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BMW Sales and Model Insights
1. Which dataset did you use?
BMW Global Sales Dataset (2010-2024) available at Kaggle. https://www.kaggle.com/datasets/ayeshaseherr/bmw-dataset
2. How did you analyze or prepare the data?
I used Auto-Insights features available in OAC.
3. Who is the intended audience for your visualization?
Automobile Sales manager.
4. What is your visualization about, and what question or problem does it address?
This visualization provides a comprehensive overview of BMW’s sales performance, customer preferences, and model trends across different regions and years. Manager can analyze overall sales volume and revenue, identify top-performing models and regions, and track year-over-year growth. The visualization also highlights evolving customer preferences such as fuel type adoption, transmission trends, popular vehicle colors. Regional comparisons and model-level analysis make it easy to identify market strengths, emerging trends, and opportunities for growth. Regional comparisons and model-level analysis make it easy to identify market strengths, emerging trends, and opportunities for growth.
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 asked few questions to the AI Assistant, example giving me the top 10 Models by sales and I used the same for one of the visual.
6. Did you upload your visualization image and dva file?
Completed!
Comments
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- Excellent visual storytelling: The layout reads like a narrative—KPIs → trends → breakdowns—so the viewer understands the business story quickly.
- Smart KPI selection & hierarchy: The top strip highlights the right executive metrics (volume, revenue, best model, region, fuel, color, ASP) with clear prioritization.
- Strong dashboard composition: Balanced spacing, alignment, and sectioning makes a lot of information feel organized rather than overwhelming.
- Good use of interlocking visuals: Trend lines, regional view, distribution chart, and category comparisons complement each other instead of repeating the same insight.
- Consistent branding & theme: The BMW look-and-feel (colors, imagery, header) is coherent and professional—feels “production ready.”
- Attention to readability: Labels, titles, and panel separation are clear, helping non-technical audiences interpret the charts quickly.
- Business-first thinking: The dashboard answers practical questions (What’s selling? Where? Which fuel/color? Price movement?)—it’s built for decisions, not just aesthetics.
- Polished finishing touches: The disclaimer/data context and clean headline framing show maturity and presentation discipline.
“This dashboard reflects a strong BI mindset—clear KPI hierarchy, cohesive visual design, and insightful chart choices that turn data into an executive-ready story.” ~ Rishi Gupta
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I like it! A little tweak would be to have the header picture in the right proportion or resolution. But overall good work! Thank you for participating to the Analytics and AI Challenge.
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Beautiful presentation and the viz.
Thanks for sharing @ManikSethi!
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@ManikSethi , very impressive, nice going. Keep it up!
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