Yes. The records fall into a few natural groupings, but the labels need cleaning first. **1. Funding stage is the strongest segmentation.** - Stage is the main classifier in every monthly table. - Seed (73 rows), Series A (56), Pre-series A (37) and Series B (26) are the largest named stages. Series C, D, E and F follow with 19, 14, 11 and 12 rows. - The stages form an ordered lifecycle: Pre-seed/Seed, then Series A to J. You can group them into early (Pre-seed, Seed, Pre-series A), growth (Series A–B) and late (Series C and beyond). - The biggest bucket is blank, with 127 rows. Another 37 are "undisclosed" and 2 are "Unknown", so roughly 166 rows have no usable stage. Keep these as an explicit "unknown" segment. **2. Sector and location are useful secondary segments.** - 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). Twenty rows are blank. - Sector is dominated by Financial Services (59), followed by Information Technology & Services (27), Computer Software (26), Health, Wellness & Fitness (15), E-learning (12), E-commerce (12) and Food & Beverages (11). Nineteen rows are blank. - Sector has a long tail of roughly 40–55 distinct values per month, so it works best rolled up into broader themes such as fintech, software/IT, health, education/e-commerce and food. **3. Announcement month is a structural segment.** - The data is split into five tables, January to May 2022, with 115, 96, 98, 95 and 42 rows. Each table is one month's funding announcements, so you can treat month as a time dimension. - May is smaller and has some misaligned or blank rows. **Cleanup needed before grouping** - **Stage:** "Preseed" and "Pre-seed", "Series-D" and "Series D", "$Series A" and "Series A", and "Undiclosed" and "undisclosed" are the same values spelled differently. - **Stage:** some entries are not stages at all, such as "Blue Ashva Capital, Supack Industries" (an investor name that landed in the Stage column) and "Working Capital to SMEs". These are likely misaligned rows. - **Location:** "Bangalore" (124) and "Bengaluru" (10) are the same city. - **Sector:** "Financial Services" (59) and "Financial services" (13) differ only in case, and "Software" and "Computer Software" overlap. **Recommendation:** segment first by normalized funding stage, grouped into early, growth, late and unknown. Then cross it with consolidated sector and city, and use month as the time axis. Stage is the most meaningful and least sparse dimension, though about 37% of its values are blank or undisclosed. The stage counts above come from the stage breakdown query, and the location and sector counts from the location/sector breakdown query.