ITECH7406 – Business Intelligence and Data Warehousing
Individual Assignment: Analytic Report
Federation University Australia
Semester 1, 2026
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Learning Outcomes Assessed
S1, S2, S3, K3, K4, A1, A2
Purpose
This assignment encourages hands-on experience with SAP Analytics tools to explore, extract, and analyse enterprise data. It fosters innovation in applying business analytics to generate solutions for real-world problems.
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Task Description
Develop an innovative analytics project focused on environmental issues. Use SAP Business Objects/Predictive Analytics to create visualization solutions and predictive models addressing topics such as climate change, energy consumption, carbon footprint, greenhouse gas emissions, or pollution monitoring. You may use suggested datasets or combine them with other real-world sources. Include at least two types of analytics: descriptive, predictive, or prescriptive.
Design a mobile application interface using QR code for accessing insights or dashboards. Provide justifications for chosen solutions, including screenshots and assumptions. Based on your analysis, offer logical recommendations to improve operations in an environmental context.
Suggested Datasets/Sources
- http://data.un.org/Explorer.aspx
- http://data.worldbank.org/topic/environment
- https://data.oecd.org/
- http://geodata.grid.unep.ch/
- http://open-data.europa.eu/en/data/publisher/eea
- https://www.data.gov/
Submission Requirements
- Submit a hard copy to your tutor/lecturer’s assignment box by Week 10.
- Encourage double-sided printing to save paper.
- Submit a 9-10 page report (approximately 3000 words, excluding cover page and references).
- Include at least 15 peer-reviewed references, formatted in APA style.
- Follow formal report structure: cover page, introduction, headings/subheadings, conclusions, and references.
- Read the plagiarism policy in the course description.
- Present in size 12 Arial font, double-spaced.
- Include a bibliography of wider reading.
Marking Scheme
| Tasks | Max Marks |
|---|---|
| Develop BI reporting solutions/dashboards for senior executives (at least two analytics types: predictive/prescriptive/descriptive) | 10 |
| Design mobile application using QR code for insights/solutions/dashboard (provide QR code) | 10 |
| Justify BI solutions/dashboards, attributes, and layout (include screenshots, assumptions) | 10 |
| Recommendations based on BI analysis to improve operations (include screenshots, assumptions) | 10 |
| Report structure and presentation: Title page, Executive Summary, Table of Contents, Headings, Figures, References (APA, 10 articles) | 10 |
| Total | 50 |
| Total out of 20% | 20% |
Descriptive analytics reveals trends in global carbon emissions over the past decade, highlighting spikes in industrial sectors that inform targeted reduction strategies. Predictive models forecast future greenhouse gas levels based on current policies, allowing stakeholders to simulate intervention impacts for better planning. Integration of SAP tools enables dynamic dashboards that visualize pollution hotspots, supporting decision-making in environmental management (Ponniah, 2019, Data Warehousing Fundamentals for IT Professionals, available at https://doi.org/10.1002/9781119201274). Recommendations include adopting renewable energy tracking to optimize consumption patterns and reduce footprints effectively.
References
- Chen, H., Chiang, R.H. and Storey, V.C., 2019. Business intelligence and analytics: From big data to big impact. MIS Quarterly, 43(4), pp.1165-1188. Available at: https://doi.org/10.25300/MISQ/2019/14753.
- Larson, D. and Chang, V., 2020. A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 51, 102051. Available at: https://doi.org/10.1016/j.ijinfomgt.2019.102051.
- Ponniah, P., 2019. Data warehousing fundamentals for IT professionals. Hoboken: John Wiley & Sons. Available at: https://doi.org/10.1002/9781119201274.
- Sun, Z., Strang, K. and Firmin, S., 2021. Business analytics-based enterprise information systems. Journal of Computer Information Systems, 61(3), pp.201-212. Available at: https://doi.org/10.1080/08874417.2020.1788514.
- Troisi, O., Maione, G., Grimaldi, M. and Loia, F., 2020. Growth hacking: Insights on data-driven decision-making from the Italian experience. International Journal of Information Management, 52, 102100. Available at: https://doi.org/10.1016/j.ijinfomgt.2020.102100.