Business Plan Assignment: Best Usage of Big Data in UniSA

Question

Task

Write a detailed report on business plan assignment discussing about the business proposal for UniSA to make the best usage of data collected from wireless devices.

Answer

Introduction
University of South Australia (UniSA) has decided to make the best use of the collected big data so that the collected data can be used for business benefits. The university wants to make a profit by using the required data. UniSA is a very modern, innovative, and popular university with more than 33,000 students. UniSA has a total of four campuses and has several data access points at various places of the university. A proper design for the best usage of big data is highly needed for the university. It will help to collect the data of wireless devices smartly and also make smart use of the collected data. The entire plan is made based on the 1week data collected from the Jeffrey building.

Aims of the design
A business proposal is made to make the best usage of data in UniSA. The university wants to make benefits from the proper usage of data collected from wireless devices. Moreover, the business proposal aims to provide a right direction to UniSA so that an attractive revenue can be generated by making proper usage of data.

Objectives

• To get a proper design for the best usage of data. It will also help to get a clear idea about the importance of collecting data.
• To know about the requirements and preferences of the students based on the collected data from the web applications they are using.
• To get an idea about the business activities of the university that requires changes.
• The strength and weaknesses of the business strategy of the university can also be known from this study.
• To get a proper planning of the data usage method that can be used to get a better understanding of the entire prospect of the university.

Significance of the study
UniSA can easily make the best usage of collected big data if the help of data mining and predictive analysis can be made. It is not suggested to collect only some specific data as all the available data of wireless devices will be used later to get the desired outcome (George et al., 2016). An agile infrastructure will surely help the university to cope up with the changes whenever required. The preference and requirements of the students can change with time. Proper employee training is also required to get a better outcome to form planning (Fisher, 2016). The data of 1 week collected from the Jeffrey building has been used to get an idea about the data that is usually generated on the university campus.

Design for the best usage of Big data
Project Timeline: The project will take 6 months to be completed. The entire process will be started on 12th November, 2020 and it is expected to be executed within 12th May, 2021. The deadline must not be missed to avoid extra expenses and wastage of time.

Methods of data collection: Primary data collection has been used here to collect the data. Data generated in 1 week has been collected from the datasets attached with the Wi-Fi of Jeffrey building.

As the requirements of students can be changed in University very frequently, Agile IT infrastructure has been proposed for the best usage of data in the University. The design will help UniSA to fulfil its objectives. UniSA wants to make the best usage of big data so that it can use the collected data for the business benefits of the organization. A strong and efficient design is required to make the best usage of big data. A huge amount of big data is generated as students of the university use wireless devices such as smartphones, laptops and tablets in the university campus. There are many data access points in the four campuses of the University and huge data is collected from the various parts of the place. A design has been made based on the requirements of the university (Banaeianjahromi and Smolander, 2019). It will help to collect, identify, evaluate, and use big data in a better way. The ideas which are used in the design have been discussed below. All the steps should be followed to get the desired result from the proposed design-

Figure 1: Steps for the usage of big data

(Source: Anshari et al., 2019)

Identification of data: Identification of data is very much essential to get an idea about the best usage of data. All types of data must be collected at the initial stage. Being specific while collecting data can affect the desired outcome. The requirements and preferences of the students of the University can change with time. Therefore, it is very important to collect all the data before identifying the data (Anshari et al., 2019). The identification of data will help to measure the importance of the collected data. The best usage of data will be possible only if unnecessary data can be eliminated. It Will help the authority of UniSA to understand in which aspect the improvement is required (Storey and Song, 2017). By making the required improvements, business benefits can also be gained.

Introduce an agile infrastructure: An agile infrastructure must be implemented to make the best usage of data. The university must have an agile infrastructure so that the system can be changed at any time. As above mentioned, the requirements and preferences of the clients change with time. Moreover, the obsolescence of technologies also requires change at some points (Abbasi, Sarker and Chiang, 2016). Agile infrastructure will help the system to cope up with sudden requirements of changes in the organization.

Comprehensive use of data: Comprehensive use of data is also required after the successful identification and collection of data. It will help UniSA to get the desired outcome with meaningful and easy to understand data. Besides providing the easy to understand data, the comprehensive use of data will also save the time of the authority. Hence, using comprehensive data will help to use collected data more smartly (Abd Ghani et al., 2018). There are different types of data that are collected from the wireless devices used by the students of the University. The university has more than 33000 students and a huge amount of data is generated from the wireless devices used by the students. Comprehensive use of data will help the authority to make the best usage of data in less time (Abbasi, Sarker and Chiang, 2016). The accuracy of the data will also increase if the meaningful and easy to understand data can be used while making decisions regarding the business benefits of the university.

