MBA633 Real-world Business Analytics and Management Case Study Sample
Your Task
Develop a real-world business analytics project plan/proposal based on the learnings from the course.
Assessment Description
This assessment seeks to simulate a real-world task that you may have to undertake. Therefore, the assignment is non-prescriptive and requires you to pose a relevant, creative and significant problem to solve that could result in benefits to the organisation of choice. In this assessment, you need to consider an organisation in an industry of your choice and articulate the steps needed to enable data driven decision making. The organisation and industry need to be familiar (e.g., where you have worked or are working, a future start-up company), NOT an organisation such as Amazon/Boeing/Qantas etc.
Assessment Instructions
• Week 11
Identify a company and industry you are familiar with that would benefit from Data Analytics.
• Week 12
Draft a preliminary report - You are encouraged to consider the current mode of operation, highlight the possible inefficiencies, and look for available data and how this data may be used to provide efficiencies based on the concepts and techniques covered in the subject. Your lecturer will advise on the appropriateness of your choice and proposed methodology with regard to the requirements for the assessment. In addition to the components discussed in your draft, your final report should include the project life cycle, management of the project, ethical considerations and stakeholder management as discussed in course workshops.
Solutions
Introduction
Background:
The report demonstrates developing a real-world business analytics project plan for the innovative IT company Insentra. The company's vision to help the IT partners and vendors progress. It provides help to global professionals and managed services (Insentragroup 2021).
Purpose:
The report will evaluate the benefits for the company which can occur from data analytics. By portraying the company's possible inefficiencies, the proper technique to use data analytics will be introduced. For Assignment Help, The key metrics identified from data will help describe the use of data analytics to tackle the possible challenges in organisational growth.
Identification of Problem within the Organisation
Problem Statement
Modern technology has provided scope to the companies for managing their operational capabilities through a data-driven decision-making process (Zhang et al. 2017). The innovative Australian company Insentra supports other companies in increasing user service capabilities for better decisions to implement business intelligence solutions by providing them with cloud services, information data management, and migration services (Owler 2021). The operational strategy of the organisation focuses upon improving the efficiency of business performance by using modern technology. However, this approach can create issues of security risks in data management. The cyber threats or attacks are prevailing to harm the company's data privacy. To provide effective solutions based on modern database networks, the company manages multiple devices, which can potentially create risks of data breaching.. The issue related to data security not only creates a risk in operational networks but also can lead to decreasing the revenues of the company.
Relationship with The Industry
The company is selected as the availability of information, which will help to analyse in brief. As per my views,the distribution network of the company, which is managed by IT partners and vendors, can increase the risk of theft for the company and those companies that take aid from the IT professional networks of the company (McGeveran 2018). To manage high risk in the global network of the company, proper security measures are required.
Ethical Considerations
Data breaching needs to be protected through a proper legislative policy. In that case, the data protection act can help the company secure the organisation's relevant information and clients. Proper policy implementation will help to manage the organisation's ethics.
Methodology
The CRISP methodology provides a framework for planning data mining projects (Sv-Europe2021). To understand the benefit of data analytics for the company, the CRISP approach will be useful. It will help to solve the problem related to the business of Insentra company. The risk management strategy can be developed using the CRISP model, which will help to implement the data analytics process within the company to solve possible issues. The six steps of the models, such as business understanding, data understanding, data preparation, modelling, evaluation, and deployment, will help create a proper solution for the company.
Business Understanding: Identifying the business need is necessary to implement a change plan (Wyzoo 2021). In the case of Insentra, the vision to provide IT services to the client is the objective of the business. The company helps its partners to grow, and through this technique, they promote mutual benefit (Insentragroup 2021). To solve the resourcing challenges, the company acts as a global industry leader. Therefore, to manage risk in its IT networks, the company needs to implement proper analytics techniques.
Data Understanding: The information may include the details of the company, clients, and employees. These are specific data that need high protection. The data incorporates to achieve the business aim, which is to provide IT services to other companies. Vulnerability in information management systems can lead to the threat of cyber-hacking (Xu et al. 2018).
Data Preparation: Identifying new data points through the existing entries can help create a predictive model to drive away from the possible threats in an information system. The company will create a data dictionary and then data analytics for proper data preparation. To secure the basic information, the process is necessary.
Modelling: Using machine learning tools and techniques, the information gained from data preparation will be used to create a specific model for managing the data risk. The testing process helps to understand the performance level of the model.
Evaluation: The process will not entirely depend upon machine learning, but the specific members within the company will also engage within the process of evaluation. The evaluation will help understand the areas where risk can occur and how data analytics can mitigate the possible cyber risk.
