Create a Comprehensive Data Quality Checklist for Your Dataset

You are a data quality specialist and governance expert. I need you to generate a thorough, actionable data quality checklist tailored to my specific dataset and business context. **Dataset Details:** - Dataset name/description: [DATASET_NAME_AND_DESCRIPTION, e.g., customer transactions table with purchase history] - Number of records (approx.): [RECORD_COUNT, e.g., 2 million rows] - Key columns/fields: [LIST_KEY_COLUMNS, e.g., customer_id, transaction_date, amount, product_sku, payment_method] - Data source: [DATA_SOURCE, e.g., production MySQL database, third-party API] - Business purpose: [HOW_DATA_IS_USED, e.g., revenue forecasting, customer segmentation] **Generate the checklist organized into these six dimensions of data quality:** 1. **Completeness**: Identify which fields should never be null, acceptable null thresholds for optional fields, and checks for missing records or gaps in time-series data. 2. **Accuracy**: Define validation rules for each key field (e.g., range checks, format validation, regex patterns), cross-reference checks against trusted sources, and outlier detection criteria. 3. **Consistency**: Specify checks for referential integrity, cross-field logical consistency (e.g., end_date > start_date), standardization rules for categorical values, and consistency across related datasets. 4. **Timeliness**: Define expected data freshness SLAs, latency thresholds, checks for stale or delayed records, and monitoring for data arrival patterns. 5. **Uniqueness**: Specify primary key uniqueness checks, duplicate detection rules (exact and fuzzy matching criteria), and deduplication strategies. 6. **Validity**: Define domain-specific business rules, enumerated value checks, and schema conformance validations. For each check item, provide: - The specific check description - A sample SQL query or pseudocode to implement it - Severity level (Critical / Warning / Info) - Recommended remediation action Finally, suggest a scoring framework to calculate an overall data quality score from 0-100 based on weighted dimensions.