Implementation of data mining: Data mining is a process where raw data is transformed into useful information so that the data can be beneficial for the improvement of the organization. Various types of data are collected from the wireless devices in UniSA. As a huge amount of data is being generated, the transformation of data is highly expected here. Proper knowledge of data mining is expected in the employees who are appointed for the implantation of the new design. There are many steps in the entire process of data mining such as data understanding, data preparation, modeling, evaluation, and deployment (Dutt, Ismail and Herawan, 2017). Evaluation is a very important part of the process as the pattern of data is evaluated here. It ensures whether the data is important or it is Unnecessary regarding the requirements of the process.

Implementation of predictive analytics: Business benefits are possible when the intensity of risk factors can be reduced in the organization. Predictive analytics will help UniSA to avoid the risk factors that can happen due to the wrong usage of data. Predictive analytics will help by giving a clear idea about the thought process of the students. Sudden changes can harm the business activities of the organization. If the changes can be predicted at the initial stage, the intensity of the effect can be reduced (Gunasekaran et al., 2017). For example, the increased or decreased demands of a course can affect the performances of the students. Predictive analytics will help the university to get an idea about the changes at the very initial stage.

Monitoring the entire system: Checking the system frequently is also required to make the best usage of data. It is often found that data leakage or data theft affects the method of data usage (Kibria et al., 2018). Here, proper security must be maintained to protect the sensitive data of the students. Not only the business activities but the reputation of the university will also increase if the data of the students can be protected. The reputation of the university is also indirectly linked with the business benefit of the University (Abbasi, Sarker and Chiang, 2016). The employees appointed for the system should also be monitored. Reports should be done if any kind of suspicious behavior is noticed. Any other devices must not be allowed in the system so that the security of data can be maintained. Monitoring the system will also ensure that all of the data is collected without making any specifications. Business benefits can only be made when reliable data can be collected and processed.

Important functions of the design
The design will require extra efforts but the effort is worth the result. Several functions of the design will help the university to improve their performances and make a good profit. The functions of the design have been discussed below –

1. The design will have to get a clear idea about the requirements of the students. The data collected from the database will help the authority to understand which type of changes are expected by the students (Brown et al., 2017). All the activities such as course design, fee structure, and other important things will be based on the collected data with the help of the design.
2. Students move with their devices such as smartphones, tablets, and laptops. Therefore, the data is collected from different locations of the campuses. Comprehensive use of data will help them to organize the collected data in a better way (Abbasi, Sarker and Chiang, 2016). Meaningful data can be derived if the design can be successfully implemented in the organization.
3. The requirement and preference of the students can change with time. Their course preference, knowledge about cyber-attacks, and activities are changed with time. All the changes can affect the data collection and data process methods. Therefore, it is very much required to implement an agile infrastructure in the university. The agile infrastructure will help the university to cope up with these changes (Dubberly and Pangaro, 2016). At least the intensity of the effects can be reduced by changing the system activities as per the requirements.
4. Raw big data can be transformed into usable data with the help of this design. The processed data will help to derive the best outcome from the huge collected data (Abbasi, Sarker and Chiang, 2016). Moreover, the evaluation of the data pattern will help to make the best usage of data by choosing the right pattern for usage.

Technologies required for the design
Both the concept of data mining and predictive analytics will be used to get a better result. Data mining is required to transform the raw data into usable data. It will help UniSA to make the data collection more organized an also use the data more effectively. On the other hand, predictive analytics will help to get an idea about the future possibilities of the data generated on the campuses of the university.

Data mining: Data mining is a widely used technique that helps to transfer raw data into usable data. The process of data mining included various steps such as data preparation, data interpretation, modeling, evaluation, and deployment. All the steps together will help UniSA to make better collection, integration, and usage of big data (Abd Ghani et al., 2018). Proper knowledge of machine learning is required to implement a data mining process successfully in an organization. The process of data evaluation is the most important step of the entire data mining process as the evaluation of the data pattern is done here. Without evaluating data patterns, the best usage of data is never possible (Williams, 2017). Hence, data mining is a must for UniSA if it wants to make the successful use of data generated in the four campuses of the University.

Predictive analytics: Predictive analytics is also an important technique that must be used in the organization for making better usage of data. Moreover, the risk factors can also be reduced by making predictive analytics at the initial stage. The future requirements of data can be understood by implementing predictive analytics (Müller, Fay and vom Brocke, 2018). The university will be surely benefited by implementing the concept in the organization. Besides, mitigating the risk factors, the technique will also help to improve the business activities of UniSA. The needs of the students can easily be known by implementing predictive analytics at the initial stage.

