Global Crude Oil Trade Dashboard

Srikanth Reddy Annaram

1. Which dataset did you use?

CrudeOil.dva

I used a global crude oil trade dataset that includes export and import countries, trade routes, crude volume (in MBbl), and product breakdown such as petrol, diesel, jet fuel, LPG, and others. It also contains production, consumption, and reserves data for exporting countries. To enhance visualization, I generated additional latitude and longitude data for mapping trade routes. This dataset provides a complete view of global oil movement and distribution.

2. How did you analyze or prepare the data?

I cleaned and structured the dataset by ensuring consistency in country names and validating key metrics like volume and reserves. I enriched the data by generating route-level latitude and longitude points to visualize trade flows on the map. Aggregations were applied for KPIs such as total volume, production, and consumption. The data was then modeled to support map, sankey, and composition visuals in Oracle Analytics Cloud.

3. Who is the intended audience for your visualization?

This dashboard is designed for business stakeholders, energy analysts, and decision-makers in the oil and gas industry. It is also useful for supply chain managers and strategy teams to understand global trade patterns. Additionally, it serves as a strong portfolio project for demonstrating data visualization and analytics skills. The insights are presented in a way that both technical and non-technical users can easily understand.

4. What is your visualization about, and what problem does it address?

The visualization provides a comprehensive view of global crude oil trade, from export to import, including routes and product distribution. It helps identify major trade flows, key exporting countries, and how crude oil is refined into different products. The dashboard addresses the challenge of understanding complex global trade movements in a simple and interactive way. It enables users to quickly analyze supply-demand patterns and trade dependencies.

5. Did you use any Oracle Analytics AI features? If so, how?

Yes, I leveraged Oracle Analytics AI features such as automatic insights and smart visual recommendations to enhance the dashboard. These features helped identify key trends and patterns in production, consumption, and trade volumes. I also used built-in suggestions to optimize visual selection and layout. This improved both the analytical depth and user experience of the dashboard.