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Ethics in Information Systems Technology; Individual Privacy and Intellectual Property

Some of the most crucial concepts in understanding the opportunities and challenges of information systems technology include data security, anonymity, and privacy. While security, anonymity, and privacy are often said with the same breadth, they do not imply the same thing. Furthermore, depending on the needs of an organization and an individual, they can prioritize one over the other. This brief paper explores contemporary information systems technology and the internet and how they pose challenges to protecting individual privacy and intellectual property. It discusses the difference between anonymity, privacy, and the ethical issues of increased anonymity. Similarly, the paper distinguishes between cloud computing, green computing, and quantum computing and further describes the services that comprise I.T infrastructure beyond physical devices and software applications. Beyond that, it explores the issues underlying the failure in the case study: How Reliable Is Big Data?

Contemporary Information Systems Technology and the Internet and the Challenge to Individual Privacy and Intellectual Property

Information communication, technology, and the internet have significantly grown, and recent developments pose significant challenges to individuals’ privacy and intellectual property. According to Laudon and Laudon (2019), the digital appearance conveys much more data on individuals, including the most intricate elements of their lives. When an organization gets access to personal data, it is not just the privacy, but the individuals losing control over their information too. Privacy laws anticipate that personal data are stored and processed accurately and safeguarded against unlawful and unauthorized processing, damage, destruction, theft, or loss. On the other hand, intellectual property laws allow creative owners to exercise their monopoly on intellectual property rights. These comprise a series of exclusive rights that exclude others from making, replicating, or using particular intangible creations for a set timeline.

Modern data storage and analysis allow organizations and individuals different ways to collect personal information from various sources and analyze it to create a comprehensive electronic profile of individuals and their behaviors. It is common to find websites and online systems that require personal data to access information and services. These sites normally store cookies, save this personal information, and later use them for different purposes (Mai, 2016). While cookies and other web monitoring functionalities closely track the activities of website visitors, there have been cases of information mishandling. Data that flows through these the internet can be monitored at different points.

In most cases, this information is not encrypted and can be accessed by anyone. Similarly, it follows that not all websites have strong privacy protection policies, and they barely allow for informed consent concerning the use of personal data. Traditional copyright regulations are limited to protecting against software pryany as digital material can be replicated easily and shared across different locations simultaneously over the web.

Anonymity and Privacy

One of the distinct features of the internet is its ability to make individuals express themselves anonymously through made-up usernames. At the same time, some people argue that anonymity is meant to ensure their privacy. Studies have developed reports to separate the two. Khalilov and Levi (2018) noted that anonymity and privacy do not mean the same thing; while the two are often used concurrently and can be censused, it is important to tell one from the other. Anonymity is described as keeping one’s identity private but not actions. For example, individuals have often used pseudo-accounts to express their opinions on social media platforms. Anonymity is hiding or concealing an individual identity, not one’s actions. Individuals can be anonymous physically by covering their faces and fingerprints. In the digital world, they can maintain anonymity by preventing online entities from collecting or storing the information that can be used in identifying them. Both anonymity and privacy should be individual rights. This is particularly due to the significance of anonymity in the freedom of speech and in the case of whistleblowers. Anonymity is necessary to safeguard individuals in cases where their opinions can jeopardize their privacy or work. Nonetheless, anonymity often overlaps with privacy and allows an individual to browse the internet without fearing anyone tracking their logs.

On the other hand, privacy can be described as keeping things to oneself, including an individual’s actions. For example, an individual can message their friend privately on social media, so they know who is engaging, but only they can decode the message. Based on the case of a computerized device, these can be unencrypted without using a password. Everyone can know their colleague has a personal computer, but they cannot read their messages. If somebody goes through the messages without their permission, it is an invasion of privacy, even if they do not intend to commit a crime. Regarding online privacy, it is a matter of how much individuals can keep their personal information to themselves when going over the internet or using software on their computers.

