Yes. The dataset has several natural ways to group records. Stage is the strongest, followed by location and sector, and the five monthly tables give a time cut. **1. Funding stage (best fit).** Stage is the clearest classifier, and I counted it across all five monthly tables (30 distinct raw values, 446 rows). - Early-stage rounds dominate: Seed has 73 records, Series A 56 and Pre-series A 37. - Growth rounds taper off: Series B has 26, C 19, D 14, E 11 and F 12, and only a handful reach G, H, I or J. - A sensible grouping is **Pre-seed/Seed → Series A (including Pre-series A) → Series B–D → Series E and later**. A separate "undisclosed/unknown" bucket would catch 127 blank records plus 37 labelled "undisclosed", along with a few rarer variants (e.g. "Unknown", "Undiclosed"). - Stage needs cleaning before it is grouped. Variants such as "Preseed" and "Pre-seed", "Series-D" and "Series D", and "$Series A" and "Series A" should be merged. A few values aren't stages at all, such as "Working Capital to SMEs" and "Blue Ashva Capital, Supack Industries" (an investor name in the wrong column). That is consistent with the card's note that some May rows are misaligned. **2. Location (geography).** Records are concentrated in a few cities: Bangalore has 124, Mumbai 62, Gurgaon 48, New Delhi 31 and Chennai 27, followed by Noida, Hyderabad and Pune. "Bengaluru" (10) is a spelling variant of Bangalore, and 20 records have no location. Grouping by city, or by metro cluster such as Delhi-NCR (Gurgaon, New Delhi, Noida), works well. **3. Sector (industry).** Financial Services is the largest group with 59 records, followed by Information Technology & Services (27), Computer Software (26), Health, Wellness & Fitness (15), E-learning (12), E-commerce (12) and Food & Beverages (11). Sector has many near-duplicate labels, such as "Financial Services" and "Financial services" (13) or "Computer Software" and "Software". It needs a roll-up into broader categories such as fintech, software/IT, health, edtech and commerce before it is useful as a segment. About 19 records have no sector. **4. Announcement month.** The tables run from January to May 2022: January has 115 rows, February 96, March 98, April 95 and May 42. May is smaller and partly misaligned. Month is a natural cut for time comparisons, though it is only five months. **5. Other possible cuts.** The `Founded` column (company age at funding) and `Amount` (a text column that would need parsing) could give size or age bands. There is no join between the tables, so any segmentation is per table or by stacking them. For most analyses I'd segment by **stage × sector (or city)**, after normalising the label variants above.