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Big data is one of the most demanded technologies, being actively implemented in all spheres of human activity. The volume of information processed by the user is growing every year. In addition, the development of machine learning and the widespread use of smart devices contribute to this, which reinforces predictions of big data (Marr, 2016). However, besides the clear advantages, this technology has many weaknesses. The purpose of this essay is to analyze big data as technology concerning JPMorgan Chase and consider how it can be used shortly by this company.
Four main factors are making the basis of big data technologies. According to statistics, JPMorgan Chase has access to more than 150 petabytes of data, and 30 thousand databases and has over 3.5 billion users ( How JPMorgan uses Hadoop, 2021). However, in the context of the volume factor, the lack of usefulness of the collected information can become a problem. Accordingly, JPMorgan needs to improve its data quality, as poor-quality information can do more harm than good (Powell, 2021). One approach to improving quality can be measured to analyze data heterogeneity within the variety factor (Reis et al., 2016). To provide better service to customers, a company must consider its variety, given the availability of data of different priorities. Setting clear priorities based on customer preferences will increase the satisfaction of existing customers and attract new ones.
One of the most critical issues of large amounts of data is its reliability, reflected in the veracity factor. This issue is even more relevant in banking operations, which should be as transparent as possible. Therefore, JPMorgan needs to create a secure environment in which the likelihood of data leaks and tampering goes to zero. This will allow the conglomerate to remain competitive and attractive to the user, demonstrating high-reliability standards. Finally, processing speed can be a significant obstacle when working with large amounts of data. Slow interaction with information and services usually hinders working with banking systems, making many clients change their minds about cooperation. On the other hand, excessively high speed increases the likelihood of various errors occurring, from malfunctions to the compilation of incorrect predictions. In this context, the company needs to prioritize developing and using high-quality equipment that balances the extremes.
The designated areas have many obstacles that can complicate decision-making. However, they can be eliminated by using additional developments related to big data and actively interacting with the technology sphere. The support of various standardizing organizations allows companies to create a more effective environment for developing new technological solutions ( Leading through innovation, 2019). It is necessary to use unique analytical algorithms that connect many statistical systems, such as Fastbase, to filter unnecessary information (ACN Newswire, 2021). Another way to represent large amounts of data more effectively is through various visualization tools (Reis et al., 2016). This allows visually demonstrating and exploring different types of information, helping to rule out excessive variety problems.
To solve the problems of information reliability, it is necessary to improve cybersecurity measures and try to use methods that are inherently protected. Decentralized blockchain-based systems are distinguished by their transparency and security from external influences, which coincides with the need to filter out data. Finally, various neural network-based machine learning technologies have much higher performance than conventional computers (Reis et al., 2016). In addition, they allow increasing operating time to improve the efficiency and reliability of operations while reducing the number of errors.
Thus, big data technologies are quite controversial, but widespread solutions which make it possible to simplify the processing of many operations. Massive structures like JPMorgan can use them to accumulate vast amounts of data, which can then be used to analyze customers in more detail and change services according to their preferences. With the use of additional technologies, such as neural networks and blockchain, there is an opportunity to increase the efficiency of existing systems significantly, facilitate decision-making and bypass big data obstacles.
References
ACN Newswire (2021). Fastbase big data revolutionizing sales and marketing for small businesses. Benzinga. Web.
How JPMorgan uses Hadoop to leverage Big Data Analytics? (2021). ProjectPro. Web.
Leading through innovation: The data opportunity. (2019). J.P. Morgan. Web.
Marr, B. (2016). 17 predictions about the future of big data everyone should read. Forbes. Web.
Powell, A. (2021). 2 early vaccination surveys worse than worthless thanks to big data paradox, analysts say. The Harvard Gazette. Web.
Reis, M.S., Braatz, R.D, & Chiang, L.H. (2016). Challenges and future directions. Chemical Engineering Progress, 112(3), 46-50
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