Eecs E6893 : Big Data Assessment Answers
For this assignment, you are required to critically analyse the content of each article, whereby you must make informed comments on the content of the article that you are reviewing by comparing the main points raised in the article with the ideas, research findings, and opinions of other authors who have published in the area that is under review
Answer:
This paper is a critical literature review of two articles related to big data analytics. This process of analysing large chunks of data is a relatively new trend that is revolutionizing information systems within organizations. The future of large data processing in information systems very bright (Assunção et al. 2015, p.5). There are numerous emerging issues on big data analytics such as its applications in information systems within enterprises, challenges, as well as how it could lead to enterprise development (Yin and Kaynak 2015, p.143). In this critical literature review, I will offer a literary critique of how these articles lead to increase in knowledge as far as big data analytics is concerned. Particularly, the overarching question while reviewing these articles will be:” To what extent do these articles contribute to increased knowledge about big data analytics.”
1. Utilizing Big Data Analytics for Information Systems Research; Challenges, Promises and Guidelines.
This journal by Muller et al (2016, p.289) first acknowledge that analysis of large data is a considered a new paradigm in information systems. They agree that this new area has been successfully applied across disciplines such as Economics and Sociology. This article focuses solely on the challenges, the potential benefits and the directions for applying big data analytics when conducting information system studies. In this part, I will offer a critical review using three sections; challenges, promises and guidelines of big data analytics.
Challenges
It is beyond any reasonable doubt that big data, as an emerging trend in information systems, is faced with many challenges. This article has thus done an excellent job of analysing the various challenges of big data analytics. Furthermore, the evidence of these challenges is backed up with scientific knowledge as well as a review of the past academic literature in this field. A complete comprehension of the state of affairs in big data analytics will enhance our understanding and appreciation of its importance (Kumar and Kumar 2015, p.118) A key dimension of big data analytics is its current shortcomings. From this article, we are able to gain insightful knowledge as far as challenges to big data analytics is concerned. For example, some challenges of big data analytics include the absence of automation in statistical reasoning which complicates big data analytics turning it into a hardship rather than an opportunity.
Promises
The journal also discloses a number of potential promises emerging from the use of large data processing. Universally, there is usually a lot that is expected from emerging issues, and similarly, big data analytics is no exception (Raghupathi and Raghupathi 2014, p.3). Muller et al (2016, p.299) have to some extent, explained some big promises of big data analytics such as overcoming the division between qualitative and quantitative, improvement in the predictive accuracy, among many others. In so doing, the reader is able to know the future of big data analytics as well as what to expect as far as big data analytics is concerned. These promises, in addition, are well supported with scientific knowledge thus contributing to an insightful knowledge about big data analytics.
Guidelines
In this part, the article illustrates the application of large data processing in the area of information system research which can be accomplished through the presentation of an incorporated, exceptional big data analytics study from the online reviews by customers. Data collection forms a major part of big data analytics (Wang, Kung and Byrd 2018, p.3). Its guidelines for application in Muller’s et al journal is presented in a step-wise manner which helps the reader to fully understand. The organization of this part is very clear, alongside the practical examples cited in the article. The article, in addition, performs a great job of summarizing the guidelines for a researcher who apply big data analytics in each phase of research, thus providing a critical information in an excellent and simplified manner.
2# Incorporating Big Data Analytics into Enterprise Information systems
It has been noted that processing of large data is a new trend in enterprise information systems (Hashem et al. 2015, p.98). It is being adopted by various organisations and business across the world and the results are fascinating. However, much about big data analytics is still not known to people and thus, an insightful knowledge about big data will revolutionize the modern business world. In this article critique, we are asking the question, “To what extent does this article lead to increased knowledge on big data analytics?” While answering this question, Sun, Pambel and Wang (2015, p.301) have organised their research into two sections;
- The interrelationbetween large data analytics and Enterprise Information System
- How large data processing can be integrated into Enterprise information system
The interrelation between large data analytics and Enterprise Information Systems
As the reader goes through the article, he/she expects to gain more appreciation of the association between processing large data and enterprise information system. From reading the article, the reader is can objectively conclude that the relationship between the two has been discussed in an effective and simple manner, thus informing the reader. Specifically, the article explains the said interrelationship through first exploring the ontology of big data analytics. Further, the word ontology, which could be new to the reader, has been defined in a such a manner that the reader easily understands its meaning. The big data analytics ontology, in addition, is broad enough to inform the people. For example, the main data analytics components are discussed. The enterprise information system is also discussed comprehensively. To ensure that the reader understands the concept of the interrelationship between big data analytics and enterprise information system, there are charts and flow diagrams. Therefore, the relationship between the two has been discussed effectively.
How large data can be integrated into Enterprise Information System
The article provides a thorough discussion of the incorporation of large data analytics into the Enterprise Information System. This discussion is made easy to understand by presenting a model known as BABES. The model is presented in a flow and chart diagrams which informs the reader. Additionally, this presentation creates a visual impression of the reader, thereby increasing the knowledge on big data analytics. This in-depth discussion of this incorporation is sufficient to inform the reader. However, although there are numerous advantages that can be inferred from this discussion relating to the incorporation of big data into EIS, some issues critical to this discussion have been left out. For instance, the writer could have considered addressing some key problems relating to big data analytics and to some extent, the organizational learning needs as it pertains to big data analytics. Therefore, we can say that our overarching questions have been fairly answered.
Conclusion:
The aim of this paper was to examine how the above articles lead to increased knowledge of big data analytics. This critical review enables the reader to deeply understand how big data analytics works and how it could be incorporated into EIS. Several issues related to big data analytics have been addressed in these articles. The journal by Muller et al (2016) clearly brings outs the benefits and challenges of its adoption in EIS. Similarly, the journal by Sun, Pambel and Wang (2015) can also be easily understood. However, it does not bring the challenges of the use of large data processing on EIS. To a large extent the two journals contribute to knowledge about large data processing.
References:
Assunção, M., Calheiros, R., Bianchi, S., Netto, M. and Buyya, R. (2015). Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79(1), pp.3-15.
Hashem, I., Yaqoob, I., Anuar, N., Mokhtar, S., Gani, A. and Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47(1), pp.98-115.
Kumar, A. and Kumar, T. (2015). Big data and analytics: issues, challenges, and opportunities. International Journal of Data Science, 1(2), p.118.
Müller, O., Junglas, I., Brocke, J. and Debortoli, S. (2016). Utilizing big data analytics for information systems research: challenges, promises and guidelines. European Journal of Information Systems, 25(4), pp.289-302.
Raghupathi, W. and Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), p.3.
Sun, Z., Wang, F. and Pambel, F. (2015). Incorporating big data analytics into enterprise Information Systems. Information and Communication Technology. ICT-EurAsia, 9357, pp. 300-311.
Wang, Y., Kung, L. and Byrd, T. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(1), pp.3-13.
Yin, S. and Kaynak, O. (2015). Big data for modern industry: challenges and trends [point of view]. Proceedings of the IEEE, 103(2), pp.143-146.
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