BM9718 / LD9718 / AT9718 Research Methods and Analytics for Business Practice Assignment Sample

1.0 Introduction

Business analytics Of Royal Dutch Shell is the process which is used in the process of statistical technology and the part of the history of data. The term business analytics is the solution of the data management and the part of the business intelligence which is also used in the area of the data mining, aggregation part of the data, part of forecasting is also included with the business analysis. In the part of the business analysis the part of data aggregation is used for the analytical part and the organizational part as well. On the other side, in the area of the business analysis the forecasting part is going to be included with the historical part of data which is also going to inform the estimate of the determining behavior of the business analysis part.

1.1 Background

Business analysis was invented in the year of the 196th century by Frederick Winslow Taylor. The business analysis is the kind of solution which is used in the area of the data mining procedure. The term business analysis is going to refer to various kinds of skills, technologies which are used in the performance area of the business (Pröllochs et al. 2020). Usually, a business analyst is going to be focused on the developing sight of the business performances which are based on the statistical process. The term business analytics is focused on the prediction and the prescription part. The part of the business analysis has the ability to to make the extensive which is going to use in the part of the analytical modeling part and the analytical part of the numeric. The analytical part of the business is also monitored by the human being.

2.0 Literature Review

2.1 Empirical study

According to the author suryanarayanan krishnamoorthi (2017) the part of the business analysis is the growing part for the business. It is very important to enhance the value of the business which is created for the investment. The term business analysis has already invested in the area of value creation. To find out the required elements the thesis analytical part can be used. To analyze the business the most important part is enhancing the business. Self awareness is the most important area of business analytics. With the help of self awareness Royal Dutch Shell people are able to know about their strengths and weaknesses. While all the people are going to know  about their strengths then business will be automatically enhanced. Self awareness has been divided into two areas. Most of the area is the external area and the internal area. With the help of the external area people get to know about their strengths, weaknesses, passion and the value. On the other side, the inner self is included with the feelings and the thoughts of the people. In the area of business analysis, interpersonal skill is really very important, with the help of the interpersonal skill people get to know about their feelings. While the people get to know about their feelings, it will be very easy to do the analytical part of the business (Shi et al. 2018). With the help of communication people are able to express their feelings and thoughts. In the area of the business practices the intrapersonal skill is oriented with the productivity, elasticity, and the resource area. In the area of the business productivity is also going to help to the Royal Dutch Shell to enhance the business. While the part of the productivity is going to be very effective then business analyst will be more proper. In the business analysis all the products need to be more resourceful which are going to optimize and help all employees to generate more new ideas? The matter of empathy is one of the most responsible parts in the area of the business. To enhance the part of the business the employees need to be leadership skills and are also given the direction to build up the most effective part of the business. In the stage of the business practices the organizational decision making is the method which is oriented by more units of the organization. The area of organizational decision making is oriented by the manager of the organization.(BM9718 / LD9718 / AT9718 Research Methods and Analytics for Business Practice Assignment Sample)

2.2 Research Gaps

In the stage of the research gap to enhance the business analytics the area of the data needs to be enhanced. In The area of the business enterprises the secrecy of the data is the most critical matter which is obtained by some inappropriate team. So, to cover up the gap the inappropriateness should be rectified (Silva et al. 2021). In the area of the business all the important data needs to be managed on a regular basis. The volume and the quality of the data is the most effective part for the company. The area of the technical shape also needs to be managed. Because of the shape of the technical data is needed to increase the volume of the company.

3.0 Methodology

3.1 Research Method

In the area of the research method the entire project is done by qualitative research. The term qualitative research method is involved with the gathering part of data which is of course non numerical (Shmueli et al. 2017). All the non-numerical data is going to be under qualitative research. Throughout the project about the business analysis all the required information is gathered by only research. To analyze the entire data it is going to say that the entire matter of business analytics needs to be grown with the help of the employee’s enhancement, their power of knowledge, capacity to understand their empathy, all the things that come under the research method.

