This topic was selected as there is a need to learn the various ways one can analyze digital information and use it. It is significant to note that there has been an increase in cases of data mining over the past few years and using this information to form new lines of thought about various topics (Ahmed et al., 2017). Most companies are using information collected from potential clientele to understand how the market responds to certain triggers to increase rates of consumption. This occurrence indicates that dealing with large data sets and making sense of them is essential as it can help influence the market. Significantly, learning how to use big data is crucial as it saves one the long process of traditional research (Pauleen & Wang, 2017). By having data on their side, organizations are able to identify gaps, which in turn become opportunities. Big data analytics guides the operations of companies in a bid by the organizations to avoid being irrelevant.
As Susan Etlinger says in the video, big data analytics's main idea is to learn what people think. For instance, if one analyzed Twitter conversations, it is easy to see the various views people have about topics such as smoking. In that sense, to start a campaign against smoking, big data may help to, first of all, understand which substance most people smoke (Etlinger, 2014). Additionally, it is convenient to learn why they think smoking the substance is helpful to them. According to Etlinger, getting information from platforms such as Twitter is not enough. One has to learn the different languages that different people use in conversations. It is also crucial to understand the context in which phrases are used. For example, the word smoking may mean different things to different people (Etlinger, 2014). This understanding points out that data is not useful unless one can interpret it. In essence, such data requires that the one analyzing it tries to view the topic through the user's eyes.
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It is significant first to create clear hypotheses and establish methodologies for analyzing and using big data. These steps are essential since there is an overwhelming amount of information. A hypothesis helps guide one on what they want to achieve with data (Etlinger, 2014). For example, if one wants to start a lingerie business, there is the initial assumption that most modern women use particular products. The task now becomes to research what sizes would sell fast and how much the users willing to reveal to learn their taste. Methodologies include how one is going to acquire this data from the online spaces available. Essentially, big data is subject to misuse, and therefore there is a need to protect this information from malicious use. The conversation in the coming years will therefore be about privacy and protection of delicate information.
The speaker starts by provoking the viewer's thoughts by quoting books like "amusing ourselves to death." this opening remark provides an interesting way to view the use of technology. One is a force to think whether the use of technology and its evolution has been crucial in improving our lives, or it has been the cause of a slow but pleasurable death (Etlinger, 2014). The speaker also talks about her son, who was diagnosed with autism disorder. The boy was once caught trying to learn by searching various words on the computer. This example shows that it is equally possible to collect information by just searching one word.
In conclusion, big data is useful in many ways, and one of them is collecting information about trends. Understanding what people think about different topics guides one on how to fill certain gaps in businesses. However, big data must be protected from misuse by malicious individuals.
References
Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. (2017). The role of big data analytics in Internet of Things. Computer Networks , 129 , 459-471.
Etlinger, Susan. (2014). “What do we do with all this big data?” TED. YouTube.
Pauleen, D. J., & Wang, W. Y. (2017). Does big data mean big knowledge? KM perspectives on big data and analytics. Journal of Knowledge Management .
