**The most surprising finding is how lopsided the funding totals are, and that one headline deal may be a data error.** **What the evidence shows** - Only 2,065 of the 3,044 rows (about 68%) have a usable `Amount in USD`. The other 979 are missing or unparseable (step-15). - Among the valued deals, the **median is about $1.7M** but the **mean is about $18.4M**, roughly 11 times higher. The total is about **$38.06B** (step-15). - **73 deals of $100M or more** (about 3.5% of valued deals) account for **63.6% of all dollars** (steps 10, 12, 15). - The single largest record is **Rapido Bike Taxi, a "Series B" on 27/08/2019, at $3.9B**. That is **10.2% of all dollars in the dataset** (steps 0 and 15). - A $3.9B Series B for a bike-taxi startup is implausible next to Flipkart's $2.5B private equity round (11/08/2017) and Paytm's $1.4B (18/05/2017) (step-0). I could not verify it, but it looks like a unit or entry error. - If it is an error, one bad cell is moving the headline total by about a tenth. **Why this matters** - The typical deal is a small cheque of under $2M, yet the dollar totals are driven by a few dozen mega-rounds, possibly one erroneous one. - Any ranking of industries, cities or years by total dollars will mostly reflect which mega-deals fall in each group. Medians or deal counts are more representative of typical activity. - Flipkart appears under two names ("Flipkart" and "Flipkart.com", step-0). Name variants like this may split or hide repeat recipients in rankings. **What this leaves open** - I did not verify the Rapido amount against any outside source, and I did not check whether the other very large rows are real. - About a third of rows have no amount, so I can't say whether the missing deals are mostly small or large. The 63.6% share applies only to the deals that have amounts. - One row came back as a non-finite value (step-8). Some of my queries disagreed by one row (2,065 vs 2,066 valued) because of how that row was parsed. The totals above come from the runs that excluded it. - I did not break the skew down by industry, city or year, so I can't say whether it holds uniformly.