**Yes. Stage of funding is the most natural way to group these records, with month, location and sector as secondary cuts.** Each row is one funding round for one company, announced between January and May 2022. **1. Funding stage (primary segmentation)** - Stage has a clear maturity order: Pre-seed/Seed, then Series A, B, C and on to later rounds. That makes it the most meaningful grouping for startup funding. - Across the five months, the biggest named groups are Seed (73), Series A (56), Pre-series A (37), Series B (26), Series C (19), Series D (14), Series F (12) and Series E (11). The distribution is top-heavy toward early stages. - Two large groups carry no usable stage: 127 rows are blank and 37 are "undisclosed". Together that is roughly 37% of the 446 rows, so you would want an explicit "Unknown stage" segment. - Stage needs cleaning before use. There are variants such as "Preseed" and "Pre-seed", "Series-D" and "Series D", "$Series A", "Undiclosed", and "Series B1" and "Series F2". - A few values are clearly misplaced, such as "Blue Ashva Capital, Supack Industries" (an investor name) and "Working Capital to SMEs". These are probably misaligned rows, which the card flags for May. - A practical rollup would be Pre-seed/Seed, Series A (including Pre-series A), Series B–C, Series D and later, and Unknown. **2. Announcement month (natural time partition)** - The data is already split into five tables: January (115 rows), February (96), March (98), April (95) and May (42). - Use this for trend views. May is much smaller and has some blank or misaligned rows, so treat it as a partial month. **3. Geography** - Location is concentrated in a few cities: Bangalore (124), Mumbai (62), Gurgaon (48), New Delhi (31), Chennai (27), Noida (15), Hyderabad (14) and Pune (12). - "Bengaluru" (10) is the same city as Bangalore and should be merged. 20 rows have no location. - A sensible grouping is the top 5–6 metros plus "Other/Unknown". **4. Sector** - Sector has about 40–55 distinct values per month, so it is too fragmented to use as-is. - The largest groups are Financial Services (59, plus 13 under the variant "Financial services"), Information Technology & Services (27), Computer Software (26, plus "Software" at 10), Health, Wellness & Fitness (15), E-learning (12), E-commerce (12) and Food & Beverages (11). 19 rows are blank. - It needs normalisation and rolling up into broader themes such as fintech, software/IT, health, consumer and edtech. **Recommendation:** segment first by cleaned funding stage, then cross it with city and a rolled-up sector. Use month for time comparison. Amount is stored as text, so it would need parsing before you could segment by deal size.