4.0 Conclusion

In the stage of the conclusion it is going to conclude that, the matter of the business analytics is able to reduce the risk factor of the business. With the help of the business analytics all the right decisions have been taken which are going to be effective for the business. All the preferences such as customer’s preferences, ongoing trends and all things are going to be very helpful for the area of the business and their short and long term goal as well.  Analyzing the data in the area of the business is not sufficient at all; the analytical part of the data is going to identify the new opportunity of the businessman able to look at the segmentation of the customers. With the help of the business analysis Royal Dutch Shell business has been able to increase the growth and their area of intelligence. To enhance the party of business analysis all the required things are attached.

Reference List

Journal

Ashrafi, A., Ravasan, A.Z., Trkman, P. and Afshari, S., 2019. The role of business analytics capabilities in bolstering firms’ agility and performance. International Journal of Information Management, 47, pp.1-15.

Aydiner, A.S., Tatoglu, E., Bayraktar, E., Zaim, S. and Delen, D., 2019. Business analytics and firm performance: The mediating role of business process performance. Journal of business research, 96, pp.228-237.

Delen, D. and Ram, S., 2018. Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), pp.2-12.

Conboy, K., Mikalef, P., Dennehy, D. and Krogstie, J., 2020. Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda. European Journal of Operational Research, 281(3), pp.656-672.

Gunasekaran, A., Yusuf, Y.Y., Adeleye, E.O. and Papadopoulos, T., 2018. Agile manufacturing practices: the role of big data and business analytics with multiple case studies. International Journal of Production Research, 56(1-2), pp.385-397.

Ajah, I.A. and Nweke, H.F., 2019. Big data and business analytics: Trends, platforms, success factors and applications. Big Data and Cognitive Computing, 3(2), p.32.

Appelbaum, D., Kogan, A., Vasarhelyi, M. and Yan, Z., 2017. Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, pp.29-44.

Smys, S. and Joe, C.V., 2019. Big data business analytics as a strategic asset for health care industry. Journal of ISMAC, 1(02), pp.92-100.

Vidgen, R., Shaw, S. and Grant, D.B., 2017. Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), pp.626-639.

Pröllochs, N. and Feuerriegel, S., 2020. Business analytics for strategic management: Identifying and assessing corporate challenges via topic modeling. Information & Management, 57(1), p.103070.

Shi, Z. and Wang, G., 2018. Integration of big-data ERP and business analytics (BA). The Journal of High Technology Management Research, 29(2), pp.141-150.

Silva, A.J., Cortez, P., Pereira, C. and Pilastri, A., 2021. Business analytics in industry 4.0: a systematic review. Expert Systems, 38(7), p.e12741.

Shmueli, G., Bruce, P.C., Yahav, I., Patel, N.R. and Lichtendahl Jr, K.C., 2017. Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.

Liu, Y., Han, H. and DeBello, J., 2018. The challenges of business analytics: successes and failures.

Seddon, P.B., Constantinidis, D., Tamm, T. and Dod, H., 2017. How does business analytics contribute to business value?. Information Systems Journal, 27(3), pp.237-269.

Duan, Y., Cao, G. and Edwards, J.S., 2020. Understanding the impact of business analytics on innovation. European Journal of Operational Research, 281(3), pp.673-686.

Ramanathan, R., Philpott, E., Duan, Y. and Cao, G., 2017. Adoption of business analytics and impact on performance: a qualitative study in retail. Production Planning & Control, 28(11-12), pp.985-998.

Chehbi-Gamoura, S., Derrouiche, R., Malhotra, M. and Koruca, H.I., 2018, June. Adaptive management approach for more availability of big data business analytics. In Proceedings of the Fourth International Conference on Engineering & MIS 2018 (pp. 1-8).

Halibas, A.S., Shaffi, A.S. and Mohamed, M.A.K.V., 2018, March. Application of text classification and clustering of Twitter data for business analytics. In 2018 Majan international conference (MIC) (pp. 1-7). IEEE.

Sun, Z., Strang, K. and Firmin, S., 2017. Business analytics-based enterprise information systems. Journal of Computer Information Systems, 57(2), pp.169-178.

Nalchigar, S. and Yu, E., 2017, July. Conceptual modeling for business analytics: a framework and potential benefits. In 2017 IEEE 19th Conference on Business Informatics (CBI) (Vol. 1, pp. 369-378). IEEE.

Assignment Services Unique Submission Offers: