I counted distinct values in five categorical columns (Company Name, Location, Sector, Stage, Investor) in each monthly table. The dataset has no single combined table, so the counts are per month, and I did not compute a cross-month total. The rendered result shows the exact counts. | Month | Rows | Company Name | Location | Sector | Stage | Investor | |---|---|---|---|---|---|---| | January | 115 | 114 | 11 | 42 | 12 | 107 | | February | 96 | 96 | 13 | 39 | 11 | 86 | | March | 98 | 98 | 17 | 54 | 11 | 90 | | April | 95 | 94 | 39 | 58 | 18 | 54 | | May | 42 | 24 | 16 | 19 | 6 | 22 | - **Company Name and Investor are nearly unique.** Company Name has almost one distinct value per row: 114 of 115 in January, 96 of 96 in February, 98 of 98 in March and 94 of 95 in April. Investor is also close to unique in January through March (86–107 distinct per month). April's Investor count drops to 54. - **Location has the fewest distinct values**, at 11 to 17 in January through March. April is the exception with 39, which suggests more granular or inconsistently written place names that month. - **Sector is mid-cardinality**, at 39 to 58 distinct values per month. Counts rise from February to April, so sector labels are fragmented rather than drawn from a fixed taxonomy. - **Stage is a small set**, with 6 to 18 values per month. April has the most (18) and May the fewest (6). - **May is distorted by data quality.** It has 42 rows but only 24 distinct company names, and every categorical count there is low. The dataset notes say some May rows are misaligned or blank, so treat May's counts as understated. The same label may be spelled differently across months, so adding these counts together would overstate the true distinct totals.