Ethical Issues Raised by Increased Anonymity

Anonymity is an ethical practice meant to safeguard the privacy of individuals while gathering, analyzing, and reporting their data. Nonetheless, anonymity has often been criticized as its increased use makes individuals lack responsibility, the feelings of other people being valued more in online spaces than in the real world, and often lacking the implications of those that behave unethically. Cross et al. (2015) explored how morals relate to the levels of anonymity during online engagements. While being anonymous is often linked with being online, this anonymity often creates the feeling of deindividuation, which is the case when an individual loses their sense of indemnity. This is the feeling that makes people not take responsibility for their actions. Pointedly, most people do not see the necessity of morality when anonymously engaging online since they are unlikely to take the consequences of their statements if someone gets hurt.

Cloud Computing, Green Computing, and Quantum Computing

Could computing describe the delivery of computing services including intelligence, analytics, software, networking, databases, storage, and servers- over the internet (in this case, the cloud) to offer enhanced and speedy innovation, the flexibility of resources, and economy of scale. Green computing describes the eco-friendly and environmentally responsible use of I.T.I.T. infrastructure (Sofia & Kumar, 2015). This also concerns the study of the design, engineering, manufacture, use, and disposal of computers in ways that have a minimal environmental impact. The difference between green computing and cloud computing is that green computing is the environmental impact attributed to machines and technology. In contrast, cloud computing refers to a device consumer internet service.

On the other hand, quantum computing refers to the area of computer science focused on developing technologies subject to the principles of quantum theory. Pointedly, quantum computing applies the distinct behaviors of quantum physics to solve issues that are too dynamic for classical computing. While cloud computing is a form of distributed computing that applies more services linked over the internet, quantum computing is founded upon the need to manipulate complex objects. Similar to cloud computing, quantum computing offers speed but more power. However, it is still in its earliest stage and not widely available as cloud and green computing.

Services that Comprise I.T.I.T. Infrastructure; Beyond Physical Devices and Software Applications

I.T.I.T. infrastructures consist of software applications and physical devices to operate the entire enterprise. However, I.T.I.T. infrastructure also comprises a series of firmwide sectors for which the management budgets are composed of physical hardware, software, and technical capabilities. The technical capabilities go beyond the usual physical devices and software applications. Laudon and Laudon (2019 ) highlighted these services as the Physical facilities management services that develop and manage the physical installations needed for computing, telecommunications, and data management services.

The services also include the I.T.I.T. management services that plan and develop the infrastructure, coordinate with the business units for I.T.I.T. services, manage to account for the I.T.I.T. expenditure and provide project management services. It includes the I.T.I.T. standards services that provide the firm and its business units with policies that determine which information technology will be used, when, and how. Similarly, it includes the I.T.I.T. education services that provide training in system use to employees and offer managers training in how to plan for and manage I.T.I.T. investments, that it includes the I.T.I.T. research and development services that provide the firm with research on potential future I.T.I.T. projects and investments that could help the firm differentiate itself in the marketplace. These “service platform” perspectives make it easier to understand the enterprise value provided by infrastructure investments.

How Reliable Is Big Data?

According to the case study, How Reliable Is Big Data? Modern enterprises deal with an avalanche of data from social media, search, and sensors, as well as from traditional sources. Analyzing billions of data points collected on patients, healthcare providers, and the effectiveness of prescriptions and treatments has helped the U.K.U.K. National Health Service (NHS) save about 581 million pounds (U.S.U.S. $784 million) (Laudon & Laudon, 2019). Compiling significant amounts of data about drugs and treatments given to cancer patients and correlating that information with patient outcomes has helped NHS identify more effective treatment protocols. Nonetheless, there are limitations to using big data. Laudon and Laudon (2019) further noted that some companies have rushed to start big data projects without establishing a business goal for this new information or key performance metrics to measure success. While these enterprises swim in numbers, they may still need to collect the right information or use the data to make smarter decisions.

