DATA4000 Introduction to Business Analytics Report 2 Sample
Brief
As an analyst within Bank of Ireland, you have been tasked with considering ways in which customer data can be used to further assist Bank of Ireland with its marketing campaigns. As a further task, you have been asked to consider how Bank of Ireland could potentially assist other vendors interested in the credit card history of its customers.
Based on your own independent research, you are required to evaluate the implications of the European legislation such as GDPR on Bank of Ireland’s proposed analytics project and overall business model.
Your report can be structured using the following headings:
Data Usability
- Benefits and costs of the database to its stakeholders.
- Descriptive, predictive and prescriptive applications of the data available and the data analytics software tools this would require.
Data Security and privacy
- Data security, privacy and accuracy issues associated with the use of the database in the way proposed in the brief.
Ethical Considerations
- The ethical considerations behind whether the customer has the option to opt in or opt out of having their data used and stored in the way proposed by the analytics brief
- Other ethical issues of gathering, maintaining and using the data in the way proposed above.
- How developments in AI intersects with data security, privacy and ethics, especially in light of your proposed analytics project.
Use the resources provided as well as your own research to assist with data collection and data privacy discussions.
Solution
1. Introduction
The General Data Protection Regulation (GDPR) has significant insinuations for The Bank of Ireland's overall business model and proposed analytics project. The bank must ensure that the personal data it collects from its 50,000 customers, should be processed and stored as part of the project and complies with the GDPR's strict requirements for data privacy and security. Since the Bank of Ireland faced a massive data breach that affected 47,000 customers, it affected the bank's data feed and the centralised system that stores and collects customer information about various financial debts. This includes obtaining explicit consent from customers for ensuring that the data is collected for specified, explicit and legitimate purposes and to protect the data from unauthorised access and theft to avoid any future fines such as the fine of €463,000. This report will focus on ethical considerations, data usability, privacy, security, and artificial intelligence with reference to the Bank of Ireland.
2. Data Usability
2.1 Costs and Benefits of the Database to the Stakeholders of an Organisation
Data usability refers to the ease with which data can be accessed, processed, and understood by various stakeholders. This is an important aspect of any data analytics project such as the Bank of Ireland’s proposed analytics project, as the ultimate goal is to turn data into actionable insights that can be used to inform decision-making and drive business results (Li et al., 2021). There are numerous benefits to data usability, including improved decision-making, enhanced customer experience, and increased efficiency. By providing stakeholders of the Bank of Ireland with accurate, relevant, and up-to-date data, they are better equipped to make informed decisions that drive business growth and success. One of the main challenges is ensuring that data is secure and protected from unauthorized access, which can be especially difficult in the context of large and complex data analytics projects.
Another important consideration for the Bank of Ireland is the issue of data privacy. With increased concerns about data privacy and security, stakeholders must be careful to ensure that data is used ethically and in agreement with relevant regulations and laws, such as the General Data Protection Regulation (GDPR) (Hoofnagle et al., 2019). This requires careful attention to data privacy and security practices, such as implementing strong encryption and access controls, and ensuring that data is only used for the purposes for which it was collected. Despite these costs, the benefits of data usability far outweigh the drawbacks. By providing stakeholders of the bank with the information they need to make informed decisions and drive business success, data analytics can help organisations achieve their goals and achieve long-term growth and success.
2.2 Concept of Prescriptive, Predictive, and Descriptive Applications the Data Analytics Software Tools Required
Descriptive analytics is the process of summarising and organising data to provide a clear picture of what has happened in the past. It is the first step in the data analytics process and is focused on understanding the underlying patterns and trends in data. Descriptive analytics is typically used for reporting, trend analysis, and identifying areas for improvement. Software tools required for descriptive analytics include data visualization tools, spreadsheets, and business intelligence (BI) software. Data visualization tools, such as Tableau and PowerBI, allow users to create interactive dashboards and charts that make it easy to understand and communicate complex data (Kaissis et al., 2020). Spreadsheets, such as Microsoft Excel, are commonly used for data organisation and manipulation.
Predictive analytics is the procedure of using statistical algorithms, historical data, and machine learning methods to identify the probability of future outcomes. It helps organisations make informed decisions by providing insights into future trends and behaviour patterns. Predictive analytics can be used in a variety of industries, including finance, healthcare, and retail, to make informed predictions about customer behaviour, market trends, and more (Aryal et al., 2020). Software tools required for predictive analytics include machine learning platforms, data visualization tools, and statistical analysis software. Machine learning platforms, such as TensorFlow and sci-kit-learn, provide powerful algorithms and models for making predictions. Data visualization tools, such as Tableau and PowerBI, allow users to create interactive dashboards and charts that make it easy to understand and communicate predictive insights.
Prescriptive analytics for university assignment help is the process of using data, mathematical modelling, and optimization techniques to recommend specific actions to achieve desired outcomes. It goes beyond predictive analytics by not only predicting what might occur but also providing recommendations on what actions to take in response. Prescriptive analytics can be used to solve complex business problems and optimize decision-making in areas such as supply chain management, workforce planning, and customer behaviour analysis (Ardito et al., 2020). Software tools required for prescriptive analytics include mathematical modelling software, optimization tools, and simulation tools. Mathematical modelling software, such as AMPL and Gurobi, provide a range of mathematical algorithms and models for solving complex optimization problems. Optimization tools, such as CPLEX and Xpress, are used to identify the best course of action given a set of constraints and objectives.
