Assignment - Business Intelligence Solution Development and Report for Social Enterprise
BI Solution Development and Report
In the BI practicals, we saw how the Watson Analytics tools interact with data sources to produce BI dashboards for managers. This practical assignment requires you to demonstrate your understanding and apply IBM Watson Analytics tool.
This group assignment topic will be on environmental issues. As the co-founders of a social enterprise, your main task is to apply Watson Analytics and develop innovative analytics solution with regards to environment, e.g. climate change, energy consumption, carbon footprint, greenhouse gas emission, air quality dashboard, etc. Besides the suggested datasets/sources, you may apply any other real-world or artificial dataset to illustrate your approach (the different datasets can be combined too). (Note: "a social enterprise is an organisation that applies commercial strategies to maximize improvements in human and environmental well-being", source: https://en.wikipedia.org/wiki/Social_enterprise)
Some possible 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/
Tasks:
1. Develop the relevant BI dashboards, predictive models and social media analytics using IBM Watson Analytics tool.
2. Justify why these BI solutions could provide insights and why those attributes are laid out in the fashion you proposed (feel free to include all other relevant justifications).
Note: To ensure that you discuss task (2) properly, you must include visual samples of the reports you produce (i.e. the screenshots of the BI report/dashboard must be presented and explained in the written report; use 'Snipping tool'), and also include any assumptions that you may have made.
3. The BI report should include: 1) BI dashboards screenshots and commentary, 2) predictive models, 3) social media findings, 4) overall findings and recommendations, 5) objectives and benefits of your BI solution, etc. Feel free to make reasonable assumptions or refine the data as needed. You may create your own artificial datasets. Real-world dataset is not compulsory.