1. Which dataset did you use?
I used the Superstore Sales dataset (order-level retail data) containing fields such as Order Date, Region/State/City, Segment, Ship Mode, Category/Sub-Category, Sales, Profit, Discount, Quantity, and customer/order identifiers.
2. How did you analyze or prepare the data?
Verified data types (dates for Order Date/Ship Date, geography for State/Region, measures for Sales/Profit/Discount).
Used built-in aggregations (SUM Sales, SUM Profit, AVG Discount, and customer counts).
Added interactive filters (Region, State, Category, Sub-Category) for quick slicing.
Organized the dashboard into key views: KPIs, quarterly profit trend, product drivers (treemap + top/bottom sub-categories), geography (profit by state), mix & contribution (profit by region and segment), and operational signals (discount vs profit, sales by ship mode).
3. Who is the intended audience for your visualization?
Sales leadership, regional managers, category/product managers, and business analysts who need an executive overview of performance and the key drivers of profit and loss.
4. What is your visualization about, and what question or problem does it address?
This visualization provides a business overview of Superstore sales and profitability. It answers: overall performance (sales, profit, margin vs target, customers), profitability trend over time, which sub-categories drive profit vs losses, where performance varies geographically, how profit is distributed by region and segment, how discount relates to profit outcomes, and which ship modes generate the most sales.
5. Did you use any Oracle Analytics AI features when building your visualization (ex. AI Assistant)? If so, please describe how they were used.
Yes. I used Oracle Analytics AI-assisted capabilities (e.g., suggested visualizations/Auto-Insights) to quickly explore patterns and relationships (such as discount vs profit) and to validate key drivers while designing the dashboard layout.
6. Did you upload your visualization image and DVA file?
Yes.
