Hi6008 Business | Use Of Assessment Answers

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  

Answer:

Introduction

The topic of the study is focused on Big Data and how it’s used within business organizations. As such, the primary aim of the project is to understand how Big Data can be utilized to realize optimal storage of resources, reduce the time spent in computation and improve the process of decision-making.  According to Lin (2013) Big Data refers to the huge amount of data that cannot be processes under normal or traditional systems. Accordingly, there are three primary features of Big Data including volume, velocity, and variety. The volume represents the large amount of data. Velocity refers to the streams on social media platforms. Finally, the variety refers to the different formats of data such as the fully structured, the semi-structured, and the unstructured data format. Currently the amount of data being produced in the different formats is very large and is constantly increasing. According to Weng (2013) there are as much as 2.5 quintillion bytes of data being produced on a daily basis. The exponential growth of data is increasingly becoming unmanageable for small business organizations as the data analytics technology is incapable of handling data of such magnitude. The use of Big Data in business organizations provides businesses with the opportunity for improved management that includes the production of new opportunities, improved arrangement, and lower cost of data management among other routine business operations. Ultimately, this paper proposes to investigate the role of Big Data in business organizations.

Project Objective

The core objective of project will be to investigate the use of Big Data in business organizations. Other objectives include reviewing literature on Big Data to understand the optimal reliability of Big Data for timely and informed decision-making processes. The challenges of Big Data will also be discussed to understand the implication of Big Data use in businesses.

Project Scope

This study shall analyse the role of Big Data business organizations. However, the research shall be limited to the investigation of the benefits and challenges of using Big Data in different organizations across several industries. The study shall assume that all businesses under review are using Big Data in their routine operations.

Literature Review

The majority of the businesses in the current world are hooking the enormous information in Big Data, its advantages and its impacts on the businesses. What's more, the organizations are also using Big Data procedure or planning to actualize huge information in their business association. It is essential to have important information of merchandise, clients, and processes with the execution of huge information to discover better approaches to manage within a competitive market (Davenport, 2013). The main purpose for the utilization of Big Data in the business organization is to help in decision makings for current and future needs of the organization. The basic leadership of the associations helps in settling on keen choices that can help them in having an immense effect on the market and industry. The business choices are made based on value-based information in present and past to help make an educated choice.

Be that as it may, there is another sort of information, i.e. organized information, for example, online networking, web journals, photos, messages that are utilized for successful basic leadership in organizations. For instance, Oracle offers the enormous information items for obtaining and sorting out these kinds of information and breaks down to discover new bits of knowledge (Costa, 2012). The enormous information arrangements take that is easier to obtain, sort out, and interpret huge information and, settle on keen choices based on the investigation. The regular models are likewise depicted to comprehend the procedure of significant worth extraction from Big Data in organizations (Lin, 2013). ETL (Extract, Transform, and Load) is the primary concept, and the secondary concepot is Interactive Questions model. Furthermore, the third model is a predictive examination. Another case is that of Intel that takes the colossal preferred standpoint of Big Data to accelerate the process of development in the associations. Business organizations like Google, Facebook, LinkedIn, eBay employ Big Data an effective and highly efficient manner. These associations have effectively incorporated Big Data with all kind of assets in the business to take final points of interest inside the business. The following (Figure 1) shows the procedure for the imminent associations for enormous information.

The above-outlined procedures in the figure have particular considerations while actualizing Big Data. The choice criteria are reliant on the vital choice components like economic, social and technological variables. The situations of the candidate are unique thus different organizations will choose different sets of data from the Big Data according to their need in a more careful manner for optimal results  (Crawford, 2013). The indicators for technology are cloud evaluations, information stockroom, and huge information perception and installed evaluations. The evaluation pointers in innovation are adoption apportion of big business, industry qualities, worldwide market size and barriers to access in the market and industry. The suggestions for innovation planning can be for the huge demand situation and for the warily optimistic situation (Halevi, 2012). The appropriation of particular techniques in the associations can receive huge rewards for the association.

