Discuss about the Big Data Use in Business Organizations.
It is evident that all the businesses in the world irrespective of their sixes along with the amount of business do not require a huge data amount along with insights to turn out to be successful within the increasingly competitive business surrounding (Akter et al. 2016). The use of big data within the companies or its management facilitates in the recognition of the target consumer group along with facilitating to recognize the preferences or the choices of the consumers. The identification, implementation along with the evaluation of distinct data forms can be helpful in attaining all the organizational objectives to make their business successful within the complex competitive environment (Chen, Preston and Swink 2015). The report intends to deal with the contemporary concerns related with the use of big data by the companies. The paper will also focus on explaining the research methodology that will be employed in investigating the role of big data use within companies.
The major objective in carrying out investigation in this paper is to reveal the ways in which the necessary big data or bulk amount is employed for the daily use of business operations by the organizations.
Another objective of this report is to evaluate the reasons behind use of such type of information by the business companies and also the major users of the different large data forms by the organizational heads will be analyzed.
The research has an objective of playing a vital role in determining suitable use, needs and storage for the big data by various business organizations.
The current research focuses on investigating the role of big data use within the business organization irrespective of the fact whether it is large or small (Akter et al. 2016). The research has taken into consideration a huge amount of distinct elements such as the likes of distinct data types used by companies, their implications, their applications, implementation along with the way they are employed by the companies. The scope of this research is also focused on providing useful implications regarding process of the big data use by companies so that they can bring competitive advantages (Erevelles and Swayne 2016). The emergence of all such different aspects within this current research has facilitated in developing the research scope that is much larger in size. It is deemed that this repot has several implications in analyzing the advantages and use of big data in companies that will make it one of the useful reports which can be helpful for the researchers to carry out future research on this topic.
Big Data
Erevelles, Fukawa and Swayne (2016) indicated that the big data within the organizations are required for different reasons that encompass the likes of the identification of the anticipated consumers for the company, preference identification regarding the consumers along with several other similar factors. Erevelles and Swayne (2016) revealed that big data can be explained as a combination of all the processes along with tools that is associated with the management and use of large data set volume. The following concept was introduced for attaining a clear picture of the preferences, trends along with patterns amongst huge range of database which was generated at the time the individuals interacts with distinct kinds of systems. Flick (2015) also stated that the big data concept is not new and it is observed that the modern business world has evidenced increased dependence on the big data by the organizations in order to install basic facilities along with implementing the same for attaining market success. These researchers also evidenced that implementation of big data by these companies clears the way in order to attain increased information regarding the market condition, the consumer strategies along with focusing greatly in the consumer perceptions. The benefits of the big data are explained in most of the previous researches available.
Xu, Frankwick and Ramirez (2016) examined several advantages that can be attained by the big data are explained below:
Taylor, Bogdan and DeVault (2015) certain vital processes through which the following data can be implemented and employed by management of the organizations:
Primary Question
The primary question set out in this research and that is to be answered through the completion of this research is:
The secondary questions those are to be answered in investigating the big data se within business organizations are explained under:
Qualitative Research
Qualitative data for investigating the role of big data use in the organizations will be gathered from employing the questionnaire method. Interview method will be employed by the researcher in attaining suitable responses on big data use within their organizations for attaining competitive advantages (Smith 2015). In employing such qualitative data collection method certain effective open and close ended questions will be asked to all the selected survey respondents or managers of selected Australian organizations. In addition, the researcher will primarily consider inviting the employees of Australian organizations in filling up the questionnaire through social media platform (Alvesson and Sköldberg 2017). For attaining qualitative data the researcher has also considered sending e-mails to its respondents that have shown increased interest and the respondents are offered a period of seven days in fling up the questionnaire. Such qualitative method has facilitated in gathering necessary explanation, understanding along with interpretation of employees and situations in which investigating is carried out (Taylor, Bogdan and DeVault 2015).
In investigating the role of big data use in business organizations, the researcher has considered employing both the primary as well as secondary data. Secondary data regarding big data use by organizations will be gathered from authentic government websites, journals and the authentic data sources (Chen, Preston and Swink 2015). The quantitative data focuses on attaining a relatively bigger size for its analysis. Considering the same, the employees working within the selected organizations will be selected in evaluating the ways in which big data is used by these Australian companies (Taylor, Bogdan and DeVault 2015). Single random sampling is employed by the researchers in selecting appropriate size for the reason that in this sampling technique there is an equal chance of all the respondents to get selected from the selected research population (Alvesson and Sköldberg 2017).
