The UAE Traffic Department’s Business Intelligence Report (Assessment)

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As the matter of fact, due to numerous advantages, business intelligence is a commonly used approach nowadays. The notion describes the methods and instruments by analyzing the information. The obtained data are used for the business analysis (Sabherwal & Becerra-Fernandez, 2011). The technologies of the business intelligence structure the information to find optimal strategies for the future development of the business. The primary objective of the business intelligence is to focus on the key factors and developing the models of behaviour and possible consequences and results (Loshin, 2013). The implementation of the business intelligence into the work of the Traffic Department of the United Arab Emirates seems to be essential; however, certain risks and benefits of the system should be taken into consideration. The primary purpose of the paper is to evaluate the effectiveness of the business intelligence for the Traffic Department of the United Arab Emirates.

Research Problem

The Traffic Department has already faced a significant problem. Because of the development of the department, it experiences the impressive flow of the information. The implementation of the business intelligence seems to be essential for managing the information flood and creation of strategies to improve the effectiveness and productivity of the work.

The research question is whether the implementation of the business intelligence into the working process will beneficially influence the work of the department and will improve it.

Hypothesis

According to the stated above research question, the following hypothesis should be taken into consideration, namely, the implementation of the business intelligence will sufficiently improve the work of the Traffic Department and will positively affect the managing of the information.

Research Plan, Justification, and Literature

To get better involved in the issue, the qualitative research method seems to be relevant. The qualitative approach while the investigation of the effectiveness of the business intelligence is commonly undervalued (Taylor, Bogdan, & DeVault, 2015). It should be highlighted that the qualitative analysis is based on finding contextual meaning, reasons for human behaviour, and feelings. These factors are essential to be considered while implementing the new system and for the business development as well. Every business has the qualitative side that will be obscured by the quantitative approach. Numeric data are not enough for the research (Bryman & Bell, 2011).

The tools that should be selected for testing the hypothesis are interviews, questionnaires, and observation. The stated above tools are relevant for implementation as they will show the results, feelings, and insights of the employees regarding the business intelligence (Corbin & Strauss, 2014). The success of the systems depends on the people who follow it. It is worth highlighting that even the best system may fail in the case it does not suit the employees or cannot be understood by the workers. Interaction with the employees will contribute to the research (Hair, 2011).

To obtain realistic results, people of different age, experience, and working position should be chosen. The number of people chosen for the observation and interview should exceed ten. The non-probability systematic sample will provide a better perception of the issue. The date should be interpreted and analyzed by measures of central tendency, content analysis, and comparison of means.

The implementation of the business intelligence contributes to the improvement of the operational performance, service, and identification of possible ways of development. The above stated benefits can be evaluated by the observation and interviewing of the worker of different age and professional background. Understanding whether the system is profitable is impossible without the interactions with the employees. However, for the best results, numerous workers should be observed and interviewed as the business intelligence can be perceived differently. Non-probability systematic approach will help to obtain real data and draw appropriate consequences (Xia & Gong, 2014).

Conclusion

In conclusion, it should be pointed out that the implementation of the business intelligence into the working process of the Traffic Department in the United Arab Emirates will beneficially affect the effectiveness of the working process, will facilitate it, and will consequently lead to the advance in the sphere of dealing with the information, and developing new strategies to succeed. As the matter of fact, certain difficulties cannot be eliminated; however, every change is followed by challenges. The most demanding task will be the adjustment of the workers to the newly implemented system. Nevertheless, after clarifications and instructions, the employees will get the general understanding of the model. It is worth stating that practice is the integral part that will contribute to the successful implementation of the approach. The chosen qualitative approach will contribute to the obtaining of the full picture of the problem and will show the drawbacks of the implemented system.

References

Bryman, A., & Bell, E. (2011). Business research methods. Oxford, U. K.: Oxford University Press.

Corbin, J., & Strauss, A. (2014). Basics of qualitative research techniques and procedures for developing grounded theory. Los Angeles, CA: SAGE.

Hair, J. (2011). Essentials of business research methods (2nd ed.). New York, NY: Routledge.

Loshin, D. (2013). Business intelligence: The savvy manager’s guide. Waltham, MA: Morgan Kaufmann.

Sabherwal, R., & Becerra-Fernandez, I. (2011). Business intelligence: Practices, technologies, and management. Hoboken, NJ: John Wiley & Sons.

Taylor, S., Bogdan, R., & DeVault, M. (2015). Introduction to qualitative research methods: A guidebook and resource. Hoboken, NJ: John Wiley & Sons.

Xia, B., & Gong, P. (2014). Review of business intelligence through data analysis. Benchmarking: An International Journal Benchmarking, 21(2), 300-311.

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