Goals of Big Data

Big Data helps in accomplishing the distinctive objectives of business after the selection and execution of the association. The fundamental objectives of Big Data in setting to business associations are said underneath for better comprehension of study: Reduced costs; reduced time (Bensrhir, 2013); support business decision-making; development of new data (Chang, 2008).

Data Mining with Big Data

The process of data mining in business in a continuous routine that never ends. The variables of Big Data examination incorporate advancement speed, viable vigor, and investigation of a large measure of information. The information is expanding quickly and created throughout the years as far as the number of clients, measure in recent years. The investigation in organizations has additionally been expanded radically, and it gets important for them to utilize enormous information to stay away from future issues (Hems, 2013). The data extraction from the standard information continuously is extraordinary compared to other approaches to comprehend the present circumstance of business association. The stream information is brought progressively and at a quick speed in huge information else; it is extremely hard to stream the information and incorporates productive computation of calculations, mining of information and exactness of calculations. For instance, small-scale websites, web-based social networking, online news are gotten from the streams based on clients (Pulse, 2012). These streams have no definite answer, and Samoa the online stag used in the mining of these streams of data. It was utilized for the online information mining inside the cloud condition. Samoa can be used on various handling stream motors and it will likewise be utilized as an open source in future and can prompt huge upheaval in the mining of huge information gushing.

Hace is also proposed as an important stage in the examination and data mining processes. This information drive shows totals, numerous sources and breaks down it from the points of view of information mining. Huge Data is being utilized as a part of Intel for business knowledge change as a substantial measure of their endeavour information was unstructured (Schroeck, 2012).

Big Data Applications 

The utilization of Big Data is dynamic and can be utilized as a part of different enterprises. The application of Big Data has several examples including the utilization of the application as a part of oil and gas field for keeping up the hardware to counteract disappointment, advancement of cost and creation, and guarantee consistency and security measures. The opposition in oil and gas industry is high and faces general changes. It is essential for oil and gas firms to expand the volume of creation and in the meantime, keep up the sound and safe condition. Huge numbers of these organizations are utilizing Big Data for generation and investigation of oil to diminish their costs, increment creation and keep up business esteems (Laptev, 2013). The utilization of Big Data is wide as it can be utilized from alternate points of view. For instance, the site of Barrack Obama has utilized Big Data application to discover the discourse impacts that are accessible on the Whitehouse site on races. All the talks were gathered from the site with the use of scrapper. Guide lessens, and Hadoop was utilized for parallel handling of addresses. The outcomes were exceptionally useful in making the race methodologies as these contemplations are the construct and have a significant impact with respect to the decisions. The usage of Big Data can possibly give useful outcomes.

Big Data frameworks

The substantial number of information is prepared by the associations that produce high system traffic. It is important to outline the information evaluation that can bolster this high system traffic and exceedingly complex frameworks. The most significant facet in the engineering of the Big Data evaluation within Cloud condition is CLAaaS (Cloud-based Analytics as a Service). The primary highlights of CLAaaS are joint effort, help, and customization. The information security can likewise be made with the execution of CLAaaS in the private cloud. Gamecube is a bunch plan and associate the servers with each other and utilized as a topology (Zulkernine, 2013).

Camdoop is used to expand the ability like parcel preparing in systems and play out the exercises of information conglomeration. In this system, the measure of yield is littler than the info estimate. To conquer these issues, the received system was to diminish the movement as opposed to expanding the data transmission. The property of Camdoop is with the end goal that Gamecube utilizes the sending of movement to play out the exercises of information arrange accumulation. Huge table is utilized for putting away the organized information having petabytes measure.  The Google applications information is majorly stored in Big Data tables, where it is retrieved for application in business. The applications like Google Earth, Google Financing, and Web Ordering use the big table; however, the capacity prerequisites of these applications are unique. The capacity, accumulation and use of information can make distinctive sorts of dangers and vulnerabilities (Bifet, 2013). After dangers investigation, the system is proposed to help the usage of information in a compelling way.

