MGMT20144 Assessment 2 Essay

MGMT20144 Assessment 2 Essay 1 | P a g e

Essay What challenges do contemporary internet-based organisations face in the management of big data, the internet of things and emerging new technologies? What creative solutions do you recommend to your chosen organisation on this issue? Big data storage facilities are changing the way intelligence is stored and analysed. With the proliferation of connected devices and the growth of the digital economy, data is increasingly being seen as a valuable resource. There are many benefits to storing and processing big volumes of data, but this process often requires collaboration across different divisions within a business. The essay will take an aspect at the challenges faced by modern web-based organisations as they try to manage big data, the Internet of Things, and other forms of emerging technology. The essay will also examine creative approaches and offer suggestions to the chosen company (Woolworths). "Big data" refers to the ever-expanding , always-complicated, and always-varied stores of information. Included are the "three v's" of big data: how much data there is, how quickly it is being created and acquired, and how many different types of data points are being considered. Data mining is a typical way to get large datasets, and the results can take many forms ( Dang et al., 2023). The "internet of things" (IoT) is a network of devices that may exchange data with one another and with cloud-based servers. The "Internet of Things" (IoT) is a system in which various electronic devices, home appliances, and even human internal functions are linked together. There has been an increase in the usage of IoT by organisations across various industries in order to increase efficiency, improve customer service, increase decision-making accuracy, and ultimately increase profits ( Peter et al., 2023). Emerging technologies include those that have only recently been developed or have not yet found widespread practical use. These innovations might be brand new, but they can also be modified, tried-and-true methods. The perception that new technology will fundamentally alter an industry is prevalent. Technology in the disciplines of education, information, nanotechnology, biology, robotics, and artificial intelligence are all examples of what are known as "emerging technologies ( Sestino et al., 2020)." Challenges internet-based organisations face in the management of big data, the internet of things and emerging new technologies 2 | P a g e

testing, as well as using an unsupported version of the application, can all lead to security flaws in software. Hackers can utilise these openings to install dangerous software or steal sensitive information. It is possible to reduce the risk of software compromise by the adoption of safe coding practises by developers and regular patching and configuration by end users ( Javaid & Khan, 2021). Businesses and individuals alike should use stringent security measures such as firewalls, antivirus software, and intrusion detection systems. Hackers can easily exploit individual IoT devices and whole networks due to software weaknesses. Insider threats: Instead of coming from outside sources like hackers or cybercriminals, security dangers that come from within an organisation are called "insider threats." Employees who purposefully or inadvertently do harm to the organisation, contractors who misuse their access credentials, and insiders who are forced into compromising the security of the organisation are all examples of insider threats ( Li et al., 2019). The actions of a malicious employee within a corporation can have far-reaching effects, including the disclosure of confidential information, the theft of intellectual property, and even the tarnishing of the organisation's good name. Frequent training on security and privacy regulations, strict access limitations, and regular monitoring of employee activities can all help reduce the risk of insider attacks. Organisations should also have a strategy for identifying insider threats, thwarting them, and recovering from the harm they do it ( Lutz, 2019). Insider attacks, whether malicious or accidental, represent a serious risk to the integrity of IoT systems. Big data, the internet of things, and other kinds of emerging technology present significant challenges for the Woolworth Cooperative's efforts to develop customer-centric solutions. Many people are concerned that their privacy may be violated as a result of the advent of big data. Using loyalty cards and GPS tracking data, supermarkets may get valuable insight on their clientele. Such information might likewise be sold or used for targeted advertising. Lack of transparency in data gathering and usage is another issue with big data in supermarkets (Woolworths, 2023). Since supermarkets are not required to inform customers, many shoppers likely have no idea how their data is being used. If information is omitted, customers may feel duped and suspicious. Big data also poses a significant threat to data security. Due to the abundance of valuable data stored in supermarkets, they are easy prey for hackers (Mitchell, 2021). A breach in a supermarket's big data system might lead to the theft of sensitive customer information like credit card numbers or addresses. Due to the potential 4 | P a g e

