DATA4600 Business Analytics Project Management Case Study 1 Sample
Your Task
• This assessment is to be done individually.
• A business project case study is provided below.
• You will be applying concepts related to the first four weeks of your subject to a business case study.
• Your case study report is to be submitted as a Microsoft word file via Turnitin
• LO1 and LO3
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Business case study: Darwin Smart city dashboard
As part of the “Switching on Darwin” project, a huge number of smart devices were installed in Darwin city in order to collect big data for their Smart city dashboard. This includes 684 LED smart streetlights, 44 wifi access points, 112 cameras, 252 parking sensors and other environmental monitoring systems.
The smart city dashboard data is displayed here
https://smart.darwin.nt.gov.au/dashboards/overview.jsp?_gl=1*1h48nw*_ga*NjkxNDQyODQxLjE2NzE1MDA4NDQ.*_ga_1LHNM1YVHP*MTY3MTUwMDg0My4xLjAuMTY3MTUwMDg0My42MC4wLjA.
and allows citizens to understand their environment better and therefore make more informed decisions while in the city.
Suppose the council are going to install such devices near you, so that your suburb can have its own local smart data dashboard. You have been asked to lead a team which will initially perform analytics on the data over a few months in order to see that the devices are optimally placed and everything is working properly. Following that, you will decide on additional data to be collected and the devices to be used. Lastly, you will work with analysts so that the final data set can be displayed in the form of a local smart data dashboard.
The main aims of the project are
• Perform analytics on the available data for accuracy
• Analyse and advise on device location with the view to installing more devices if not optimal, and decide on the final data set to be obtained, analysed and displayed
• Evaluate the smart dashboard
• Work with a team to implement the smart dashboard
Source: https://www.darwin.nt.gov.au/transformingdarwin/innovation/smartdarwin/smartdarwin dashboard
Case study Instructions
With the CRSIP-DM overall methodology in mind, answer the following questions
1. In a short paragraph, introduce the project and define four business questions, in your own
words, that you could ask in relation to this project.
2. Set two relevant KPI’s for the project. These should be related to the business questions,
and may be concerned with timelines, data collection or analytics methods.
3. Describe the specific data to be used for the project.
4. Write a paragraph describing general features of the waterfall project methodology and how
it would be applied to this project.
5. Write a paragraph describing how one of the newer Agile/Hybrid project methodologies
(Scrum, Kanban,etc.) could be applied to this project.
6. Explain how you would evaluate and deploy (implement) the project.
7. Describe possible issues you may encounter as analytics meets project management.
8. Find at least five supporting references for your report. List the references in Harvard format.
Answers should be in the context of CRISP-DM. Structure your work as a report.
Solution
Introduction
The “Switching on Darwin” project is a smart city initiative that aims to improve the quality of life of citizens by leveraging data collected from smart devices installed throughout the city. The project involves the installation of various devices such as smart streetlights, wifi access points, cameras, parking sensors, and environmental monitoring systems. University Assignment Help, The data collected from these devices is used to provide citizens with a better understanding of their environment, helping them make more informed decisions while in the city (Darwin City Council).
In order to ensure the success of the project, a team has been assembled to perform analytics on the data collected from the devices (Bughin, et.al. 2018). The team will initially focus on optimizing the placement of the devices and ensuring that everything is working properly. They will then decide on additional data to be collected and the devices to be used, and work with analysts to develop a local smart data dashboard.
Four potential business questions that could be asked in relation to this project are:
• What are the most important metrics to measure in order to determine the success of the “Switching on Darwin” project?
• How can we optimize the placement of the smart devices to ensure maximum efficiency and accuracy in the data collected?
• What additional data could be collected to provide citizens with even more insights into their environment and improve their quality of life?
• How can we ensure the data collected from the smart devices is secure and protected from potential cyber threats?
