Application of Big Data in Business
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Application of big data in business decision making
Big data is the set of structured, semi-structured, and unstructured information which are collected by a person or organization, that can be mined and used for the decision-making process. In simple words, big data is a huge and more complex data set collected from new data sources. The large volume of the data cannot be managed by traditional systems or software, but if we analyze it and manage it, it can be hugely beneficial for the business. While it is an important aspect of decision-making, Oracle (2021) said that big data are the business capital.
As data helps businesses thrive due to innovation in the decision-making process, it is an economic factor of production in digital goods and services as well. For example, due to the lack of financial capital, a company cannot lunch a new model of vehicle in the market. Similarly, without the big data to feed the onboard algorithms, the automakers cannot make the car autonomous. Therefore, the role of data management has impacted the overall strategy of the business as well as the future of computing.
Case study of China Eastern Airlines Application of big data
The application of big data helped businesses grow in various ways. While businesses analyzed big data, the outcome helps businesses to predict the future and perceive the solution. According to Oracle (2022), big data can be used in product development, predictive maintenance, customer experience, fraud and compliances, machine learning, operational efficiency, and driving innovation. This paper will discuss the applicability of big data in the later chapter.
Application in production Application in predictive maintenance
Analysis of big data helps businesses to predict future maintenance. For example, it can be beneficial for the aircraft industry. With the help of monitoring the big data collection, it is possible to get the information for aircraft on when the parts are needed to be replaced. It increased reliability along with bolstered operational and supply chain efficiencies. These days, most industrial and electronic machines are equipped with a sensor, that can see hear and feel ever than before. It gives a huge number of data that can be analyzed, and with the help of algorithms, it can be operated in efficient ways. Daily, & Peterson (2017) believe that to know the significance of predictive maintenance and get the impactful result, machines, data, insight, and people need to be brought together.
Application in fraud and compliances Summary
Uses of big data becomes critical for all industries. It helps businesses to stand out in the competitive environment. Nowadays, almost all businesses including small to big organizations need the data and insight from it. While an organization is looking for its potential customers and their preference, big data plays a significant role. Therefore, the companies have been using big data to make data-driven strategies that help them to compete with the competitors. This paper discussed the case studies on China Eastern Airlines (CEA) used the Oracle big data that helped them to know their services, know the relationship of passengers and aircraft members, and even know when to replace the parts of the aircraft. Besides, the paper explained how we can use big data for production, predictive maintenance, and prevent fraud and compliances. The paper summarized that big data plays a significant role for almost all the industries, but for the airlines, the production and financial sector has their crucial roles.
References
Cardenas, A. A., Manadhata, P. K., & Rajan, S. P. (2013). Big data analytics for security. IEEE Security & Privacy, 11(6), 74–76.
Choi, T. M. & Lambert, J. H. (2017). Advances in risk analysis with big data. Risk Analysis, 37 (8), pp. 1435–1442. https://doi.org/10.1111/risa.12859
China Eastern Airlines (2022, May). Introducing CEAhttps://www.ceair.com/global/en_static/AboutChinaEasternAirlines/intoEasternAirlines/chinaeasternInto/index.html
Daily, J., Peterson, J. (2017). Predictive Maintenance: How Big Data Analysis Can Improve Maintenance. In: Richter, K., Walther, J. (eds) Supply Chain Integration Challenges in Commercial Aerospace. Springer, Cham. https://doi.org/10.1007/978-3-319-46155-7_18
Fang, B., & Zhang, P. (2016). Big data in finance. In Big data concepts, theories, and applications (pp. 391–412). Springer, Cham. https://doi.org/10.1007/978-3-319-27763-9_11
Labrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12), 2032–2033.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2016). Big data: The next frontier for innovation, competition, and productivity. McKinsey & Company. https://www.automation.com/en-us/articles/2016-1/applications-of-big-data-in-manufacturing
Oracle. (2021, June). Data Management, defined. https://www.oracle.com/database/what-is-data-management/
Oracle (2022, May). What is Big Data? https://www.oracle.com/ca-en/big-data/what-is-big-data/
Oracle (2022, May). China Eastern Airlines adopts Oracle Big Data to enhance flight safety. https://www.oracle.com/customers/cea-1-big-data/
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