Introduction
Big data analytics is the process of analyzing large and complex data sources to uncover trends, customer behaviors, and market preferences to help make better business decisions. The complexity of extensive data analysis requires new analytical tools such as predictive analytics, machine learning, streaming analytics, and techniques such as in-database and in-cluster analysis (Terzi, 2015, Kumari et al., 2018). In addition to the sheer volume of data, the complexity of the data being collected creates challenges in data management, integration, and analysis. However, companies that combine unstructured data sources, such as social media content, with existing structured data, such as transactions, can add context and generate new, often more affluent, insights.
In addition, big data describes the increased speed of incoming data coming from proliferating sources such as sensors, mobile devices, web clickstreams, and transactions, leading to the need for real-time analytics. Organizations that can capitalize on what is happening now to prevent equipment failure recommend an item to buy, detect credit card fraud, and quickly become industry leaders. At the heart of analytics is turning data into insightful actions that add value to the organization (Monino, 2021). However, the rise of structured and unstructured data, also known as big data, has radically changed the function of analytics (Hariri et al., 2019). While big data has expanded the opportunities available to businesses, it has created more challenges for collecting, storing, and accessing information.
Big data has many advantages in the context of operational management, but these theoretical considerations have not been adequately tested in the scientific community in practice. For example, a robust big data analytics platform allows users to discover invisible trends and patterns in large and complex datasets that help them quickly identify strategic opportunities and threats. What is more, a unified data architecture provides companies with a rich, consistent, comprehensive data window that improves decision-making and enables users to work with the most accurate and timely information. Moreover, big data analytics improves decision-making performance by enabling everyone in the company to anticipate situations and opportunities, ask relevant and timely questions, and get answers that lead to decisive action. Data science discovery tools and statistical computing take large amounts of historical data and use it to gain new knowledge and find patterns. Finally, machine learning helps to create and train powerful algorithms that can improve business processes and add value to any business.
This revolutionary shift places significant new demands on data storage and poses new challenges for analytics software. This factor also creates powerful opportunities to discover and implement new strategies to develop a competitive advantage. Realizing these capabilities requires two things: the technological capacity to collect and store big data and new tools to turn data into understanding and, ultimately, value (Hamilton & Sodeman, 2020). At the same time, within the framework of operational management, the actual transformation of resources into products, big data analytics is a relatively new and unexplored area in detail. In addition, information about the use of such technologies is often hidden as a trade secret (Acharjee, 2022). Therefore, this research gap needs to be filled first by obtaining primary information through interviews with people from companies with experience with these technologies in the field of operational management.
Methods
Questionnaires and quantitative approaches, including statistics and mathematical analysis, are a priority methodology option utilized in the studies presented in the literature review chapter. This type of research is quite common in many business industries as a tool for collecting data for various purposes (Saris & Gallhofer, 2014). Most of these are in-house surveys looking for relationships between two or three selected variables, with samples ranging from 150 people. Qualitative methods in relation to the understanding, interpretation and interpretation of empirical data at the moment can be used as attempts to build theories on the experience of companies already using such technologies. However, in the current environment, a new experience trend has been established for many companies regarding this phenomenon (Choi et al., 2018, He et al., 2017, Acimovic, J., & Graves, 2015). As a result, to begin with, a quantitative assessment is required, which is chosen as a priority in this study over qualitative and, accordingly, over mixed ones (Mikalef et al., 2019). The implementation of mixed methods requires more data collection methods, which will require more resources, including human and time (Creswell & Creswell, 2017). This study is quantitative in that it relies on numerical and measurable data such as questionnaire scores and financial indicators.
To do this, it is needed to determine the degree of reliability using the t-test tool. Since, for example, in ANOVA analysis of variance, a quantitative continuous data type is needed, discrete data is less desirable (Creswell & Creswell, 2017). This criterion will show how the results obtained with a recognized level of confidence can be extrapolated to larger samples with the same questions (Creswell & Creswell, 2017). The survey was administered in three phases according to the Dillman model, involving a preliminary letter via email, a letter with a questionnaire and a final cover letter, which resulted in the duration of this event taking up to three weeks (Creswell & Creswell, 2017). The reliability of the instrument is achieved by repeating the same results under the same conditions, so it is necessary to exclude the possibility of error at this stage.
In this experiment, a five-point Likert scale will be used. According to research, among various scale divisors from 1 to 5, it has the highest p-value of about 0.11 and, accordingly, validity, which was confirmed by numerous tests through Monte Carlo simulation (Louangrath, 2018). Accordingly, the collection of quantitative data will be carried out by questionnaires of respondents to evaluate statements on hypotheses. The survey tool will be an e-mail distribution, and financial information will be obtained through the analysis of official sources. All of these activities are primarily aimed at obtaining measurable answers to research questions, which is the basis of data collection (Creswell & Clark, 2017). Due to the rather small sample, the p-value will be taken equal to 0.1.
The questionnaire will be a complex system of questions, including the use of various technologies in big data analytics, for example, machine learning, and the degree of success of this implementation. Along with this, the financial performance of the companies in which employees were interviewed will be studied, and the dynamics of the organization’s market value, liquidity, profitability, and asset turnover will be tracked on a timeline. Finding a correlation between employee Likert scores and changes in financial performance will form the basis of this study, which will shed light on the effectiveness and prevalence of several big data analytics technologies and give impetus to future work.
Therefore, the dependent variable will be the financial success of the company, while the independent variables will include the selected big data analytics technologies used and their impact on aspects of operational management such as customer service, HR, marketing, finance, and the company’s direct operations. This differentiation will allow us to explore in more detail the impact and opportunities of big data in business. The data obtained from questionnaires and financial reports will be analyzed using regression analysis. Regression analysis can explain the nature of the correlation between the listed variables or their absence. One way or another, the presence of linear regression will prove the effectiveness of the remote node and the selected style, or vice versa, depending on the direction of the trend line, which can be rising or falling (Brook & Arnold, 2018). A more complex polynomial regression may suggest the nature of the relationship between a company’s financial performance and telecommuting. This type explains more complicated relationships between the selected variables and is not considered in this paper. The nature of this correlation may spur further research in this area, which may suggest new integrated management practices for leaders.
Outcomes
The study output will be tested for statistical significance. After this check, the degree of correlation or its absence between the dependent variables of financial indicators and the independent ones obtained from the questionnaires will be obtained. Differentiated data within questions to employees of companies will be divided into the implementation of big data technologies within their departments or companies as a whole and a subjective assessment of the application’s success in the operational management terminology.
The design of the questionnaire will be based on the conducted literature review. While research in the field of operations management is often narrow and applied in nature, the technologies themselves and the methods of their implementation in business at a higher abstract level are discussed without the context of operations management. Consequently, the data obtained should be biased towards this terminology, and participants in the study will be selected from companies with an emphasis on using this type of leadership. As a result, the work’s scientific value will lie in the conclusions based on practical experience, which will be classified into a visually statistically significant roadmap. The application value will be for companies that will implement this type of analytics and want to be familiar with the various options and their relative success in influencing the company’s financial performance in advance.
Conclusion
This paper provides a draft study describing the chosen methodology based on a review of the literature and the most common techniques in this field. In addition to determining the dependent and independent variables and choosing a way to find a correlation between them, a preliminary plan for a differentiated approach to creating a questionnaire has been outlined. Working with the financial performance of companies that will be divided into several groups involves using quantitative research over mixed and qualitative research, which leads to using a numerical Likert scale for participant questionnaires. As a result, output data will be obtained that are of scientific and applied value in the business field and operational management in the context of using big data analytics technologies.
References
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