ago. The creation of the World Wide Web was the premise for the emergence of big data. People stored data in various written sources (books, articles, encyclopedias, or even brief notes). In the 1970s, computers were used to store and process data (Chen, Mao, & Liu, 2014). Clearly, the data could hardly be shared. Moreover, the volume of data was growing exponentially, and the existing hardware and software could not adequately process the existing information.
The development of the Internet enabled people to store data online. In the 1980s, the World Wide Web was regarded as a good solution to the problem, but it soon became clear that big data should be managed properly. Search engines were among the pioneers who developed effective strategies to manage big data. They used different algorithms to analyze the available data. Different types of information in large quantities have been stored in numerous databases, cloud systems and so on. Scientists, business people, officials, companies, agencies, and individuals wanted to store, share or access information. Individuals want to share their emotions and memories while companies want to analyze their existing and potential customer’s behaviors and desires, and so on. Of course, such organizations as NASA need to store and analyze an overwhelming volume of information. It has been estimated that the volume of data stored online grew by approximately nine times between 2006 and 2011 (Chen et al., 2014). It is also expected that the amount of information will double every two years.
Interestingly, although big data have been analyzed and discussed for decades, there is still no definition universally accepted. When analyzing big data, some utilize the so-called 3Vs (Volume, Velocity, and Variety) model. According to this model, big data can be characterized by a constant increase in the amount of information. This increase takes place at a significant pace, and the information becomes more varied. However, in the 2010s, a new model including four Vs was developed. The four Vs stood for Velocity, Volume, Variety, and Value. The concept of value can be defined as “huge value but very low density” (Chen et al., 2014, p. 173). Of course, defining the concept is an important thing, but it is also necessary to focus on different ways to use data, extract value, and transform the information available online into big data.
In conclusion, it is possible to note that big data have become a part of the modern society at different levels. When defining the term, people focus on such aspects as the volume, the speed of new data emergence, the variety of data as well as the information’s value. Several decades ago, people only started using computers and the World Wide Web to store and share data. At present, the amount of information is difficult to manage, but the amount of information is increasing each day. However, there are some strategies that can help people address the issues associated with big data.
Reference
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A Survey. Mobile Networks and Applications, 19(2), 171-209.