Considering the same, 51 employees from selected companies of Australia are chosen for attaining their viewpoints regarding big data use in and competitive advantages that are attained by their organizations. Quantitative data will be gathered through employing interview research instrument as this can offer relevant information on the subject of inclination regarding human perception regarding specific research information of big data use (Alvesson and Sköldberg 2017). Quantitative data gathered will be analyzed through employing reliable statistical tools such as MS excel. Moreover, correlation and regression analysis will also be conducted for attaining suitable results regarding the data gathered from the employee opinions. Such results gathered will be represented through employing graphs and charts that can facilitate in better evaluation of collected interview responses (Alvesson and Sköldberg 2017).
Desire of having several research implications, the current research has numerous limitations that are associated with its data collection method. As primary data has been gathered from the managers of the selected organizations there are increased risk regarding ensuring the reliability of such data (Alvesson and Sköldberg 2017). The reliability of the information gathered from interview might get affected as the employees might have a tendency to providing positive image of the companies through disclosing the major concerns. Another limitation of this research is too observed in collection of secondary data as it might get manipulated to a certain extent. Moreover, this might also affect the research quality along with the research findings (Ary et al. 2018).
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Conclusion
The report intends to deal with the contemporary concerns related with the use of big data by the companies. The paper will also focus on explaining the research methodology that will be employed in investigating the role of big data use within companies. It was gathered from the paper that the reliability of the information gathered from interview might get affected as the employees might have a tendency to providing positive image of the companies through disclosing the major concerns. However, certain research implications can also be gathered from such investigation. The paper gathered that big data is efficient in collecting and employing feedback attained from consumers. Evaluation of such consumer feedback analysis can facilitate companies in understanding their product expectations based on which amendments can be made in organizations structure. Considering the same, qualitative and quantitative data has been collected in gathering suitable research findings based on the viewports of the selected managers of selected organizations.
References
Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R. and Childe, S.J., 2016. How to improve firm performance using big data analytics capability and business strategy alignment?. International Journal of Production Economics, 182, pp.113-131.
Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R. and Childe, S.J., 2016. How to improve firm performance using big data analytics capability and business strategy alignment?. International Journal of Production Economics, 182, pp.113-131.
Alvesson, M. and Sköldberg, K., 2017. Reflexive methodology: New vistas for qualitative research. Sage.
Ary, D., Jacobs, L.C., Irvine, C.K.S. and Walker, D., 2018. Introduction to research in education. Cengage Learning.
Chen, D.Q., Preston, D.S. and Swink, M., 2015. How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), pp.4-39.
Erevelles, S., and Swayne, L., 2016. Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), pp.897-904.
Erevelles, S., Fukawa, N. and Swayne, L., 2016. Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), pp.897-904.
Erevelles, S. and Swayne, L., 2016. Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), pp.897-904.
Flick, U., 2015. Introducing research methodology: A beginner’s guide to doing a research project. Sage.
Gandomi, A. and Haider, M., 2015. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), pp.137-144.
George, G., Haas, M.R. and Pentland, A., 2014. Big data and management. Academy of management Journal, 57(2), pp.321-326.
Mackey, A. and Gass, S.M., 2015. Second language research: Methodology and design. Routledge.
Riggins, F.J. and Wamba, S.F., 2015, January. Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics. In System Sciences (HICSS), 2015 48th Hawaii International Conference on (pp. 1531-1540). IEEE.
Smith, J.A. ed., 2015. Qualitative psychology: A practical guide to research methods. Sage.
Taylor, S.J., Bogdan, R. and DeVault, M., 2015. Introduction to qualitative research methods: A guidebook and resource. John Wiley & Sons.
Wamba, S.F., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D., 2015. How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, pp.234-246.
Wamba, S.F., Akter, S., Edwards, and Gnanzou, D., 2015. How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, pp.234-246.
Xu, Z., Frankwick, G.L. and Ramirez, E., 2016. Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), pp.1562-1566.
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