KPIs for the "Switching on Darwin" project
• Time to resolution for device maintenance issues: This KPI would measure the average time it takes for the team to resolve any maintenance issues with the smart devices. This is important because any downtime of the devices could lead to gaps in the data collection process, which would negatively impact the accuracy and effectiveness of the smart city dashboard (Kim, & Kim, 2019).
• Percentage increase in citizen engagement with the smart city dashboard: This KPI would measure the percentage increase in the number of citizens accessing and using the smart city dashboard over a set period of time (Solís, et.al. 2019). This is important because increased citizen engagement with the dashboard would indicate that the data being collected and presented is useful and relevant to them, and that the project is having a positive impact on the community.
Data Used
The "Switching on Darwin" project collects data from various smart devices installed throughout the city, including 684 LED smart streetlights, 44 wifi access points, 112 cameras, and 252 parking sensors (Darwin City Council). The data collected from these devices is used to gain insights into various aspects of the city, including traffic flow, parking availability, air quality, and public safety.
The specific data collected from the smart devices includes:
• Traffic data: This includes data on traffic flow, congestion, and travel times. It is collected from the smart streetlights and parking sensors.
• Parking data: This includes data on parking availability, occupancy, and turnover. It is collected from the parking sensors.
• Environmental data: This includes data on air quality, temperature, and humidity. It is collected from environmental monitoring systems.
• Public safety data: This includes data on incidents such as accidents, crimes, and fires. It is collected from cameras and other sensors.
In addition to these specific types of data, the project may collect additional data in the future based on the insights gained from the initial data analysis. For example, if the data analysis reveals a high level of noise pollution in certain areas of the city, additional sensors could be installed to collect more detailed data on noise levels (Solís, et.al. 2019). Similarly, if the data analysis reveals a high level of pedestrian traffic in certain areas of the city, additional sensors could be installed to collect more detailed data on foot traffic.
Application of Waterfall Project Methodology
The waterfall project methodology is a linear, sequential approach to project management that consists of distinct phases, each of which must be completed before the next can begin (Project Management Institute. 2017). In the context of the CRISP-DM methodology, the waterfall approach would be applied to the data analysis phase of the project. The general features of the waterfall methodology include:
• Requirement analysis: In this phase, the project team would define the requirements of the project, including the data to be analyzed, the scope of the analysis, and the desired outcomes.
• Design: In this phase, the project team would design the analytics methods to be used to analyze the data. This would include selecting the appropriate statistical techniques and tools for the analysis.
• Implementation: In this phase, the project team would implement the analytics methods and apply them to the data. This would involve programming and testing the algorithms to ensure they are working correctly.
• Testing: In this phase, the project team would test the analytics methods to ensure they are producing accurate results. This would involve comparing the results of the analysis to known sources of information to validate the accuracy of the methods.
• Deployment: In this phase, the project team would deploy the analytics methods and integrate them into the local smart data dashboard.
The waterfall methodology is well-suited to projects where the requirements are well-defined and the outcomes are clear. In the case of the "Switching on Darwin" project, the requirements are well-defined and the desired outcomes are clear: to optimize the placement of the smart devices, ensure the data collected is accurate, and develop a local smart data dashboard.
However, one potential drawback of the waterfall methodology is that it can be inflexible and may not allow for changes to be made once a phase has been completed. In a project like this, where the data being analyzed may change or additional data may need to be collected, this inflexibility could be a problem (Project Management Institute. 2017).
Application of Agile/Hybrid Project Methodologies
One of the newer Agile/Hybrid project methodologies that could be applied to the "Switching on Darwin" project is Scrum. Scrum is a flexible, iterative approach to project management that focuses on delivering small, incremental improvements over time (Majeed, Ramayah, & Arshad, 2019). In the context of CRISP-DM, Scrum could be applied to the data analysis phase of the project.