Efforts required for the implementation of the design
UniSA will have to make a huge effort if it wants successful implementation of the design. The design included several steps and each step is equally important for the success of the design. Some major efforts should be given by the University so that the best usage of data can be made successful. Some of the essential efforts are discussed below –

• The employees must be experienced and enough skills in the implementation of this design. As the data mining process has been used here, the knowledge in machine learning is always required. Without enough knowledge of data mining and predictive analytics, the design cannot be used for the best usage of data (Manca and Ranieri, 2017). Employee training programs will help to make them aware and enough skilled so that they can very easily deal with any kind of problem during the entire process.
• Security services must be implemented in the organization so that the sensitive data of the students cannot be stolen (George et al., 2016). A huge amount of data is collected from the use of wireless devices on university campuses. The huge amount of data also includes sensitive data. The concern of cyber-attack, data breach, and data leakage are growing with time (Dutt, Ismail and Herawan, 2017). UniSA must make sure that all the data collected is being protected. If any kind of malicious attack takes place, both the reputation and business benefit of the organization will be affected to a great extent.
• Wi-Fi services are available in the multiple sections of Jeffrey building so that students can even access web applications when they are moving from one place to another in the University (Elragal and Haddara, 2019). If any kind of disturbance happens in the Wi-Fi services, there is a huge chance that the best usage of data will not be possible (Gunasekaran et al., 2017). Many important data can be skipped if the disturbance in Wi-Fi services take place.

Conclusion
UniSA has to make strong efforts to make the proper implementation of the design mentioned in the business proposal. Data mining and predictive analytics are the two techniques that are must to make the best usage of data for the University. Besides this, it is also mentioned in the business proposal that an agile infrastructure is required so that any kind of change can be made in the system if required. Comprehensive use of data is also required so that the entire process does not become messy. A meaningful and clear concept of data is highly expected in the university as business benefits cannot be made without proper and reliable data.

Employees must be skilled and experienced in the data mining and predictive analytics technique. Lack of knowledge in employees is not expected. Employee training programs can be arranged if required. Cybersecurity concerns should also be kept in mind and it must be made sure by UniSA that the sensitive data of the students are protected. The business proposal will surely help to get a clear idea about the best usage of data. It is important so that the collected and processed data can be used for the business benefit of the university.

Reference list
Abbasi, A., Sarker, S. and Chiang, R.H., 2016. Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), p.3. http://ahmedabbasi.com/wp-content/uploads/J/AbbasiSarkerChiang_BigData_JAIS_2016.pdf

Abd Ghani, M.K., Mohamed, M.A., Mostafa, S.A., Mustapha, A., Aman, H. and Jaber, M.M., 2018. The Design of Flexible Telemedicine Framework for Healthcare Big Data. International Journal of Engineering & Technology, 7(3.20), pp.461-468. https://www.researchgate.net/profile/Mohd_Khanapi_Abd_Ghani/publication/327981999_The_Design_of_Flexible_Telemedicine_Framework_for_Healthcare_Big_Data/links/5bb2731e299bf13e6059f1ae/The-Design-of-Flexible-Telemedicine-Framework-for-Healthcare-Big-Data.pdf

Anshari, M., Almunawar, M.N., Lim, S.A. and Al-Mudimigh, A., 2019. Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), pp.94-101. https://www.sciencedirect.com/science/article/pii/S2210832718300735

Banaeianjahromi, N. and Smolander, K., 2019. Lack of communication and collaboration in enterprise architecture development. Information Systems Frontiers, 21(4), pp.877-908. https://lutpub.lut.fi/bitstream/handle/10024/160604/banaeianjahromi_et_al_lack_of_communication_aam.pdf?sequence=1

Brown, K., Xu, T., Iwabuchi, K., Sato, K., Moody, A., Mohror, K., Jain, N., Bhatele, A., Schulz, M., Pearce, R. and Gokhale, M., 2017, June. Accelerating big data infrastructure and applications (ongoing collaboration). In 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW) (pp. 343-347). IEEE. https://pdfs.semanticscholar.org/2d74/5ce1c9aac8077a3ec2c927030ec6f7f43dd5.pdf#page=13

Dubberly, H. and Pangaro, P., 2016. Distinguishing Between Control and Collaboration—and Communication and Conversation. She Ji: The Journal of Design, Economics, and Innovation, 2(2), pp.116-118. http://www.dubberly.com/wp-content/uploads/2019/01/DDO-Article-Communication-Collaboration.pdf

