1. Which dataset did you use?

I used a comprehensive Uber dataset containing ride-level details such as bookings, cancellations, revenue, distance, ride type (Auto, Bike, Sedan, Go Mini, Premier), timestamps (Month & Quarter), customer and driver ratings, and cancellation reasons. This dataset enabled multi-dimensional analysis across ride performance, revenue trends, and operational efficiency.

2. How did you analyze or prepare the data?

I performed data preparation and transformation within Oracle Analytics Cloud by:

Standardizing measures like Revenue, Distance, Ratings, and Booking Counts into numeric formats

Creating calculated KPIs such as:

Completed Bookings

Lost Bookings

Total Revenue

Average Distance

Customer & Driver Ratings

Implementing parameters to dynamically switch views between Month and Quarter analysis

Designing drill-down functionality using vehicle icons (Auto, Bike, Sedan, Go Mini, Premier) for intuitive navigation

Structuring data to support trend analysis (Q1–Q4) and cancellation breakdown insights

Applying filters (Year selection) to make the dashboard interactive and dynamic

3. Who is the intended audience for your visualization?

This dashboard is designed for:

Operations Managers → to monitor ride performance and efficiency

Business Analysts → to analyze trends in bookings, revenue, and cancellations

City/Regional Heads → to track performance across time and ride types

Decision-Makers → to optimize pricing, driver allocation, and customer experience

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

This dashboard provides a complete analytical view of Uber ride operations, focusing on bookings, revenue, cancellations, and customer experience.

It addresses the key business questions:

• “How are ride bookings, revenue, and cancellations trending over time?”
• “Which ride types and periods perform best or worst?”
• “What are the main reasons for ride cancellations?”

By integrating KPIs, trend analysis, and cancellation breakdowns, the dashboard helps:

Identify high and low-performing quarters/months

Understand revenue fluctuations

Detect customer behavior patterns

Analyze cancellation reasons (driver issues, wait time, etc.)

Enable data-driven operational improvements

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

Yes, I used Oracle Analytics AI features to enhance insights:

Generated AI-driven insights on revenue trends and booking patterns

Identified anomalies and key drivers behind lost bookings and cancellations

Used AI suggestions to highlight performance variations across quarters

Refined insights to focus on customer experience and operational efficiency

These AI capabilities helped transform raw data into actionable insights.

6. Did you upload your visualization image and DVA file?

Yes, the dashboard image and DVA file have been successfully uploaded.