### 3.1 Quality Grade The dataset earns an **A (Excellent)** with a 100% score, and completeness is 100% across all five monthly tables (446 rows in total, January–May 2022). Leaders can rely on these tables as fully populated records of funding activity, with no gaps that would distort totals or counts. Uniqueness was not scored (N/A), so a clean grade does not by itself confirm the absence of duplicate funding entries. ### 3.2 Key Risk Areas | Risk | Severity | Tables Affected | |---|---|---| | No significant risks detected | None | All five tables | No risks block reporting or ML use today. The main caveat is structural, not a defect. The five tables are separate monthly snapshots with no validated joins, so the 100% referential integrity score reflects the absence of relationships rather than verified linkage. Any analysis spanning January to May has to combine the monthly tables deliberately, and the unscored uniqueness check leaves duplicate-entry risk unverified, particularly if a startup appears in more than one month. ### 3.3 Remediation Priorities - **Verify uniqueness in all five monthly tables** (Indian Startups _ Funding Investors Data January 2022 through May 2022): confirm that no funding round is recorded twice. This is the only dimension not yet scored, so it is the most valuable check before figures are quoted to stakeholders. - **Check for repeat startups across months:** the same company may legitimately raise funding in more than one month, so separate real follow-on rounds from accidental repeats before totalling deal counts. - **Standardise the monthly layout:** confirm that column names and formats match across the five tables so they can be stacked into a single view. The January–May comparison then becomes dependable, and later months can be added without rework. - **Account for volume differences when comparing months:** May 2022 holds only 42 rows against 115 for January 2022. Confirm whether that reflects genuinely slower activity or a partial month of data before drawing trend conclusions. - **Retain the current quality checks as new months arrive,** so the 100% completeness standard holds as the dataset grows.