### Fit-for-Purpose Verdict **What this dataset supports well:** This is a clean, single-table dataset ideal for **univariate and multivariate analysis within the Iris table**. With 100% completeness across all 150 rows, you can confidently perform column-level profiling, distribution analysis, correlation studies among the five attributes (sepal length, sepal width, petal length, petal width, species), and classification modeling. The absence of nulls eliminates the need for imputation or missing-data handling. Basic descriptive statistics, clustering, and species-level segmentation are all fully supported. **What it cannot support and why:** Any analysis requiring **cross-table aggregation, dimensional modeling, or relational queries** is impossible—the dataset contains only one table. There are no foreign keys, no related entities, and no validated joins. You cannot trace lineage across business processes, reconcile metrics from multiple sources, or build star/snowflake schemas. Referential integrity scores 100% vacuously: there are no constraints to test. If your use case demands understanding relationships between customers and orders, products and sales, or any multi-entity workflow, this dataset provides zero coverage. **Top remediation steps:** 1. **No action required for referential integrity.** The 100% score reflects the absence of foreign key constraints, not their validation. If this dataset will remain single-table, no remediation is needed. 2. **Expand the dataset with related tables** if business questions require relational context (e.g., add tables for experimental conditions, botanical taxonomy, or measurement provenance, then define foreign keys linking them to Iris). 3. **Document the intended scope.** If Iris is a standalone analytical snapshot, formalize that boundary so stakeholders do not expect join-based insights the structure cannot deliver.