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For this assessment, students are expected to present a written research proposal that is based on the literature review (Assessment 1) and demonstrates their understanding of business research/research paradigm.
Based on their literature review, students are required to write a business research proposal outlining the details of the proposed research project that addresses the gaps identified in the literature. Derived from the business topic, the proposal needs to include clearly state research questions (primary and secondary), and provide a solid research methodology that supports the feasibility of the research project
Marking criteria of the research proposal include: the justification of the research questions that operationalize the research topic; the demonstration of a detailed research methodology addressing research instrument, sample size and sampling approach, research location/s and schedule as well as providing a critical assessment of the limitations of the methodology and the expected outcome of the research project.
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The terms big data and cloud computing has gained a lot of importance and recognition during the past years both in academia and in industry. The idea of big data has evolved over the years due to continuous increase in data in terms of volume and variety (De Mauro, Greco, & Grimaldi, 2015) generated by organizations, the internet, social media platforms, and increase in automation across a variety of application areas and so on. It is important to note that big data does not fall into the realm of traditional transactional databases but are highly unstructured and are generated by multiple sources. In fact, big data growth can be understood from the rise in the number of users on the internet. Therefore, big data has become quite relevant in all types of organizations. Especially in the case of business organizations, big data is considered highly essential as it offers different insights which are not easily visible with transactional data (Villers, Olofson, & Eastwood, 2011). These insights contain the potential to enhance business value which is substantiated by the industry (Eric Boyd, Chandy, & Cunha Jr, 2010).
Cloud computing is regarded as a significant technology development because it provides IT as a service and offers numerous advantages to enterprise organizations in terms of resources and cost savings (Shayan, Azarnik, Chuprat, Karamizadeh, & Alizadeh, 2014). Over the years cloud computing platforms have evolved and proved to have abilities to perform large-scale computing tasks that are complex namely big data processing. Clouds due to their scalable architecture and distributed computing abilities provide the capacity to easily handle large datasets and manage big data applications well by storing, updating and processing large datasets from multiple sources (Kaisler, Armour, Espinosa, & Money, 2013). Clouds are increasingly adopted by organizations for big data applications and for integrating big data big data sources with internal transactional data and deploying them for processing and analytics (Wu, Zhu, Wu, Q. & Ding, 2014). There are numerous industry white papers and survey reports to demonstrate that organizations that have cloud deployments and big data solution implementations are able to gain more value and achieve competitiveness (Davenport & Dyche, 2013).
The report affirms the claim that big data with clouds can truly be a business value enabler. To establish this claim, primary research is conducted using a survey questionnaire. The participants in the survey are Australian business firms that make use of big data in the cloud for their business operations. The developed survey questionnaire is provided in appendix 1. The research is conducted using relevant synthetic data pertaining to this area of big data on clouds. Synthetic data is derived from industry information sources to arrive at data sets as appropriate to generate data for the question statements in the questionnaire. The data set is further analyzed using statistical methods, and the results are presented to verify if the proposed claim holds true. The results are interpreted and discussions provided in the report.
The term big data is often understood in terms of volume and size of data. According to Manyika, et al. (2012), big data is a dataset that typical database system tools and software cannot manage because the size of datasets is beyond the capabilities of RDBMS software that capture, store, analyze and manage data. Schroeck, et al. (2012), stated that due to growth in different types of data, the term big data came into practice.
Clouds offer businesses to leverage a variety of applications (Delen & Demirkan, 2013). Examples include Hadoop, MapReduce, analytics software, BI tools, etc. (Chang, Ranjan, Xuyun, Chi, Georgakopoulos, & Jinjun, 2013). According to Hashe, et al. (2015), virtualization provides processing capabilities needed by big data environments. Big data is ideal for clouds because clouds have computing capabilities to extract information and knowledge from them, along with predicting trends, patterns, and identify new business potential (Hashem, Yaqoob, Anuar, Mokhtar, Gani, & Khan, 2015). Further, big data due to their large volume are usually described as consisting of variety, velocity, value, veracity and volume (Sakr & Gaber, 2014). The Proceedings of the 3rd International Conference on Computational Techniques & AI, Myo & Thein (2014) explains that clouds help resolve the effort of the total cost of ownership (TCO), which includes expenses such as hardware, software licenses, IT personnel and infrastructure maintenance. Therefore, clouds offer significant benefits in terms of the latest technology at affordable prices leading to its adoption by all type of organizations (Neves, Schmerl, Bernardino, & Cámara, 2016).
