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Southern New Hampshire University *
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Information Systems
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Feb 20, 2024
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docx
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Uploaded by fmalik89
Introduction
According to Segal, big data is defined as “large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it
is created and collected, and the variety or scope of the data points being covered.” Such big data
is oftentimes collected through a plethora of methods such as social media, questionnaires, and electronic check-ins. Within most market domains, many organizations have begun to utilize big data as a means to differentiate themselves from their respective competition. Studies performed by Cote-Real et al and Ghasemaghaei & Calic have shown that nearly 90% of companies hold a belief that big data will have an impact on the competitive environment of their business and nearly 90% of businesses all believe that failure to embrace big data will result in loss of substantial market share. As such, many organizations store and analyze big data via specialized software.
Current Research
In the study performed by Hughes-Cromwick and Coronado, the researchers sought to understand the effects of big data on the automotive, energy, and financial service sectors. In their study, they found that business decisions made within the automotive industry are heavily dependent on big data regarding economic activity and forecasts, while the energy industry relies
on big data from inventory, demand statistics, the price of goods, and decisions regarding imports and exports. Financial firms’ big data are slightly more complex, gathering big data from
a variety of sources and, in return, making decisions regarding investments, tracking fraud, and reducing organizational risks. From this big data, those in the financial sector can produce a large
portion of their borrowing and mortgage revenue, allowing firms to remain competitive in low-
interest environments. Researchers in this study also provides several assumptions include the increasing relevance of government big data and the subsequent technical developments made available to individuals, businesses, and within various government levels. They also propose the
use of big data as a means of formulating budget function, which will in return, allow federal agencies to diversify the quality, reach, access, and timeliness of data generation.
Additional current research within big data has studied the potential relationship between big data and overall innovative performance, such as that seen in the study “Big data and predictive analytics in humanitarian supply chains” performed by Dubey et al. and “Big Data Facilitation, Utilization, and Monetization: Exploring the 3Vs in a New Product Development Process” performed by Johnson et al. In these studies, and those similar focusing on innovation within big data, innovation refers to the use of new information to develop and introduce new ideas. Such innovation can be broken down into two main categories:
Innovative effectiveness – the degree to which innovation is advantageous for an organization
Innovation efficiency- the time and effort needed to reach such gains or advancements
This use of big data will allow organizations to track and present how innovation has advanced their organization by methods such as analyzing market information to achieve a better understanding of consumer wants and/or needs, which in return can play a significant role in innovative success.
Future Research
In terms of future research, studies should be conducted on how organizations that use big data can improve overall operational efficiency and revenue growth when compared to competitors, particularly those who do not utilize big data. As stated by Johnson et al., “Despite these potential benefits, however, many companies have not been able to increase their
innovation efficiency using big data and others are also unclear if the processing of big data is positively related to their outputs.” In the earlier referenced study performed by Ghasemaghaei &
Calic, researchers found that nearly 50% of firms have some level of investment into big data use, however, in the same year of the study (2020) the overall use of big data declined by 6.1%. As such, it may be beneficial to also examine how organizations fare after either reducing or eliminating the use of big data. Similar studies can also be conducted in measuring the success of
innovation when using big data; do organizations yield better results when big data is no longer used or result consistent with organizations that use big data.
Works Cited
Côrte-Real, N., Oliveira, T., & Ruivo, P. 2017. “Assessing business value of Big Data Analytics in European firms.” Journal of Business Research, 70, 379–390. Retrieved from https://doi.org/10.1016/j.jbusres.2016.08.011
Dubey, R., Luo, Z., Gunasekaran, A., Akter, S., Hazen, B. T., & Douglas, M. A. 2018. “Big data and predictive analytics in humanitarian supply chains.” The International Journal of Logistics Management, 29(2), 485–512. Retrieved from https://doi.org/10.1108/ijlm-02-
2017-0039
Ghasemaghaei, M., & Calic, G. 2020. “Assessing the impact of big data on firm innovation performance: Big data is not always better data.” Journal of Business Research, 108, 147–162. Retrieved from https://doi.org/10.1016/j.jbusres.2019.09.062
Hughes-Cromwick, E., & Coronado, J. (2019). “The Value of US Government Data to US Business Decisions.”
Johnson, J. S., Friend, S. B., & Lee, H. S. (2017). “Big Data Facilitation, Utilization, and Monetization: Exploring the 3Vs in a New Product Development Process.” Journal of Product Innovation Management, 34(5), 640–658. Retrieved from https://doi.org/10.1111/jpim.12397
Segal, T. 2021. “Big Data.” Retrieved from https://www.investopedia.com/terms/b/big-data.asp
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