3. Data Privacy and Security
3.1 Data Privacy, Security, and Accuracy Issues
Data security, privacy, and accuracy are critical issues in the world of data analytics. Ensuring the protection of sensitive information is a top priority for organisations, as data breaches can result in significant financial losses and harm to a company's reputation. Data privacy is a particularly pressing issue, as many organisations collect and store sensitive personal information, such as financial and medical records. The General Data Protection Regulation (GDPR) in Europe set strict guidelines for the assemblage, storage, and use of personal data, and organisations must ensure that they are in compliance with these regulations (Ferraris et al., 2019). This requires careful attention to data privacy and security practices, such as implementing strong encryption and access controls, and ensuring that data is only used for the purposes for which it was collected. Data accuracy is also important for ensuring that decision-making is based on reliable information. Inaccurate data can lead to poor decision-making and negatively impact the overall success of an organisation. It is important to have quality control processes in place to validate and verify the accuracy of data, and to identify and correct errors on time.
4. Ethical Considerations of Using Customer Data by Organisations
4.1 Ethical Considerations regarding the Options Available to the Customers
The options available to a customer regarding the usage of their personal data by organizations such as the Bank of Ireland involves several ethical considerations and is often regarded to be complex and multifaceted. On one hand, companies argue that the use of customer data is critical to improving their products and services and providing a more personalised customer experience (Dubey et al., 2021). On the other hand, customers may be concerned about the privacy of their personal information and the potential for misuse or abuse of their data. One key ethical consideration is informed consent. Customers should be fully informed about how their data will be used and stored and should have the opportunity to make an informed choice about whether they want their data to be used in this way.
Another important consideration is data security. Customer data is often sensitive and confidential, and organisations have a responsibility to protect this information from unauthorised access and use. This requires the implementation of robust security measures and protocols to ensure that customer data is protected from theft, loss, and other forms of abuse. Finally, organisations must consider the potential consequences of using customer data, such as the potential for discrimination or the misuse of data for malicious purposes. This requires careful evaluation of the data collection and use practices, and taking steps to minimize the potential for harm and to ensure that data is used in an ethical and responsible manner (Kelleher et al., 2020). Therefore, the ethical considerations involving the consent of customer regarding how their data will be used and stored by the Bank of Ireland should be regarded by authorities with careful considerations and judgements.
4.2 Other Ethical Ramifications that should be considered
The gathering, maintenance, and use of data raise a range of ethical issues that organisations must consider. These issues can include issues related to privacy, discrimination, transparency, and accountability. One key ethical issue is privacy. As organisations collect and store more data about individuals, there is a growing concern about the privacy implications of this data collection. Organisations must be transparent about their data collection and storage practices and must take steps to ensure that sensitive personal information is protected from unauthorized access and use (Kabacoff, 2022). Transparency is another important ethical consideration. Organisations such as the Bank of Ireland must be transparent about how they collect, store, and use data, and must give customers the ability to control and manage their personal information. This requires clear and concise communication about data practices and giving customers the ability to access and control their data. Finally, organisations must be accountable for their data use practices. This requires regular monitoring and evaluation of data use practices and taking steps to address any ethical concerns that may arise.
5. The Emergence of Artificial Intelligence in Data Analytics
5.1 Contributions of AI to Data Security, Privacy, and Ethics
Artificial intelligence (AI) has the potential to make significant contributions to data security, privacy, and ethics. AI can be used to enhance data security by detecting and preventing cyber threats, such as hacking and fraud, through the use of machine learning algorithms. This can help to protect sensitive personal information from unauthorized access and use. For example, AI can be used to automatically redact sensitive information from documents or to mask sensitive information in databases (Efron and Hastie, 2021). This helps to prevent the accidental or intentional release of confidential information. In addition, AI can also contribute to ethical considerations by allowing organisations to better understand and manage the potential consequences of their data use practices. For example, the Bank of Ireland can use AI to evaluate the potential for discrimination or bias in data use practices, and to identify and address any ethical concerns that may arise.
6. Conclusion
The Bank of Ireland may use data analytics to gain a deeper understanding of its customer's needs and preferences, which can be used to develop products and services to better meet their needs. However, there are also costs associated with data usability. The main challenge is ensuring that data is secure and protected from unauthorised access, which can be especially difficult in the context of large and complex data analytics projects.
References
Ardito, L., Scuotto, V., Del Giudice, M. and Petruzzelli, A.M., 2019. A bibliometric analysis of research on Big Data analytics for business and management. Management Decision, 57(8), pp.1993-2009.
Aryal, A., Liao, Y., Nattuthurai, P. and Li, B., 2020. The emerging big data analytics and IoT in supply chain management: a systematic review. Supply Chain Management: An International Journal, 25(2), pp.141-156.
Dubey, R., Gunasekaran, A., Childe, S.J., Fosso Wamba, S., Roubaud, D. and Foropon, C., 2021. Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), pp.110-128.
Efron, B. and Hastie, T., 2021. Computer age statistical inference, student edition: algorithms, evidence, and data science (Vol. 6). Cambridge University Press.
Ferraris, A., Mazzoleni, A., Devalle, A. and Couturier, J., 2019. Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57(8), pp.1923-1936.
Hoofnagle, C.J., Van Der Sloot, B. and Borgesius, F.Z., 2019. The European Union general data protection regulation: what it is and what it means. Information & Communications Technology Law, 28(1), pp.65-98.
Kabacoff, R., 2022. R in Action: Data Analysis and Graphics with R and Tidyverse. Simon and Schuster.
Kaissis, G.A., Makowski, M.R., Rückert, D. and Braren, R.F., 2020. Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6), pp.305-311.
Kelleher, J.D., Mac Namee, B. and D'arcy, A., 2020. Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT press.
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X. and He, B., 2021. A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering.
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