The ethical decision-making process in business analysis comprises six consecutive steps. First, it is important to allocate the time for defining the problem, which brings a clearer understanding of the issue and associated problems related to ethics. Taking time is the natural characteristic of opinion leaders, who might remain silent before voicing final decisions based on their cognitive thinking. Second, it is critical to consult resources and seek assistance, since the strategy requires additional guidance that could be validated against opinion leadership and consultancy. Eventually, human resource professionals might assist in this ethical endeavor based on their expertise in labor-related laws. Third, it is essential to think about the lasting effects, since any systematic decision has its consequences and influences project stakeholders. Eventually, it might be a part of the stakeholder analysis exercise and provoke a chain of poorly predicted effects that should be managed and controlled in line with the approved project schedule. However, lasting effects require formal leadership approval, which also requires taking ethical steps in explaining and arguing particular opinions views related to the project.
The rest of the ethical principles are based on external factors and therefore might be adjusted based on the project specifications or expected deliverables. The fourth principle is to consider regulations that exist in other industries that are related to the executed project, assuming that such regulations could bring both benefits and risks. Any project might be at risk of sporadic failure, which means that a clear understanding of potential bottlenecks is paramount to ensure the overall project success. Furthermore, the fifth principle postulates that there is a notion of ‘deciding on a decision, where the action plan could be developed based on the conceptual ideas collected from the previous steps. Finally, the final ethical principle is to implement and evaluate, which normally precedes the formal project closure and eventually manifests during the lessons learned session.
The newest technologies might benefit from the various applications and their versions deployed depending on organizational needs. For virtualization, the following benefits could be identified:
- Virtualization minimizes the operational or physical workload on the system, which allows for storing less data locally and distributing one in the cloud.
- Virtualization reduces costs for computing infrastructure since organizations might not store large amounts of data on local servers
- Virtualization improves productivity since virtual machines are capable of memory stacking and therefore can support users during extended periods
- Virtualization helps in managing multiple devices, which is important for enterprise initiatives such as ‘bring-your-own-device’ and Internet of Things (IoT)-related activities
For green computing, the following benefits were identified:
- Green computing minimizes costs related to corporate social responsibility if the enterprise successfully passes related accreditation procedures
- Green computing helps to develop new organizational procedures related to the use of devices and reporting failures depending on the environmental requirements
- Green computing allows for meeting governmental challenges for reducing environmental challenges and therefore could be used as a prospectus for awarding government-sponsored software development contracts
For the multicore processors, the following benefits were identified:
- Multicore processors decrease the risk of operation system thrashing when multiple page faults are attempted to be resolved
- Multicore processors reduce the workload on the other hardware elements and therefore could be used to emulate certain ‘heavy’ applications
- Multicore processing is cost-efficient since the workload of modern applications is mostly designed to distribute the power between processor cores and therefore allow better functionality
- Multicore processing enhances web navigation since under the appropriate configuration web pages are best displayed when only a single processing unit of the CPU is used
The three applications of sentiment analysis are knowledge-oriented approaches, statistical methods, and hybrid techniques. It is commonly used in organizations involved in e-commerce, where the importance of using contextual communication with both targeted and loyal users is essential. The use of a knowledge-based approach in business ethics is primarily used for matching certain arbitrary words with emotions, which might be best manifested in product or service reviews. For instance, it could be implemented through the use of emojis that reflect the customer’s expectations or experience of using a particular product. Statistical methods are more complex and are usually implemented when the company is oriented toward business changes or seeks service improvements based on the available data collected from the existing user population. In this case, deep learning and semantic analysis are used to classify user groups, preferred product or service categories, as well as data mining.
The hybrid methods are implemented in a narrower industry context, where is high uncertainty in understanding customer behavior. It is more natural for the startup companies that want both to sense the market and acquire a relevant portion of potential customers. In this case, it is crucial to use cloud computing technologies and develop local data repositories that are accessible and could be managed in terms of collecting and retrieving organizational data. However, using hybrid methods might be complex given the organizational requirements for data security, since cloud scenarios and enterprise resource planning policies are not equal and require focused customization from system analysts and software developers.
The most common information system threats include unsolicited data access, personal data manipulation, system failures related to data processing, and account management issues. First, the unsolicited data access assumes that large enterprises might store a significant amount of data that should be restricted to the specified group of users who access the data based on the user privileges. The use of data encryption protocols is paramount here since hacking attempts are majorly based on searching the loopholes in data storage environments and database management issues, leading to unexpected scenarios of data retrieval. Second, personal data manipulation refers to the process of extracting the database information about the credentials f particular users, where the most vulnerable manifestations refer to the banking and service industry. However, these examples are currently being monitored and controlled even n small companies, realizing the importance of customer-centricity.
The system failures related to the data processing are more complex since those might similarly be related to programming issues created internally, or access issues created externally. In this way, it is important to adhere to the principles of careful project planning and system analysis, where the role of system analysts is to prepare a valid testing plan, while the role of programmers is to verify relevant use cases before the system deployment. Finally, account management issues should be handled by system administrators based on the appropriate authentication policies and procedures that regulate secure access to both internal and external working environments.
There are four different systems described in the case study. The first one is the alert system that provides data exchange with the main server to notify system administrators about an offense and further support district attorneys regarding bail applications, charging decisions, and sentencing recommendations using the big data analysis model. Another system also refers to the capabilities of the big data concept, but one is used for forecasting purposes and is aimed to predict the time of the future crime, as well as additional aspects such as the likelihood of crime commitment based on the geographical norms and statistics. Furthermore, the telecommunication surveillance system is used to assist the civil liberty groups in reporting possible or occurring crimes in their neighborhood, while there is a raising concern if such systems are juridically or legally available for use concerning people targeting and personal image processing in terms of data protection laws. Finally, the crime-mapping program was implemented as a state-of-art solution for geolocating most concentrated crime places that could be further extrapolated in social networks, navigation detectors, and other means of public information access.
Overall, the case study shows that systems are feasible for crime prevention and civil monitoring. However, the use of such systems requires training, contribution, and regular system support, which might be complex in police work. Potential recommendations for the system integration are to interview police officers about system applicability and provide initial training related to the system use and practical adoption. However, such effort might require additional costs that are not budgeted at the government level and therefore might require additional compensation in terms of resource planning and rough estimations.