### 3.1 Quality Grade The Iris table earns an **A (Excellent)** with a 100% score, driven by 100% completeness and 100% uniqueness across its 150 rows. Referential integrity is structurally inapplicable in a single-table dataset, so this assessment rests on completeness and uniqueness. Both are perfect, which means analyses built on Iris will not be skewed by missing values or duplicated records. ### 3.2 Key Risk Areas | Risk | Severity | Table | |---|---|---| | No significant risks detected | None | Iris | With no risks in the evidence, nothing blocks use of Iris for reporting or machine learning today. The remaining considerations are about scope rather than defects. A perfect score describes cleanliness, not representativeness or depth, and a 150-row table limits how far conclusions can be generalized. Any model or report should be framed with that limit in mind. ### 3.3 Remediation Priorities No repairs are required. The recommendations below protect the current quality level and extend the table's usefulness: - **Lock in the baseline on Iris.** Record the current 100% completeness and uniqueness as the benchmark, and re-check them whenever rows are added or the table is reloaded, so any future degradation is caught immediately. - **Document the meaning and units of each Iris column.** Clear definitions let business users interpret measurements consistently and prevent misreading when the data is shared beyond its original audience. - **Confirm sample adequacy before relying on Iris for predictive work.** At 150 rows, results suit demonstration, benchmarking, or prototyping. Validate against a larger or independent sample before using them for consequential decisions. - **Plan schema evolution through enrichment, not repair.** If the business needs richer analysis, add descriptive attributes or new observations to Iris directly, and apply the same completeness and uniqueness checks to anything new.