 In this structure, a few areas are viewed such as administration, morals, innovation and science. The blend of these spaces in the business association is very compelling while at the same time settling on business choices and stays away from negative circumstances in future undertakings. The technique utilized for precise and quick examination on the substantial arrangement of information is testing for the portrayal of information. Baron structure is recommended that has high exactness level and expanded examination of Big Data. It functions admirably to pick the most suitable example estimate (Costa, 2012). This system is additionally used to mine the calculations to ascertain the blunders and results. The exactness in this structure can be expanded by expanding the example sizes.

Big Data Challenges

Despite the various benefits of Big Data there are several known disadvantages of Big Data application in Business. The difficulties of Big Data are said underneath for better comprehension of study. It is imperative to comprehend the positives and negatives of Big Data previously actualizing in the business associations. Some of the negatives include privacy and security (Dijcks, 2013); dynamic provisioning (Lin, 2013); algorithms (Pulse, 2012); misuse of data and information (Bensrhir, 2013); and data management.

Facts and Figures of Big Data

The business choices are the key action of the associations, and these choices rely upon the present circumstances and analysis systems. The tasks of associations rely upon these business choices, and these choices rely upon the information mining calculations methods of information mining and Big Data systems (Luers, 2013). The reconciliation of information mining and Big Data system can prompt better and exact basic leadership process in the business. It is imperative for associations to mastermind and breaks down various sorts of information from numerous sources. It will empower associations to have the energy to break down various size and kind of information from changed sources to pick up inside and out and solid yields (Halevi, 2012). These yields can be as qualities, examples and business patterns. The diverse sorts of information utilized as a part of business choices are demonstrated in the underneath (Figure 2) for better comprehension of study.

The underneath (Figure 3) demonstrates the portion of business choice help information that is utilized for deciding. The business association thinks about 70% of the item information and 76% of the shopper information for business choice help purposes (Lin, 2013). In the item information, decision making is influenced by 61% of the purchasing side and 62% of the offering side (Laptev, 2013). In the client information, 66% of the business shopper information is utilized for settling on business choices (Costa, 2012).

Gap

There’s a limitation in the number of relevant published materials for the regarding the topic of the study and the keywords, as the use of Big Data in business is a relatively new phenomenon, and much still needs to be done to help future retailers in innovation and decision making.

Justification

The findings of this study will contribute significantly to the existing body of research on big data in business. Students and organizations that are interest in big at will find this project to be helpful.

Research Questions

Primary Question

1. What is the use of big data in a business organization?

Secondary Questions

1. What is the process of data mining with Big Data?
2. How is Big Data applied in business today?
3. What is the framework of Big Data?
4. What are the challenges of Big Data?

Research Design and Methodology

The study will ask some questions to fill the research gap and answer the research question to meet the objective of the project and make recommendations for future studies. Using the positivism philosophy to answer the research questions, the study will employ both qualitative and quantitative methods of study (mixed approach).

Qualitative Research

The qualitative design employed will be used to determine the facts and figures of Big Data (Saunders, Lewis, and Thornhill, 2015). To do this effectively, the study will review secondary published material from relevant databases including but not limited to Google Books, Google Scholar, and the University Library. A search on the database will include the keywords and phrase of the study (Big Data, business organization, uses, facts, figures) in different combinations until 16 of the most relevant literature with the best combination are selected.

Quantitative Research

The quantitative research wills employee the face-to-face interview method. The population to be samples will be selected through purposive stratified random sampling techniques (Bryman and Bell, 2015). This technique will allow the researcher to purposively select 5 business organizations using Big Data based on accessibility. At the same time the Researcher will be able to distinguish the participants within the identified business organizations in to three strata based on the level of employment, that is, executive, top manager, and Supervisor. The identified participants will be selected randomly with two representatives from each organization to form a sample size of 10 respondents, an executive member, a top manager and/or a supervisor. The interview will seek to know about the goals of Big Data in business; the Big Data mining process; the application of Big Data in business especially in innovation and decision making; the Big Data framework; and the challenges of Big Data. To test for the validity of the interview questions, the researcher will evaluate 5 colleagues individually to determine the clarity and ability of the tool to collect relevant data that is reliable and valid (Saunders et al, 2013). In case of ambiguities then the researcher shall adjust the interview questions accordingly.