Deployment: The result gained from evaluating the information will help to understand the proper deployment method. The monitoring process can help to identify the possible threats in the IT networks of the company.
Challenges and Limitations
The limitations of the approach can be the lack of clarity in the process, blind hand-offs to IT, mindless rework, and failure to iterate (Taylor 2017). The need for repeatable approaches leads to create persistent problems in the CRISP methodology. So, the company can face challenges in reviewing the available data and developing a proper analytic model. However, the approach can limit the proper implementation of the data analytics technique as it creates complexity for the IT members.
Benefits and Consequences
The CRISP approach can provide a long-term strategy for risk management for Insentra. The model will avail data for the IT specialist of the company so that they can create proper use of data and improve confidentiality. The approach will help to focus the team upon the objective; thus, the team will be able to understand the need for change to create proper security of data, and they will apply specific analytical tools for risk management. For example, blockchain technology provides effective security in IT networks (Taylor et al. 2020). However, the approach will increase the flexibility in work which will help the employees of Insentra to work to achieve the vision of the company.
Management of the Project
To manage the project of the company, the Agile method will help to divide the project into several iterations. This will help the company to break the large project into manageable tasks. The agile method will increase the speed of the project life cycle (Workfront 2021). On the other hand, the Prince 2 method helps guide the project by managing small to large-scale projects. The project management method helps to manage risk through implanting its theme in the project environment (Axelos 2019). The PMBOK method project management best practices and standard application of guidelines and framework. However, it is a traditional project management technique that does not provide efficient agile and Prince 2 methods. However, for managing risk in the company's IT network and to create a proper method for handling the project, Prince 2 will be an effective project management method.
Key Stakeholders
The key stakeholders can be the project manager, owner of the company, employees, clients, or companies who take services from Insentra and the investors who spend money on the development of the company.
Budget
The large-scale project regarding risk management through data analytics may cost $100,000.
Schedule
The project will be accomplished within 2 months, where each task will be divided into several iterations.
Data
The data or information regarding the project plan will be gathered from company websites and other literary sources such as journal articles, and the testing process of the analytical tool will provide specific data.
Quality:The data will be collected from reliable sources such as literary journals and articles. Books and authentic websites will be also be used. The company website will be a reliable source for data collection.
Data Preparation:The creation of a data dictionary, through dividing the information into segments, will be the first step of preparation where data of last purchase, date of mailing, and other relevant data will be encoded. The next step, data analytics, will help to create data points from the current information, and the process will be useful to identify the data entries like gender information and mailing addresses.
Data Analysis
The effective management process in the performance of the company will help to develop a proper project plan for mitigating the risks. The security metrics can help to ensure the uninterrupted work process and continuous improvement in organisations (Ramos et al. 2017). The analysis of KPIs and key risk indicators will provide an idea of the work process of the security team within the company. The company reduces the information security policy violation. The security compliances for the supply providers have increased up to 70% (Appendix 1). However, the key metrics will provide quantitative information to take proper projection to integrate sensory information. For determining accurate results from the key risk metrics, the data analytics process implementation will benefit the company to retain its present revenue growth. The key elements of security metrics include cost, resources, time, and people (Appendix 2). While managing new data analytics processes such as implementing AI or Blockchain or machine learning techniques, the employees need to be aware of the process.
Data Visualization
.png)
Figure 1: KPIs of the enterprise level
Source : (Securityinfowatch 2021)
The business understanding of security is effective as it scored better than the actual objective. Cost needs to be managed through attaining the objective of business ownership of security risk and timely identification of issues.
.png)
Figure 2: KPIs at enterprise level
Source : (Securityexecutivecouncil 2021b)
The business knowledge and understanding of security requirements to be improved as the actual level is less than the objective. Though the program maturity of security risk appetite is effectively improved. Security risk control measures are also needed to be implemented for business growth.
Identification of Key Metrics
The key metrics help measure the risk potentiality and form possible solutions for mitigating the issues related to risk. The key risk metrics related to the issues within the company Insentra are listed below.
Table 1: Key Risk Metrics
Source : (Developed by the author)
Design of the system
The system for risk management will be based on a specific user interface design. The registration desk within the system will allow the users to input proper credentials for system security. Implementation of data analytics techniques will create a proper storage system for preserving vital information of the users such as names, IP addresses, the organisation they work in, and other basic information. The system will have a communication desk where the clients of the company will be able to ask questions to the company for hiring their IT services. The messages will be end-to-end encrypted. The system will provide access to an unidentified user who is not registered using proper credentials. While information transfer, the analytics within the system will restrict interference of third parties.
