### 3.1 Quality Grade The **C (Moderate)** grade reflects a dataset that is structurally complete but exhibits significant duplication challenges. While both `ittf_rankings` and `ittf_rankings_women` achieve 100% data completeness—meaning no missing values in critical fields—the 3% uniqueness score reveals substantial redundancy across the 473,582 combined records. This grade signals that the data is usable for immediate reporting needs but requires deduplication work before supporting advanced analytics or machine learning models that assume distinct observations. Business leaders can trust the data's presence and accuracy but should exercise caution when calculating aggregate metrics like total player counts or unique ranking movements, where duplicates will inflate results. ### 3.2 Key Risk Areas | Risk Level | Issue | Impact | |------------|-------|--------| | — | No significant risks detected | — | Despite the absence of critical structural risks, the low uniqueness score warrants attention. The 3% uniqueness metric suggests that approximately 97% of records may be duplicates or near-duplicates, likely representing repeated snapshots of player rankings over time or across tournament categories. Before using this data for trend analysis, player performance benchmarking, or historical comparisons, teams must establish clear deduplication rules—such as retaining only the most recent ranking per player or aggregating by time period—to avoid double-counting achievements and distorting competitive insights. ### 3.3 Remediation Priorities - **Establish temporal deduplication logic for `ittf_rankings` and `ittf_rankings_women`**: Define business rules to identify the "current" or "authoritative" record for each player, likely based on date fields or ranking period identifiers, to eliminate redundant snapshots that skew player counts and performance metrics. - **Implement unique player identifiers**: If not already present, introduce or validate a consistent player ID across both tables to enable accurate tracking of individual athletes' ranking trajectories and prevent the same player from being counted multiple times in cross-gender or historical analyses. - **Document the grain of each table**: Clarify whether records represent daily snapshots, tournament-specific rankings, or point-in-time captures, then communicate this definition to all downstream consumers to prevent misinterpretation of duplicate-looking records that may actually reflect legitimate temporal or categorical variations. - **Create aggregated summary tables**: Build pre-deduplicated views that roll up rankings to monthly or quarterly snapshots, reducing the risk of accidental duplication in executive dashboards while preserving the granular history for specialized analysis.