data-quality-profiler

Testing & Quality

Profiles data assets to assess quality dimensions, detect anomalies, and generate comprehensive data quality reports with actionable recommendations.

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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

Integration Points

MCP Server Integration

Elementary MCP - Data observability and anomaly detection
Database MCPs - Direct profiling queries

Related Skills

Great Expectations Generator (SK-DEA-006) - Generate expectation suites from profiles
Data Catalog Enricher (SK-DEA-017) - Enrich catalog with profile metadata