WHAT IS BIG DATA?
Collecting and analyzing data from the internet is a significant feature of big companies around the world. This kind of process is necessary for these companies to determine the current trends of the society and their relevant needs. From analyzing the collected data, companies can create predictions of the current market and user behavior patterns. As companies continue to adapt to the latest trends from the market, the data collecting process can be considered as a regular occurrence for these companies.
In the data management field, one of the most commonly used terms is big data. By definition, big data is a field of activity where people analyze and extract information from some complex data sets that are too complicated to be done with traditional data-processing software. The complexity of the data sets can lead to a higher possibility of false discovery rates (FDR) and statistical power. Apart from their complexity, companies will seek out the most usable information as soon as these data sets are assessed.
When discussing big data, four basic concepts illustrate the aspects of big data:
- Volume (the quantity of generated and stored data, where the size of the data determines the value and potential insight)
- Velocity (the type and nature of the data, which helps people to use the insight result from the analysis process)
- Variety (the generation speed of the data’s procession to meet the analysis’ goals)
- Veracity (the extended definition of big data, which refers to the data quality and the data value that are vital to the accurate analysis process)
Due to its characteristics, big data is an essential aspect of many companies’ decision-making process. It is a commonly agreed preposition within the business world that a set of data is meaningless if not used properly, regardless of how many or accurate that data set is. Therefore, it is not entirely inaccurate that the most successful companies are those that managed to beat their competitors in searching for the most usable data. Furthermore, data secrecy is also equally important in this context, and companies will do anything within their extent to maintain their formula of success.
In practice, data sets within the big data field can be varied as well. They can range from Google search indexes to Amazon’s products price list and many others. Since these two companies are some of the most notable companies in the world, it is only natural to use a massive database to store the abundance of their users’ data. The stored data, in turn, can help Google in maximizing the search result process and Apple in managing the most viable price margin for its customers. In addition, this kind of benefit only scrapes the overall benefit that the big data field offers to these companies’ life span.
However, many have realized the dark side of the big data as well over the past few years. While it is true that big data offers more accurate market prediction and business strategy more than ever, a new challenge has arisen as well. Unintended results may also follow the results that come from the analysis process of big data. For instance, many fears that unsecured database of collected data sets will cause a breach of privacy and security for its holder. Millions of user profiles worth of billions of dollars can be misused just to earn some extra pocket money for the perpetrators.
The breach of the users’ profile security can also be detrimental to society and the government. Trust to the government and database providers can plummet itself in the event of major data security breach. Facebook-Cambridge Analytica data scandal in 2018 was a watershed moment for many people since it revealed that everyone is just as vulnerable to the next person. The fact that users’ profile data were used en masse for political purposes without their consent has led to a surge of distrust of the people to the current data security. This, in turn, resulted in a higher conscience for data security and algorithmic fairness more than ever before.
In the end, the key challenge for big data users is to ensure both maximum profitability and data security to prevent future losses. While it is admirable for companies to earn more income through big data analysis, they also have to deal with the ethical aspects of big data. Since data security is very much a sensitive topic, people’s trust to these companies will suffer a downfall if their data is not secure. Moreover, in the light of recent events, it is only imperative for companies to uphold their users’ data security more than ever before.
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