With the consent of the University and that of the business organizations, the researcher will collect data with each interview projected to take between 30 to 45 minutes. Respondents will be informed of their voluntary right to participate in the research with the freedom to quit at any point of the study. They will be assured of their privacy and confidentiality of use of their information in no other research without their consent except for the purposes of this research.

Data collected will be analyzed using descriptive statistics to determine the frequency, mean, median, mode, and the distribution of variables on a normal curve. The results will be presented in tables and graphs. From the analysis, the data will be interpreted as meaningful information. The findings will be used to determine a conclusion and make future recommendations.    

Research Limitations

The main limitation of the study is the sample size which is smaller and can be generalized to represent the cross-sectional issue of using Big Data in the business organization while using a longitudinal approach. Other limitations are in the qualitative and quantitative data collection process. In qualitative research, there’s a limitation in the number of relevant published materials for the regarding the topic of the study and the keywords, as the use of Big Data in business is a relatively new phenomenon and much still needs to be done to help future retailers in innovation and decision making. Finally, face to face interviews are expensive since the researcher will be required to travel yet transport fees are dynamic and therefore unpredictable. This may disorient the budget of the research. Moreover, in case of a phone interview, more charges may be incurred depending on tariff charges and the length of the interview.

Time Schedule 

Figure 1: Gantt chart showing the research plan

 

Conclusion

This paper provides a review of the use of big data in business organizations and considers how various business organizations are adopting big data to predict future trends in business and analyse the procedures to be taken by businesses for improved outcomes. The review of important literature reveals that big data can be integrated with the daily operations of business organizations. As such, some of the most popular businesses employing big data in their routine operations include LinkedIn, Facebook, Google, and eBay. These organizations use big data in combinations with conventional analytics to achieve optimal outcomes. The use of big data has significant implications on the structure of the organization, and the employee skills on leadership, and technology. Moreover, most of the organizations are largely benefiting from the use of big data as they use specific sections of the data including those related to consumers and products for the purpose of decision making. The main challenges of big data are the concerns on user privacy and confidentiality, potential insecurity, algorithms, and management of data. Nonetheless, a mixed research approach combining both the quantitative and qualitative approaches can best reveal how big data is used in business and the implication involved.       

Reference List

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Bifet, A., 2013. Mining Big Data in Real Time. Informatica, (37).

Bryman, A. & Bell, E., 2015. Business research methods. USA: Oxford University Press.

Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, et al., 2008. Bigtable: A distributed storage system for structured data. ACM Transactions on Computer Systems (TOCS), 26, pp. 4.

Costa, A. Donnelly, A. Rowstron C., & O’Shea , G., 2012. Camdoop: Exploiting in-network aggregation for big data applications. USENIX NSDI.

Crawford, G. Faleiros, A. Luers, P. Meier, C. Perlich, & Thorp J., 2013. Big Data, Communities and Ethical Resilience, A Framework for Action, Big Data, Communities and Ethical Resilience, White paper.

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Halevi, G. and Moed, H., 2012. The evolution of big data as a research and scientific topic: an overview of the literature. Research Trends, 30(1), pp.3-6.

Hems, A., Soofi, A. and Perez, E., 2013. Drilling for New Business Value: How innovative oil and gas companies are using Big Data to outmaneuver the competition. A Microsoft white paper.

Laptev, N., Zeng, K. and Zaniolo, C., 2013, April. Very fast estimation of result and accuracy of big data analytics: The EARL system. In Data Engineering (ICDE), 2013 IEEE 29th International Conference on (pp. 1296-1299). IEEE.

Lin, J. and Ryaboy, D., 2013. Scaling big data mining infrastructure: the twitter experience. ACM SIGKDD Explorations Newsletter, 14(2), pp.6-19.

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Saunders, M., Lewis, P. & Thornhill, A., 2015. Research Methods for Students. 7th ed. Pearson.

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