Name: Yasharth Sharma

Email Id: yasharth.sharma@oracle.com

1. Which dataset did you use?

Superstore Sales

2. How did you analyze or prepare the data?

In Oracle Analytics Cloud, the dataset was structured by:

• Assigning Sales, Profit, Quantity, and Discount as measures
• Defining Order Date and Ship Date as time dimensions with hierarchies (Year, Quarter, Month)
• Creating geographic hierarchies for Region → State → City
• Ensuring categorical fields such as Category, Sub-Category, Segment, and Ship Mode were modeled as dimensions

The dataset was validated by reconciling totals and spot-checking values against raw data to ensure accuracy.

3. Who is the intended audience for your visualization?

These are designed to support both executive-level decision making (through high-level KPIs and trends) and operational analysis (through detailed drill-downs into products, regions, and customers).

The primary audience for this visualization includes:

• Business Leaders
• Sales Leadership and Regional Managers
• Category and Product Managers

The secondary audience includes:

• Business Analysts
• Finance and Operations teams

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

The visualization provides a comprehensive view of sales performance and profitability across products, regions, and customer segments. It answers key business questions such as:

• How are sales and profits trending over time?
• Which categories and sub-categories are driving growth and which are causing losses?
• Which regions and customer segments are most profitable?
• Are discounts negatively impacting profitability?
• Which products generate high sales but low or negative profit?

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, below

Oracle Analytics Cloud AI features were used to enhance analysis and insight discovery:

• Explain / Auto Insights: Used to automatically surface drivers behind changes in sales and profit, such as identifying specific regions or categories contributing to a decline in profitability during certain periods.
• AI Assistant: Used to quickly explore the data by asking questions such as “Sales by category over year” and “Top 10 products by Sales.” This helped validate trends and identify areas that needed focused visualizations.