---

name: data-quality-profiler description: Profiles data assets to assess quality dimensions, detect anomalies, and generate comprehensive data quality reports with actionable recommendations. version: 1.0.0 category: Data Quality skill-id: SK-DEA-005 allowed-tools: Read, Grep, Glob, Bash, WebFetch

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

Applicable Processes

• Data Quality Framework (data-quality-framework.js)
• Data Catalog (data-catalog.js)
• ETL/ELT Pipeline (etl-elt-pipeline.js)
• A/B Testing Pipeline (ab-testing-pipeline.js)

References

• Data Quality Dimensions - DAMA
• Great Expectations
• Elementary Data
• Awesome Data Quality

Version History

• 1.0.0 - Initial release with six dimension profiling and PII detection