data-quality-profiler

Data Quality Profiler

Profiles data assets to assess quality dimensions and detect anomalies across the six core data quality dimensions.

Overview

This skill performs comprehensive data profiling to assess completeness, accuracy, consistency, validity, timeliness, and uniqueness. It generates statistical profiles, detects anomalies, identifies PII, and provides actionable recommendations for data quality improvement.

Capabilities

• Statistical profiling - Distributions, cardinality, null percentages, min/max values
• Data type inference and validation - Detect actual vs declared types
• Pattern detection - Regex patterns, formats, common structures
• Anomaly detection - Outliers, drift, unexpected values
• Referential integrity checking - Foreign key validation
• Freshness monitoring - Data age and update frequency
• Volume trend analysis - Record count patterns over time
• Schema change detection - Structural changes between runs
• Cross-column correlation analysis - Identify dependent columns
• PII detection and classification - Sensitive data identification

Input Schema

{
  "dataSource": {
    "type": "object",
    "required": true,
    "properties": {
      "type": {
        "type": "string",
        "enum": ["table", "file", "query"],
        "description": "Type of data source"
      },
      "connection": {
        "type": "object",
        "description": "Connection details (platform, database, schema)"
      },
      "identifier": {
        "type": "string",
        "description": "Table name, file path, or query string"
      }
    }
  },
  "sampleSize": {
    "type": "number",
    "description": "Number of rows to sample (null for full scan)",
    "default": 10000
  },
  "dimensions": {
    "type": "array",
    "items": {
      "type": "string",
      "enum": ["accuracy", "completeness", "consistency", "validity", "timeliness", "uniqueness"]
    },
    "default": ["completeness", "validity", "uniqueness"],
    "description": "Quality dimensions to assess"
  },
  "previousProfile": {
    "type": "object",
    "description": "Previous profile for drift detection"
  },
  "businessRules": {
    "type": "array",
    "items": {
      "column": "string",
      "rule": "string",
      "threshold": "number"
    },
    "description": "Custom business rules to validate"
  },
  "piiDetection": {
    "type": "boolean",
    "default": true,
    "description": "Enable PII detection and classification"
  }
}

Output Schema

{
  "profile": {
    "type": "object",
    "properties": {
      "tableName": "string",
      "rowCount": "number",
      "columnCount": "number",
      "profileTimestamp": "string",
      "columns": {
        "type": "array",
        "items": {
          "name": "string",
          "declaredType": "string",
          "inferredType": "string",
          "statistics": {
            "nullCount": "number",
            "nullPercent": "number",
            "distinctCount": "number",
            "distinctPercent": "number",
            "min": "any",
            "max": "any",
            "mean": "number",
            "median": "number",
            "stddev": "number",
            "histogram": "array"
          },
          "patterns": {
            "mostCommon": "array",
            "detectedFormat": "string",
            "regexPattern": "string"
          },
          "qualityScores": {
            "completeness": "number",
            "validity": "number",
            "uniqueness": "number"
          }
        }
      }
    }
  },
  "anomalies": {
    "type": "array",
    "items": {
      "column": "string",
      "type": "outlier|drift|unexpected_null|unexpected_value|format_violation",
      "severity": "high|medium|low",
      "description": "string",
      "examples": "array",
      "recommendation": "string"
    }
  },
  "piiFindings": {
    "type": "array",
    "items": {
      "column": "string",
      "piiType": "email|phone|ssn|credit_card|name|address|ip|custom",
      "confidence": "number",
      "sampleCount": "number",
      "recommendation": "string"
    }
  },
  "overallScore": {
    "type": "number",
    "description": "Weighted quality score (0-100)"
  },
  "dimensionScores": {
    "completeness": "number",
    "accuracy": "number",
    "consistency": "number",
    "validity": "number",
    "timeliness": "number",
    "uniqueness": "number"
  },
  "recommendations": {
    "type": "array",
    "items": {
      "priority": "high|medium|low",
      "category": "string",
      "description": "string",
      "impact": "string"
    }
  },
  "drift": {
    "type": "object",
    "description": "Changes compared to previous profile",
    "properties": {
      "schemaChanges": "array",
      "statisticalDrift": "array",
      "volumeChange": "object"
    }
  }
}

Usage Examples

Basic Table Profiling

{
  "dataSource": {
    "type": "table",
    "connection": {
      "platform": "snowflake",
      "database": "analytics",
      "schema": "core"
    },
    "identifier": "dim_customers"
  },
  "dimensions": ["completeness", "validity", "uniqueness"]
}

File Profiling with PII Detection

{
  "dataSource": {
    "type": "file",
    "identifier": "./data/customer_export.csv"
  },
  "sampleSize": 50000,
  "piiDetection": true,
  "dimensions": ["completeness", "validity", "accuracy"]
}

Query-Based Profiling with Business Rules

{
  "dataSource": {
    "type": "query",
    "connection": {
      "platform": "bigquery",
      "project": "my-project"
    },
    "identifier": "SELECT * FROM orders WHERE order_date >= '2024-01-01'"
  },
  "businessRules": [
    {"column": "order_total", "rule": "positive", "threshold": 0},
    {"column": "status", "rule": "in_set", "values": ["pending", "completed", "cancelled"]},
    {"column": "customer_id", "rule": "not_null", "threshold": 100}
  ]
}

Drift Detection

{
  "dataSource": {
    "type": "table",
    "identifier": "fact_sales"
  },
  "previousProfile": {
    "profileTimestamp": "2024-01-01T00:00:00Z",
    "rowCount": 1000000,
    "columns": [...]
  },
  "dimensions": ["consistency", "timeliness"]
}

Quality Dimensions

Completeness (0-100)

Measures the presence of required data:

| Metric | Calculation | |--------|-------------| | Column completeness | (total - nulls) / total 100 | | Row completeness | rows with all required fields / total rows 100 | | Overall | Weighted average across columns |

Validity (0-100)

Measures conformance to business rules:

| Check Type | Example | |------------|---------| | Type conformance | String in INT column | | Format conformance | Invalid email format | | Range conformance | Age > 150 | | Referential | FK without matching PK |

Uniqueness (0-100)

Measures duplicate and cardinality:

| Metric | Calculation | |--------|-------------| | Distinct ratio | distinct / total 100 | | Duplicate count | total - distinct | | PK uniqueness | unique PKs / total 100 |

Accuracy (0-100)

Measures correctness against ground truth:

• Requires reference data or business rules
• Cross-validates related columns
• Checks mathematical relationships

Consistency (0-100)

Measures uniformity across the dataset:

• Format consistency (dates, numbers)
• Categorical value consistency
• Cross-column logical consistency

Timeliness (0-100)

Measures data freshness:

| Metric | Threshold | |--------|-----------| | Data age | Hours since last update | | Freshness SLA | % meeting freshness requirement | | Lag detection | Processing delay measurement |

PII Detection Categories

| Type | Pattern Examples | |------|------------------| | Email | xxx@domain.com | | Phone | (XXX) XXX-XXXX, +1-XXX-XXX-XXXX | | SSN | XXX-XX-XXXX | | Credit Card | XXXX-XXXX-XXXX-XXXX (with Luhn check) | | Name | First/Last name patterns | | Address | Street, city, state, zip patterns | | IP Address | IPv4 and IPv6 |

Int