Introduction
Management and organizational behavior require data analysis, as otherwise, it would be impossible to assess the situation and identify the appropriate directions of performance. In particular, analyzing big data at each stage of hiring process as well as prior to it and following it enables human resource managers to optimize the search (Graham, 2021). The optimization includes a better understanding of growth opportunities, more reasonable decisions, revealing skill gaps in employees, for which it is necessary to compensate, and other. This means the use of predictive models, the software for sorting and storing CVs, and artificial intelligence, which also may be helpful in assessing the achievements of the staff members, allocating tasks, and planning.
Other areas of the discipline utilize data analysis as well, for instance, in large-scope research that presupposes collecting and processing big amounts of information, frequently within tight deadlines. Thus, Gusti et al. (2021) mention bibliometric analytical approaches as the methodology of their investigation on the trends in organizational citizenship behavior for the environment. This is a quantitative approach to quality assessment; simply stated, it relies on numerical metrics, such as citation rates, to analyze the content of academic sources and interconnections between them. This allows for identifying the trends without assessing each work separately.
BUS530 and Data Analysis
The subject of managerial economics is resource allocation, which it aims to do in a way that allows for a maximally possible improvement of the efficiency of a business or the entire industry. This, in turn, presupposes never-ending data analysis, as it is critical to be aware of the current situation at every moment to respond to probable changes quickly and appropriately. The need for awareness, in turn, determines that for sharing and spreading information, which may compromise privacy and, therefore, bears certain risks. Among the most serious threats is personal data leakage; such occasions are real in the world where gadgets can identify the locations, typical activities, and even health statuses of their owners (Carrière-Swallow & Haksar, 2021). The experts highlight that the development of artificial intelligence has simplified analyzing big arrays of information considerably but, along with that, caused non-negligible privacy issues.
Therefore, a recent strategic trend in managerial economics is investing additional effort in digital security. Adding to the credibility of organizations and institutions, such an approach attracts positive public attention to those and, subsequently, helps build relationships of trust between businesses and the population (Carrière-Swallow & Haksar, 2021). This enables balancing commercial interest and customer privacy; hence better loyalty, which increases the productivity of the company.
ACC501 and Data Analysis
The role of accountants in making business decisions is undergoing considerable transformations due to the development of digital technologies that introduce new approaches to managing financial data. Progress, in fact, provides the representatives of this profession with outstanding career prospects, giving them a chance to improve the quality of their work considerably (Gould, 2021). Specifically, accountants can apply their expertise to data analysis in several areas and ways, enhancing their competences and, consequently, maintaining their relevance as specialists.
One of the new functions that an accountant can fulfil is data engineering. This means monitoring the integrity and reliability of information, to which the tools that accounting provides are applicable due to their measurement capacities (Gould, 2021). Data controlling is another possible area; briefly, it lies in shifting the focus of stewardship from financial resources to informational. The interpretation of the outcomes, which provides an analytical perspective and, therefore, is critical for making decisions, also can be a responsibility of an accountant (ibid). Finally, he or she can make assumptions and build models based on the data, which enables investigating complex business issues in particular contexts. Accounting skills, therefore, may be necessary at each stage of data analysis in combination with other knowledge, such as engineering.
FIN501 and Data Analysis
It is necessary to highlight the irrationality of regarding data science exclusively as a more technically advanced equivalent of statistics. In fact, this sphere of knowledge aims at obtaining the information that not necessarily is apparent at first sight from large and frequently unstructured collections (“How data science is transforming,” 2021). In business, it can simplify finding reasonable solutions to complex issues. In addition, data analysis enables predicting customer behavior by collecting records of their activity and revealing tendencies in it. On the condition of appropriate use, such information can add substantially to the competitiveness of a business by improving its flexibility.
Considering the above, the popular approaches in strategic corporate finance are real-time and predictive analyses. The former, as apparent from its name, focuses on the current financial status of an organization. The latter, meanwhile, utilizes the evidence on its performance in the past to anticipate future trends and design relevant development strategies (“How data science is transforming,” 2021). Consumer behavior is an essential criterion in both cases; it is possible to assume from specially designed profiles, whose creation presupposes collecting personal data of users.