Conclusion
The problem related to risk management in the IT network of Insentra can be managed through data analytics techniques. The CRISP approach of the project life cycle has been selected to provide a proper framework for solving the risk issues. For project management, Prince 2 is selected as an effective method. However, the key stakeholders and budget will give a good idea of project planning. The data authentication is proved from delivering quality data sources, and the process of the data preparation has been stated.
Recommendations
• For managing risk, the company needs to identify the proper risk management strategy for its IT networks.
• Implementation of AI or blockchain technology can help to restrict unauthorised access (Wang et al. 2019).
• Risk metrics are needed to be evaluated for understanding the potentiality of risk.
• The stakeholders need to form an integrated network for risk management.
• The designed system for risk management needs to be used properly and updated timely.
References
Axelos 2019, What is PRINCE2? | Methodology & Qualification | AXELOS, viewed 12 June 2021 <https://www.axelos.com/best-practice-solutions/prince2/what-is-prince2>
Cipher 2021, 10 Cybersecurity Metrics You Should Be Monitoring – Cipher, viewed 12 June 2021 <https://cipher.com/blog/10-cybersecurity-metrics-you-should-be-monitoring/>
Insentragroup 2021, Home – Insentra, viewed 12 June 2021 <https://www.insentragroup.com/au/>
McGeveran, W 2018, ‘The Duty of Data Security’, Minn. L. Rev., vol. 103, p. 1135.
Owler 2021, Insentra's Competitors, Revenue, Number of Employees and Funding, viewed 12 June 2021 <https://www.owler.com/company/insentra>
Ramos, A, Lazar, M, Holanda Filho, R & Rodrigues, JJ 2017, ‘Model-based quantitative network security metrics: A survey’, IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2704-2734.
Securityexecutivecouncil 2021a. viewed 12 June 2021 <https://www.securityexecutivecouncil.com/imlibrary/metrics_graphics/KPIs.png>
Securityexecutivecouncil 2021b, viewed 12 June 2021 <https://www.securityexecutivecouncil.com/imlibrary/metrics_graphics/KPIs_Enterprise_lines.png>
Securityinfowatch 2021, viewed 12 June 2021 <https://img.securityinfowatch.com/files/base/cygnus/siw/image/2009/05/Metrics_10537861.png?auto=format&w=720>
Slideplayer 2021, viewed 12 June 2021 <https://slideplayer.com/slide/6287299/21/images/5/Key+elements+of+security+metrics.jpg>
Sv-Europe 2021, Crisp DM methodology - Smart Vision Europe, viewed 12 June 2021 <https://www.sv-europe.com/crisp-dm-methodology/>
Taylor, J 2017, Four Problems in Using CRISP-DM and How To Fix Them – Kdnuggets, viewed 12 June 2021 <https://www.kdnuggets.com/2017/01/four-problems-crisp-dm-fix.html>
Taylor, PJ, Dargahi, T, Dehghantanha, A, Parizi, RM & Choo, KKR 2020, ‘A systematic literature review of blockchain cybersecurity’, Digital Communications and Networks, vol. 6, no. 2, pp. 147-156.
Tunggal, A 2021, 14 Cybersecurity Metrics + KPIs You Must Track in 2021 | UpGuard, viewed 12 June 2021 <https://www.upguard.com/blog/cybersecurity-metrics>
Wang, K, Dong, J, Wang, Y & Yin, H 2019, ‘Securing data with blockchain and AI’, IEEE Access, vol .7, pp. 77981-77989.
Workfront 2021, Agile Project Management - A Beginner's Guide | Adobe Workfront, viewed 12 June 2021 <https://www.workfront.com/project-management/methodologies/agile>
Wyzoo 2021, CRISP-DM Methodology for Data Mining in Marketing, viewed 12 June 2021 <https://wyzoo.com/blog/crispy.html>
Xu, M, Schweitzer, KM, Bateman, RM & Xu, S 2018, ‘Modelling and predicting cyber hacking breaches’, IEEE Transactions on Information Forensics and Security, vol. 13, no. 11, pp. 2856-2871.
Zhang, Y, Ren, S, Liu, Y, Sakao, T & Huisingh, D 2017, ‘A framework for Big Data-driven product lifecycle management’, Journal of Cleaner Production, vol. 159, pp. 229-240.
Would you like to schedule a callback?
Send us a message and we will get back to you
Highlights
Earn While You Learn With Us
Confidentiality Agreement
Money Back Guarantee
Live Expert Sessions
550+ Ph.D Experts
21 Step Quality Check
100% Quality
24*7 Live Help
On Time Delivery
Plagiarism-Free
81 Isla Avenue Glenroy, Mel, VIC, 3046 AU