The Need for Big Data for all Organizations

Big data analysis infers the processes of uncovering data corrections, patterns, and trends in significant amounts of war data to help make data-informed choices. Such processes employ familiar statistical analysis approaches such as regression and clustering and use them in more extensive datasets with the help of modern advanced tools. The future of businesses is for those that know how to collect big data. However, the opponents of this view have argued that not all companies need to analyze big data. as such, smaller enterprises that barely depend on technology (such as convenience stores) are likely to have to make rather extreme investments in technology so that they can benefit from bug data analysis (Cai & Zhu, 2015). Big data analytics can help organizations in Customer Acquisition and Retention, developing Focused and Targeted Promotions, Potential Risks Identification, Innovate products and services, developing Complex Supplier Networks, Cost optimization, and improving Efficiency. Businesses looking to grow in the competitive landscape, whether small or large, needs valuable data and insights. When it comes to an understanding the target audience and clients` preferences, big data plays a very crucial role. While it helps the business anticipate its needs, the correct data must be effectively presented and properly analyzed.

Management and Organization, and Technology Considerations in Big Data Implementation

Big data analysis is necessary to improve decision-making, notwithstanding the significant investment. Hiver, Big Data can pose a challenge to businesses. These issues cut across Data quality, storage, a shortage of data science experts, validating data, and gathering data from many sources. According to Merino et al. (2016), some issues organizations encounter when managing big data include Storage, Processing, Security, Finding and Fixing Data Quality Issues, Scaling Big Data Systems, Evaluating and Selecting Big Data Technologies, Big Data Environments, and Real-Time Insights.

Nonetheless, not all organizations can guarantee that the integrity of the data is followed to the latter. As such, the managers need to ensure that data governance measures are adhered to by all employees. One of the questions that organizations should ask themselves is if they already have a big data platform. If affirmative, the management should look towards developing a cross-functional, consistent, and combined architecture that would offer further opportunities for cross-business analysis and make the most out of scarce technical resources that exist in the leading-edge technology. Big data technologies can be classified into four types, data visualization, data analytics, data mining, and data storage. The organization must ensure that its existing systems align with this scope. The available platforms should enable faster data processing. For example, enterprises that look to store and process tens of terabytes of data using open-source distributed file systems are preferable due to their predictable scalability over clustered hardware. Similarly, this serves as the base platform for already established big data architectures.


Consequently, information communication and technology, and the internet have significantly grown. I.T. infrastructures consist of a series of software applications and physical devices that are needed to operate the entire enterprise. Recent technological developments in the sector pose significant challenges to individuals’ privacy and intellectual property. Contemporary information systems technology has also marked intellectual property even more challenging to protect as digital material can be replicated and shared across different locations simultaneously over the internet. The paper described anonymity as keeping one’s identity private but not actions. On the other hand, privacy has been presented as keeping things to oneself, which can include an individual’s actions. While being anonymous is often linked with being online, this anonymity often creates the feeling of deinviduation, which is the case when an individual loses their sense of indemnity. Similar to cloud computing, quantum computing offers speed but more power. However, quantum computing is still in its earliest stage and not widely available that cloud and green computing. It is worth noting that while only some companies can handle big data, its analysis is necessary to improve decision-making, notwithstanding the significant investment.


Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data science journal14.

Cross, D., Barnes, A., Papageorgiou, A., Hadwen, K., Hearn, L., & Lester, L. (2015). A social–ecological framework for understanding and reducing cyberbullying behaviours. Aggression and Violent Behavior23, 109-117.

Khalilov, M. C. K., & Levi, A. (2018). A survey on anonymity and privacy in bitcoin-like digital cash systems. IEEE Communications Surveys & Tutorials20(3), 2543-2585.

Laudon, K. C., & Laudon, J. P. (2019). Management information systems: Managing the

digital firm. Pearson Education.

Mai, J. E. (2016). Big data privacy: The datafication of personal information. The Information Society32(3), 192-199.

Merino, J., Caballero, I., Rivas, B., Serrano, M., & Piattini, M. (2016). A data quality in use model for big data. Future Generation Computer Systems63, 123-130.

Sofia, A. S., & Kumar, P. G. (2015). Implementation of energy efficient green computing in cloud computing. International Journal of Enterprise Network Management6(3), 222-237.


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