Expert Techniques For Big Data Query Manipulation And Aggregated Data Modeling Essay.

Learning Outcomes:

1. Expertly apply techniques to perform big data query manipulation, evaluate various data storage option and type of aggregated data modelling. Through a critical study, choose an appropriate storage model based on the application requirements for processing large amounts of structured and unstructured data.

2. Carry out research on emerging Big Data technologies to evolve models/solutions such as configurable and executable compute jobs on top of using distributed and shared memory architecture and Resilient Distributed Data Sets (RDDs).

This assessment expects the student to submit a critique report on a research article related to a Big Data and its current trend technologies. This assessment is designed to improve student presentation skills and to give students experience in researching a topic and writing a report/Critique report relevant to the Unit of Study subject matter. The research articles would be tasked to explore further research trends relevant to the unit content. As further research findings evolve the unit lecturer may supplement or substitute these to keep the research delivery current and updated.

For this component you will prepare a report/critique on an academic paper related to Big data or big data technologies, Big data analytics, Big data security etc. The paper you select must be directly relevant to one of these major topics.

Kache, F. and Seuring, S., 2017. Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Production Management.

This paper has focused on the issues and opportunities faced in digital information at intersection of Big data analytics and supply chain management system. “There have been variety of supply chain management system with the utilization of Big Data Analytics. The purpose of this study has been contributing towards theory development of SCM with the help of potential effects of Big Data Analytics on information utilization in the business world. Big Data Analytics has been gaining importance in business world with its massive capacity for enhancing business performance.  This research has used Delphi technique for identifying opportunities and issues in the interaction of Big Data Analytics and supply chain management. However, this report has been a critique to this research paper. There will be proper analysis of the research paper based on other studies and papers based on the same topic.

Data has been an important operator in this digital world.  The amount of data and information have been helping maintaining keen approach in the development of the organization. However, there have been challenges in maintaining huge amount of data and information in the business organization. The term big data has been used in this context for managing this huge amount of data in a business organization. The potential for triggering a revolution has been a key driver for capturing value in business [1]. The research questions have been derived in this research paper. However, the researchers has chosen Delphi method to answer these research questions. Inductive approach has been used in this research for compacting the literature of the paper. As argued by [3], deductive might be used in this paper for focusing on existing theories and models based on big data on supply chain management systems. The design of the report has been properly mentioned in the paper which has helped in getting an overview of the knowledge attainable form this paper. The emergence of the internet has been helping in gaining amount of knowledge related to the implication of bug data in SCM. The predictive nature of Big Data Analytics have been unplanned in nature.  Thusly, not having the option to mine the information accessible and subsequently not approaching real, precise, and important data speaks to a hazard for organizations and thusly for the gracefully chain, as choices should be made on a dependable, proof driven premise. This holds particularly obvious concerning SCM, which vigorously relies upon the accessibility of precise and modern data for business execution. As needs be, the significance and estimation of data for powerful SCM has been featured broadly in SCM inquire about, most quite with respect to the sharing of data. Be that as it may, given the consistent development of conceivably pertinent data  for example, readings from GPS sensors or client information gathered through cell phones and the comparing difficulties to distinguish the most important things, it is amazing that examination on dangers attached to and originating from the utilization of data in gracefully chains is rare. Specifically, the issue that the absence of forward-thinking and right data, despite the fact that being a basic element for the utilization of Big Data Analytics, may represent a hazard to the flexibly chain has increased little consideration in the scholarly gracefully chain scene, as distinguished in a writing audit concentrate by [9]. From an academic point of view this examination void plainly legitimizes the pertinence of further research around there. Be that as it may, because of the curiosity of the idea of Big Data Analytics, the administration examine network is battling to get a handle on the estimation of this idea as other comparable data the board ideas exist, for example, business knowledge, business investigation, or on the other hand ace information the executives. By the by, from the creators' perspective, the idea of Big Data Analytics regardless of having undisputed similitudes to different ideas, for example, the investigation and evaluation of information or its prescient contact is extraordinary. Depicting an all-encompassing methodology, Big Data Analytics rather supplements the current ideas, offering an augmentation to the extent of these data the board ideas [8]. The enhancing, transformative character of Big Data Analytics can be depicted along two measurements, which the creators marked the birthplace of data measurement and the kind of data measurement.

