The Dashboard of Good Times

This piece was inspired by a moment in Raj Shamani 's podcast, where he touched on the rise and crash of India’s most flamboyant business empire. It made me wonder: What if someone had simply built the right dashboards in time?

Vijay Mallya wasn’t just a businessman — he was a brand. From United Breweries to Kingfisher Airlines, he built empires wrapped in lifestyle, luxury, and spectacle.

But underneath all that glitz, the data was trying to talk.

“The analysis above is based on public data, market reports, and modeled estimations for illustrative and educational purposes only. No proprietary or confidential data has been used.”

The Data Story

Below are four analytical lenses that could’ve changed the course of the Mallya empire — had they been built, seen, and acted upon.

1. Bubble Chart: Margin vs Volume vs Marketing Spend

Each product SKU mapped by:

• X-axis: Units sold
• Y-axis: Margin
• Bubble size: Ad spend

Goal: Show how some products were all hype, no profit.

What it revealed: Heavily marketed products with low volume and low margin — hidden cost centers draining profitability.

Data lesson: Portfolio optimization using simple clustering could’ve reallocated spend and saved cash fast.

🧠 Layman Insight:

One SKU (Storm) had low sales, low profit, and still burned a high marketing budget. Meanwhile, local brews quietly made money with little or no ads. That's a clear signal for portfolio correction.

2. Capital Diversion Heatmap (2005–2012)

Brewing profits silently siphoned into aviation losses year over year.

What it revealed: Kingfisher Airlines’ capital intake ballooned from ₹50 Cr to ₹700 Cr while brewery investments stagnated.

Data lesson: A real-time capital tracker with variance alerts would’ve red-flagged the bleed before it turned fatal.

🧠 Layman Insight:

By 2012, almost every rupee earned from beer was lost to flights. That’s like running a profitable shop and funding a burning factory next door — in silence.

3. Sentiment vs Share Price (2008–2016)

Tracking media tone alongside market price.

What it revealed: Sentiment nosedived before the stock crashed. Market reaction lagged behind public confidence.

Data lesson: A simple NLP engine on press coverage could’ve informed a PR pivot — or at least a damage-control strategy.

🧠 Layman Insight:

Sentiment went negative in 2012 — a full 2 years before the stock price hit rock bottom. If they’d been tracking media tone, they could’ve acted before investors ran.

4. Regional Distributor Churn (2012–2016)

Loss of retail partners by geography.

What it revealed: Southern and Western India saw sharp distributor exits — early cracks in the ground network.

Data lesson: Churn models using engagement, receivables, and order volumes could’ve forecasted risk and allowed pre-emptive retention action.

🧠 Layman Insight:

Half the partners in the South and West quit. That’s not just numbers — it’s trust breaking, brand fading, and beer disappearing from shelves before headlines hit.

🧭 The Bigger Lesson

We often romanticize business collapses as “inevitable” or “doomed from the start.” But in reality, the signs were there — scattered across spreadsheets, reports, and forgotten dashboards.

Analytics isn’t just about hindsight. It’s about hearing what the numbers are trying to say before it’s too late.

And that’s what Data & Drama is all about.

✍️ Author Note

This series is my attempt to blend data science with strategic storytelling, inspired by brands, leaders, and moments that shaped modern business.

Thanks to Raj Shamani for unknowingly triggering this first case study with your podcast reflections on legacy, ego, and vision.