In Scrum, the project is divided into a series of sprints, each of which lasts between one and four weeks. At the beginning of each sprint, the project team would define a set of goals for the sprint, based on the business questions that have been identified for the project (Project Management Institute. 2017). The team would then work together to accomplish those goals over the course of the sprint.
During each sprint, the project team would hold daily stand-up meetings to review progress, identify any obstacles that need to be overcome, and make any necessary adjustments to the plan (Palvia, & Palvia, 2018). This would allow the team to be flexible and responsive to changes in the data being analyzed or the project requirements.
At the end of each sprint, the project team would review the results of the analysis and make any necessary adjustments to the plan for the next sprint. This would allow the team to continually refine their approach to the analysis based on the results they are seeing.
One advantage of Scrum is that it is highly flexible and can accommodate changes to the project plan as needed ((Palvia, & Palvia, 2018). This would be particularly useful in a project like "Switching on Darwin" where the data being analyzed may change over time or new data sources may become available. Another advantage is that Scrum encourages collaboration and communication among team members, which can lead to more effective problem-solving and decision-making.
Scrum requires a high degree of collaboration and communication among team members, which may be challenging if team members are working remotely or are in different time zones.
Evaluation and Deployment
In the context of CRISP-DM, evaluating and deploying the "Switching on Darwin" project would involve the following steps:
• Evaluation: The project team would evaluate the results of the data analysis phase to ensure that the devices are optimally placed and everything is working properly. This would involve reviewing the KPIs established earlier in the project to determine if they have been met. If the results are satisfactory, the project team would proceed to the deployment phase. If not, the team would need to revise the project plan and conduct additional analysis as needed.
• Deployment: Once the data analysis phase is complete and the results have been evaluated, the project team would work with analysts to develop the local smart data dashboard. This would involve selecting the appropriate data visualization tools and designing the dashboard to meet the needs of the local community.
• Implementation: After the dashboard has been developed, the team would deploy the devices to collect the additional data needed to populate the dashboard. This would involve installing the devices in the appropriate locations and ensuring that they are functioning properly.
• Monitoring: Once the project has been deployed, the project team would monitor the dashboard to ensure that it is functioning properly and that the data being collected is accurate and relevant. This would involve ongoing data analysis and dashboard maintenance to ensure that the information displayed is up-to-date and accurate.
Conclusion
When analytics meets project management in the context of CRISP-DM, there are several possible issues that may arise, including:
• Scope creep: The project team may encounter scope creep, which occurs when the project's objectives or deliverables expand beyond the initial scope. This can happen when new data sources are identified or stakeholders request additional features for the dashboard.
• Data quality: Data quality can be a challenge when working with large data sets collected from various sources. Poor data quality can lead to inaccurate or incomplete analysis, which can impact the validity of the project's findings.
• Resource constraints: The project team may face resource constraints, such as a lack of funding or limited availability of skilled personnel. This can impact the project timeline and deliverables.
References
Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., & Henke, N. (2018). Artificial intelligence: The next digital frontier? McKinsey Global Institute.
Darwin City Council. (n.d.). Smart city dashboard. https://smart.darwin.nt.gov.au/dashboards/overview.jsp
Kim, W., & Kim, H. (2019). A review of project management performance measurement systems for smart city projects. Sustainability, 11(18), 5050.
Majeed, S., Ramayah, T., & Arshad, M. F. (2019). Challenges of implementing agile project management in organizations: A systematic literature review. International Journal of Innovation, Creativity and Change, 6(2), 33-53.
Palvia, P., & Palvia, S. (2018). Agile project management for big data analytics projects: An exploratory study. Journal of Database Management, 29(4), 1-16.
Project Management Institute. (2017). A guide to the project management body of knowledge (PMBOK® Guide) (6th ed.). Project Management Institute.
Solís, O., Astudillo, H., Gómez, J., & Cerón, J. D. (2019). An evaluation model for smart city projects using the balanced scorecard. International Journal of Advanced Computer Science and Applications, 10(2), 74-80.
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