Overview of Assessment

The article by [7] forms a case for utilizing investigation as a serious advantage. He infers that methodically gathering, breaking down, and following up on information isn't just valuable on the corporate level but at the same time is a key fixing to improve the flexibly chain structure. This as of now focuses to Big Data Analytics as a subject in spite of the fact that without referencing the term. Applying case examine, the articles by [1] the positive effect of investigation capacities on gracefully chain execution. The work in this way adds to a superior comprehension of centered examination application in a gracefully chain arrangement, establishing the framework for the relevance of Big Data Analytics. The work by [4] diagrams the requirement for organizations to build up a information driven business condition. By giving case models the creators underline the significance of information examination for corporate and gracefully chain dynamic. In their publication [6] center around the general relevance of information science, prescient examination, and Big Data as to SCM. The examination by [9] researches the job of information quality for information investigation capacities, for example, prescient investigation, and Big Data. With regards to SCM, the creators Large Data Investigation and SCM underline that, as the intricacy of information builds, observing and controlling information quality is key for compelling administrative dynamic. Featuring the importance of their exploration, they advance a community oriented interdisciplinary methodology including IT specialists just as SCM experts to address the difficulties of information quality in a corporate setting with expect to accomplish more significant levels of information quality just as information honesty. [11] proposes another investigative system intended for the appraisal of social media use in a flexibly chain arrangement. In light of Twitter hashtags, the creator explores how Twitter presents can help organizations on better shape request while simultaneously conveying significant client bits of knowledge which are helpful for new item advancements. Despite the fact that the introduced approach is an intriguing utilization of Big Data with regards to a gracefully chain setting, it comes up short on a more extensive applied methodology. Joining RFID and cloud innovation,[12]  present a savvy choice emotionally supportive network design for creation observing and planning for work serious dispersed assembling arrangements. This paper tends to a key advantage of connecting Big Data Investigation and SCM, as the novel design approach exhibits how the efficient joining of information driven dynamic underway and coordination activities increments data deceivability and straightforwardness over the flexibly chain. The article by [10] examines the capability of utilizing data and correspondence advancements, for example, Big Data Analytics, for the improvement of the cargo transport flexibly chain. Connecting viewpoints, for example, multimodal activities, obstructions to innovation reception, and mechanical patterns, the creators give a clear discussion on how mechanical advancements may affect on gracefully chain combination. As information applicable for administrative dynamic may well begin past the single company's limit, the flexibly chain mix point of view is vital to completely utilizing the advantages of an information driven flexibly chain environment.

Critique of Research Paper

The Delphi Study data collection has been conducted in order to collect data and information rated to the big data analytics in SCM. Data has been analysis has been done with the help of Quantitative method. As argued by [6], secondary data might have been collected with the help of online journals.  Several online journals and articles based on this research topic might be used for gathering data and information based in this topic. The results have identified various opportunities and challenges in applying big data analytics in the SCM.  There have been several challenges depicted including cyber security, integration and collaboration, IT capabilities and infrastructure. The contribution of this research has been focused on intersection of SCM and Big Data analytics in an exploratory manner. As argued by [8], descriptive nature might have been used in this research. Descriptive research design might have helped in gathering more data an information based on the existing theories and models based on intersection of big data and SCM.

Conclusion

It can be concluded that this research paper has helped in gaining exploratory approach to various opportunities and challenges faced in the intersection of big data analytics and SCM. The researchers have well presented its contribution towards the betterment of SCM practices in an organization with the help of big data analytics. However, there have been certain issues focused in the utilization of Big Data in SCM. Other researchers have been showing interest in this topic.  This has been a beneficial part for the development of research ethics. This report has able to critique this research based on other view of other researchers.  This has helped in creating possibilities and future scope for conducting this research in a different methods.” Other researchers think that use of descriptive design and qualitative parch might help in gaining ore knowledge and data related to this issue and opportunities in interaction of big data analytics and supply chain management system.

References

Wang, G., Gunasekaran, A., Ngai, E.W. and Papadopoulos, T., 2016. Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, pp.98-110.

Mishra, D., Gunasekaran, A., Papadopoulos, T. and Childe, S.J., 2018. Big Data and supply chain management: a review and bibliometric analysis. Annals of Operations Research, 270(1-2), pp.313-336.

Kache, F. and Seuring, S., 2017. Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Production Management.

Alotaibi, S. and Mehmood, R., 2017, November. Big data enabled healthcare supply chain management: opportunities and challenges. In International Conference on Smart Cities, Infrastructure, Technologies and Applications (pp. 207-215). Springer, Cham.

Büyüközkan, G. and Çifçi, G., 2019, July. Extending QFD with Pythagorean Fuzzy Sets for Sustainable Supply Chain Management. In International Conference on Intelligent and Fuzzy Systems (pp. 123-132). Springer, Cham.

Tiwari, S., Wee, H.M. and Daryanto, Y., 2018. Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, pp.319-330.

Brinch, M., 2018. Understanding the value of big data in supply chain management and its business processes. International Journal of Operations & Production Management.

Wamba, S.F., Gunasekaran, A., Papadopoulos, T. and Ngai, E., 2018. Big data analytics in logistics and supply chain management. The International Journal of Logistics Management.

Lamba, K. and Singh, S.P., 2017. Big data in operations and supply chain management: current trends and future perspectives. Production Planning & Control, 28(11-12), pp.877-890.

Richey, R.G., Morgan, T.R., Lindsey-Hall, K. and Adams, F.G., 2016. A global exploration of big data in the supply chain. International Journal of Physical Distribution & Logistics Management.

Sanders, N.R. and Ganeshan, R., 2018. Big data in supply chain management. Production and Operations Management, 27(10), pp.1745-1748.

Nargundkar, A. and Kulkarni, A.J., 2020. Big Data in Supply Chain Management and Medicinal Domain. In Big Data Analytics in Healthcare (pp. 45-54). Springer, Cham.