Yes. The most natural segmentation is by **funding stage**, with **sector** and **location** as secondary cuts. All three need label cleanup first. **1. Announcement month (built into the structure)** - The data is split into five tables, one per month from January to May 2022. The card gives their sizes: 115, 96, 98, 95 and 42 rows. - Each row is one funding round. May is much smaller, and the card notes some of its rows are misaligned or blank. - The tables share no join key, so month-level comparisons have to be done table by table and then stacked. **2. Funding stage (the strongest categorical segment)** - Across the tables, a handful of stages dominate: Seed (73), Series A (56), Pre-series A (37) and Series B (26). Series C, D, E and F follow with 11–19 each, and later rounds (G, H, I, J) have only a few rows each. - The stage column is messy, so these counts need cleaning before use: - 127 records have a blank stage and 37 are "undisclosed", which together are the largest groups. - Spelling variants of the same stage appear: "Pre-seed" (8) vs "Preseed" (2), "Series D" vs "Series-D", "Undiclosed" vs "undisclosed", and "$Series A" vs "Series A". - Some values are not stages at all, for example "Working Capital to SMEs" and "Blue Ashva Capital, Supack Industries". The latter is an investor name in the wrong column, which fits the misalignment noted for the May table. - A sensible grouping is early stage (Pre-seed, Seed, Pre-series A, Series A), growth (Series B–D), late (Series E and beyond), and unknown/undisclosed. **3. Sector (useful but needs consolidation)** - Financial Services leads with 59 records, plus 13 more under "Financial services", which differs only in capitalisation. Information Technology & Services (27), Computer Software (26) and Software (10) overlap heavily. - Other common sectors are Health, Wellness & Fitness (15), E-learning (12), E-commerce (12) and Food & Beverages (11). - 19 records have a blank sector. The card shows about 55 distinct sectors in March and about 53 in April, so rolling these up into 8–10 broader themes (fintech, software/IT, health, edtech, commerce, food) would work better than using the raw values. **4. Location (geographic segment)** - It is dominated by Bangalore (124, plus 10 as "Bengaluru", which should be merged), Mumbai (62), Gurgaon (48), New Delhi (31), Chennai (27), Noida (15), Hyderabad (14) and Pune (12). - 20 records have a blank location. A "top metro vs. other" grouping would be natural. **5. Funding amount (a measure, not a segment)** - The `Amount` column is text in every table, so it would need cleaning and casting before you could bucket rounds by size. **Recommendation:** segment first by cleaned stage group, then by consolidated sector or city, and keep month as a time dimension. Standardise the label variants and handle the blank and misaligned rows before you rely on any group counts.