Essay Sample: Big Data Usage towards Efficient Operations

Introduction

Big data is explained as a large and complex dataset for ordinary computers to process. Usually, the sets of information are diverse and gradually grow. Scientists at the IBM data classify big data into four dimensions. That is veracity, volume, velocity, and variety. The essay below explains the varied ways in which big data has helped improve the efficiency of operations. The essay also explains possible ways in which privacy risks when using big data can be minimized. The company identified and contrasted is Netflix, a company providing entertainment streaming services.

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Big data usage by Netflix has helped improve operations' efficiency by identifying the causes of issues and failures in the operation time. That, in turn, helps in the development of the management scopes to effectively manage the problems. In Netflix, big data has also influenced the accurate generation of offers based on customer purchases and subscription trends (Ren et al. 1352). That has helped in customer attraction and retention, in turn, boosting profits and promoting efficiency in operations. Customer loyalty and engagement have also been boosted by the use of big data on Netflix. That is due to proper methods of addressing customer needs.

In Netflix, big data usage has significantly helped to understand data-initiated marketing potential. That has helped improve its operations' efficiency due to the establishment and implementation of efficient marketing methods. Big data has also helped the company personalize customer experience accessing its content. That has resulted in boosted income and efficiency in operations (Wamba et al. 361). Big data has influenced the addition of value to both online and offline client interactions. That is initiated by customization of the website to accommodate both situations.

The privacy risk misinterpretation of personal data when using big data can be managed by involving the owner to rectify any errors made when feeding data. That will ensure that information about and individual is always accurate and will not be exposed to threats such as failure in credit card acquisition. Another possible privacy risk due to big data usage is the loss of clientele due to mistrust (Wang et al. 101). People will avoid indulging in companies; they are not assured of their information safe. That can be addressed by the company being compliant with acts protecting customer personal information.

Minimizing privacy risks when using big data can be achieved by discarding unwanted data. Holding onto stockpiles of data by companies increases the privacy risk. Companies, therefore, should filter and acquire the necessary data and jettison the rest. Privacy risks can also be minimized by strengthening back-door security systems (Ren et al. 1352). That is, the locations data is saved but not part of the active system such as disaster recovery engines. Utilization of business acumen significantly minimizes the privacy risks involved in big data usage. That entails the managers addressing data security sensitively, just like other issues in the organization.

Conclusion

Netflix's big data usage has significantly influenced the operations' efficiency due to identifying the causes of issues and failures in the operation time. Big data has also influenced the accurate generation of offers based on customer purchases and subscription trends. Customer loyalty and engagement have also been boosted by the use of big data on Netflix. Big data usage has helped to understand data-initiated marketing potential. It has also helped the company personalize the customer experience and addition of value to online and offline clients. Minimizing privacy risks when using big data can be achieved by discarding unwanted data, strengthening back-door security systems, and Utilizing business acumen.

Works Cited

Ren, Shan, et al. "A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: a framework, challenges and future research directions." Journal of cleaner production 210 (2019): 1343-1365. https://www.sciencedirect.com/science/article/pii/S0959652618334255

Wamba, Samuel Fosso, et al. "Big data analytics and firm performance: Effects of dynamic capabilities." Journal of Business Research 70 (2017): 356-365. https://www.sciencedirect.com/science/article/pii/S0148296316304969

Wang, Gang, et al. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications." International Journal of Production Economics 176 (2016): 98-110. https://www.sciencedirect.com/science/article/pii/S0925527316300056