Claire Barratt, Jake Tuck and Thomas Robson, with academic support from Rich Conniss, entered the competition in the autumn of 2016. They gained 9th place out of 210 registered teams from universities around the world.
The competition was organised by the Watson Analytics Global Academics Network and IBM. The competition was open to undergraduate and taught masters students in teams of three supported by an academic.
The topic was on environmental issues. The main task is to apply Watson Analytics (including data refinement, exploration, predictive model, social media analytics & dashboard functions) and develop innovative analytics solutions with regards to environment, e.g. climate change, energy consumption, carbon footprint-GDP correlation, greenhouse gas emission, air quality-health impact dashboard, etc. Besides the suggested datasets/sources, you may apply any other real-world dataset to illustrate your approach (the different datasets/sources can be combined).
Submission: The submissions should include a report (with dashboard screenshots and commentary, predictive models, social media analytics, recommendations etc, and Watson Analytics login detail) and a 5 to 10 minutes video presentation link of the team’s solution (to be uploaded to YouTube). The presentation document should explain the purpose and benefits of the solution. Feel free to make reasonable assumptions as needed.
Evaluation criteria: Submissions will be judged based on Innovation/ creativity of the solution, Quality of commentary and recommendations, Expected benefits for users and relevant stakeholders, Organization and presentation of the submission.
The assessment criteria were clearly defined across four criteria covering
1. Application Innovation and Creativity (purpose, expected benefits for users and relevant stakeholders, etc): 25%
2. Clarity, ease of understanding, and logical flow of the presentation (with dashboard screenshots and commentary): 25%
3. Full use of Watson Analytics Capabilities (i.e., data refinement, exploration, predictive, social media & dashboard assemble functions): 25%
4. Business value, insights, quality of recommendations, etc : 25%