for data breaches brought on by the use of unreliable third-party solutions, it is imperative that great care be taken while storing and retrieving sensitive customer information. Many different types of data quality issues may develop throughout the process of gaining insights from enormous data sets. Issues in this domain include data that is inconsistent, erroneous, or out of date (Reuter, 2021). Analysts access a centralised database to conduct their analyses, but much information is lost in the process of transferring data from each site to the database. There are a variety of other elements that might lead to data quality issues, such as ambiguous wording, inaccurate SKU classification and matching mistakes, and human error. It is possible that poor quality data is more of a liability than an advantage because it can only lead to erroneous conclusions. Sales numbers, customer footfall, profit margins, stock levels, advertising campaign efficacy, and so on are all examples of the kind of data that might prove most beneficial in a retail setting (Su, 2021). The wide range of available data compounds the challenge by necessitating a toolbox of approaches. Recommendation of creative approaches for solving issues For a store like Woolworths, which carries hundreds of products and offers different discounts on different days of the week, finding the optimal price structure can be difficult. The degree to which consumers react to price fluctuations varies greatly among product types and demographics. They can still discover the price that works best for them, even though a one-size-fits-all approach to pricing will not yield the best results. Advanced analytic tools make it feasible to offer customers personalised prices based on their individual interests and buying habits (Woolworths, 2023) . However, a little tweaking here and there might make a big difference. Woolworth can do something similar to what K-Mart has done successfully in the Australia by analysing customers' past shopping and web browsing behaviour, displaying, at the customer level, the types of products that he or she typically buys when shopping or the product on promotion that might be of interest to them when they land on the website ( Misra et al., 2020). An increasing number of establishments, beyond only online merchants, are initiating the provision of tailored services. In fact, app-guided shopping visits will be the norm by 2034, according to Woolworths' The Future of Fresh research, which will completely transform the in-store shopping experience. Based on the user's current location and product history, the app might offer customised recommendations and in-store savings. Using data analytics, Woolworths was able to confirm a significant change in pricing strategies and predict the 5 | P a g e

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References Dang, V. A., Vu Khanh, Q., Nguyen, V. H., Nguyen, T., & Nguyen, D. C. (2023). Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity. Sensors , 23 (9), 4200. https://www.mdpi.com/1424-8220/23/9/4200 Javaid, M., & Khan, I. H. (2021). Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic. Journal of oral biology and craniofacial research , 11 (2), 209-214. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7897999/ Kabalci, Y., Kabalci, E., Padmanaban, S., Holm-Nielsen, J. B., & Blaabjerg, F. (2019). Internet of things applications as energy internet in smart grids and smart environments. Electronics , 8 (9), 972. https://www.mdpi.com/2079-9292/8/9/972/pdf Kumar, S., Tiwari, P., & Zymbler, M. (2019). Internet of Things is a revolutionary approach for future technology enhancement: a review. Journal of Big data , 6 (1), 1-21. https://link.springer.com/article/10.1186/s40537-019-0268-2 Li, F., Lam, K. Y., Li, X., Sheng, Z., Hua, J., & Wang, L. (2019). Advances and emerging challenges in cognitive internet-of-things. IEEE Transactions on Industrial Informatics , 16 (8), 5489-5496. https://core.ac.uk/download/pdf/237444541.pdf Lutz, C. (2019). Digital inequalities in the age of artificial intelligence and big data. Human Behavior and Emerging Technologies , 1 (2), 141-148. https://biopen.bi.no/bi- xmlui/bitstream/handle/11250/2596781/Lit+Review+HBET.pdf?sequence=4 Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2020). IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of things Journal , 9 (9), 6305-6324. https://ksra.eu/wp- content/uploads/2020/08/10.1109@JIOT.2020.2998584.pdf Mitchell, S. (2021, April 20). Woolworths doubles down on data, takes control of Quantium. Australian Financial Review . https://www.afr.com/companies/retail/woolworths-doubles- down-on-data-takes-control-of-quantium-20210420-p57knw Peter, O., Pradhan, A., & Mbohwa, C. (2023). Industrial internet of things (IIoT): opportunities, challenges, and requirements in manufacturing businesses in emerging economies. Procedia Computer Science , 217 (2) , 856-865. 8 | P a g e