Dutt, A., Ismail, M.A. and Herawan, T., 2017. A systematic review on educational data mining. Ieee Access, 5, pp.15991-16005. https://ieeexplore.ieee.org/iel7/6287639/6514899/07820050.pdf

Elragal, A. and Haddara, M., 2019. Design science research: evaluation in the lens of big data anal ytics. Systems, 7(2), p.27. https://www.mdpi.com/2079-8954/7/2/27/pdf

Fisher, D., 2016, June. Big data exploration requires collaboration between visualization and data infrastructures. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics (pp. 1-5). https://materials.dagstuhl.de/files/17/17461/17461.DanyelFisher.Preprint.pdf

George, G., Osinga, E.C., Lavie, D. and Scott, B.A., 2016. Big data and data science methods for management research. https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5963&context=lkcsb_research

Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B. and Akter, S., 2017. Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, pp.308-317. https://kar.kent.ac.uk/57171/13/JBR%20accepted.pdf

Jiang, W., 2019. An intelligent supply chain information collaboration model based on Internet of Things and big data. IEEE Access, 7, pp.58324-58335. https://ieeexplore.ieee.org/iel7/6287639/6514899/08698785.pdf

Kibria, M.G., Nguyen, K., Villardi, G.P., Zhao, O., Ishizu, K. and Kojima, F., 2018. Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE access, 6, pp.32328-32338. https://ieeexplore.ieee.org/iel7/6287639/6514899/08360430.pdf

Kumar, R., Singh, S.P. and Lamba, K., 2018. Sustainable robust layout using Big Data approach: A key towards industry 4.0. Journal of Cleaner Production, 204, pp.643-659. https://www.researchgate.net/profile/Surya_Prakash_Singh2/publication/327484452_Sustainable_Robust_Layout_using_Big_Data_approach_A_Key_towards_Industry_40/links/5b952207299bf14739310065/Sustainable-Robust-Layout-using-Big-Data-approach-A-Key-towards-Industry-40.pdf

Manca, S. and Ranieri, M., 2017. Networked scholarship and motivations for social media use in scholarly communication. International review of research in open and distributed learning, 18(2), pp.123-138. https://www.erudit.org/en/journals/irrodl/1900-v1-n1-irrodl05001/1066136ar.pdf

Müller, O., Fay, M. and vom Brocke, J., 2018. The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. Journal of Management Information Systems, 35(2), pp.488-509. https://www.researchgate.net/profile/Oliver_Mueller5/publication/323773046_The_Effect_of_Big_Data_and_Analytics_on_Firm_Performance_An_Econometric_Analysis_Considering_Industry_Characteristics/links/5aaa4ef0a6fdccd3b9baebc3/The-Effect-of-Big-Data-and-Analytics-on-Firm-Performance-An-Econometric-Analysis-Considering-Industry-Characteristics.pdf

Ndikumana, A., Tran, N.H., Ho, T.M., Han, Z., Saad, W., Niyato, D. and Hong, C.S., 2019. Joint communication, computation, caching, and control in big data multi-access edge computing. IEEE Transactions on Mobile Computing, 19(6), pp.1359-1374. https://arxiv.org/pdf/1803.11512

Robinson, N.H., Hamman, J. and Abernathey, R., 2019. Science needs to rethink how it interacts with big data: Five principles for effective scientific big data systems. arXiv preprint arXiv:1908.03356. https://arxiv.org/pdf/1908.03356

Storey, V.C. and Song, I.Y., 2017. Big data technologies and management: What conceptual modeling can do. Data & Knowledge Engineering, 108, pp.50-67. https://files.stample.co/browserUpload/af67f41a-819b-49f6-9ff5-bd584f68d990

Williams, P., 2017. Assessing collaborative learning: big data, analytics and university futures. Assessment & Evaluation in Higher Education, 42(6), pp.978-989. https://hull-repository.worktribe.com/preview/567065/2018-01-28%2013582%20Williams%20FINAL.pdfa

Yeung, K., 2017. ‘Hypernudge’: Big Data as a mode of regulation by design. Information, Communication & Society, 20(1), pp.118-136. https://sts.wisc.edu/wp-content/uploads/sites/328/2019/06/%E2%80%98Hypernudge%E2%80%99-Big-Data-as-a-Mode-of-Regulation-by-Design-Yeung.pdf

Zhou, T., Song, Z. and Sundmacher, K., 2019. Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design. Engineering, 5(6), pp.1017-1026. https://www.sciencedirect.com/science/article/pii/S2095809918313559