MKT501 and Data Analysis
Strategic marketing is among the spheres where data analysis gains special importance since it helps maintain the awareness of customers’ needs as well as behavior and adjust business performance accordingly. Gathering and processing large amounts of information on a constant basis, however, may be technically difficult, which is the reason for the popularity of AI in the field. Notably, it can improve the quality of the collected data, that is, its topicality, actuality, ethicalness, and variety (Taylor, 2021). This, in turn, allows for targeting and individualized approaches to advertising; hence, additional opportunities for customer attraction as well as retention.
The above makes it reasonable to implement artificial intelligence to marketing, which actually is among the widespread trends in the area. It monitors consumer behavior, identifying the changes in demand, which may be outstandingly rapid (Taylor, 2021). Never-ending control, meanwhile, improves the speed of the producer’s response, consequently enabling it to outperform the competitors. However, this is not the only possible use of AI in marketing; as mentioned in the previous paragraph, it also can serve to ensure the absence of ethical concerns associated with collecting the data. Specifically, information should be gathered in accordance with the law and stored with sufficient protection, so that no third party accesses it (ibid.). Privacy policies, therefore, should be integral to marketing strategies, and AI may be useful in developing them.
Overall Data Analysis Strategy
The above evidence shows that applying artificial intelligence to gathering and analyzing information, especially in big amounts, is a stable trend in data science. Its main advantage is the improved relevance of both the collected material and the outcomes. These parameters are critical for the firms that seek to align their production with consumer needs, motivations, and preferences. Specifically, AI allows for personalization of services, relying on the available information about each particular customer (Loureiro et al., 2021). It is possible to state that collecting and processing data are becoming the priorities of modern businesses. Even financial management presumably is less meaningful at the present day, considering the suggestion to accountants to master data science (Gould, 2021). In other words, awareness is the key to success in the digital epoch.
Along with the benefits for producers, AI bears risks for consumers that are associated with violating their right for privacy. The point is that designing individual approaches presuppose gathering a maximum of personal information, which frequently happens without knowledge. Thus, Carrière-Swallow & Haksar (2021) highlight that any software, which a user installs on his or her smartphone, may collect and store such sensitive data as precise location, places of attendance, or health issues. As a result, this information becomes obtainable, which puts the person at the threat of stalking, burglary, and other types of crime. According to Munoko et al. (2020), AI will find even more application in the future; this will aggravate the problem. Therefore, it is essential to develop business strategies with a focus on customer privacy.
The latter actually is a recent trend that apparently will continue to spread. The level of digital security, in fact, is among the factors of credibility, on which consumers rely when choosing producers (Carrière-Swallow & Haksar, 2021). However, complete anonymity in the Internet would not be reasonable, as personal data can be useful not solely for marketers and frauds. For instance, human resource managers utilize them in search for talents, which improves everybody’s chances for successful employment (Graham, 2021). The most appropriate solution, therefore, is to balance personal, corporate, and commercial interests; ideally, users should have a possibility to control and change the scope of data that are accessible for third persons.
Conclusion
Data analysis is critical in business, as it provides awareness of customer needs, which, in turn, is the main driver of production decisions. The paper examines the ways in which it happens in various areas, identifies the current trends, and describes the resulting benefits as well as risks.
References
Carrière-Swallow, Y., & Haksar, V. (2021). Let’s build a better data economy. International Monetary Fund.
Gould, S. (2021). Data and the future-fit accountant. International Federation of Accountants (IFAC).
Graham, J. T. (2021). 10 benefits of using big data in human resources. Sage HR.
Gusti, M. A., Yasri, Ya., & Idris, I. (2021). Knowing trends in organizational citizenship behavior for the environment (OCBE) using bibliometric analysis based on the Scopus database. Academy of Strategic Management Journal, 20(6), 1-9.
How data science is transforming the financial services industry. (2021). The World Financial Review.
Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, L. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129, 911-926.
Munoko, I., Brown‑Liburd, H. L., Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167, 209–234.
Taylor, K. (2021). How AI-driven analytics help marketers manage and understand mass amounts of data. Dataconomy.
