Data Quality Monitoring

Data Quality Monitoring

Overview

Data Quality (DQ) Monitoring is the continuous process of validating data against predefined rules and expectations. In a modern data stack, monitoring must happen at every stage: Ingestion, Transformation, and Serving.

Core Principle: "Garbage in, garbage out. If the data is wrong, the analytics, AI, and decisions will be wrong too."

1. The Six Dimensions of Data Quality

Common industry standards for measuring data health:

2. Automated DQ Testing (Frameworks)

A. dbt (Data Build Tool) Tests

Ideal for SQL-based transformations.

# schema.yml
models:
  - name: active_users
    columns:
      - name: user_id
        tests:
          - unique
          - not_null
      - name: age
        tests:
          - accepted_values:
              values: [18, 120]

B. Great Expectations (Python)

The most flexible tool for complex Python-based data pipelines.

import great_expectations as ge

df = ge.read_csv("data.csv")
df.expect_column_values_to_be_between("age", 18, 120)
df.expect_column_values_to_not_be_null("email")
df.expect_column_values_to_match_regex("email", r"[\w.-]+@[\w.-]+")

3. Real-Time DQ with SQL Assertions

You can run "Check" queries alongside your production workload to catch silent failures.

-- Detect "Price Drift" anomaly
SELECT 
    product_id, 
    AVG(price) OVER(PARTITION BY category) as avg_cat_price,
    price
FROM current_inventory
WHERE price > (avg_cat_price * 5) -- Alert if price is 5x the average

4. Monitoring Tools Landscape

5. Data Profiling and Drift Detection

Data Drift occurs when the statistical properties of the data change over time, even if the schema stays the same.

• Schema Drift: Adding/Removing columns.
• Concept Drift: The meaning of a value changes (e.g., "Active" now means something different).
• Predictive Drift: Distribution of data shifts (e.g., average purchase value drops by 50%).

6. DQ Incident Management

When a monitor fails, it should trigger an incident workflow:

1. Detect: Alert fires (Slack/PagerDuty).
2. Triage: Is the data just late, or is it incorrect?
3. Isolation: Block downstream pipelines to prevent "polluting" the data warehouse.
4. Remediation: Re-run the pipeline or fix the source code.
5. Validation: Verify the fix.

7. The Data Quality Dashboard

A high-level view for stakeholders:

• DQ Score: (0-100) Aggregated health of all datasets.
• Freshness SLA: % of pipelines meeting their timeliness target.
• Top Failing Tests: Which columns are most problematic?
• Time to Resolve: Average time to fix DQ incidents.

8. Real-World Scenario: E-commerce Ghost Orders

• Scenario: A promotional campaign led to a surge in orders, but a bug caused the tax_amount to be 0 for all of them.
• Detection: A DQ monitor was in place checking that tax_amount > 0 for all non-exempt regions.
• Response: The monitor failed at 2:00 AM. The data pipeline was automatically paused.
• Outcome: The finance team was notified before the morning report was generated. The tax glitch was fixed, preventing thousands of incorrect invoices.

9. DQ Monitoring Checklist

• Completeness: Are there checks for NULL values in primary keys?
• Freshness: Do we have alerts for data that hasn't arrived in X hours?
• Volume: Sudden drop or spike in row count (e.g., < 50% of typical volume)?
• Distribution: Has the average or median value shifted significantly today?
• Schema: Have any columns been renamed or dropped?
• Downstream blocking: Does a failure stop downstream tasks automatically?

Related Skills

• 43-data-reliability/data-contracts
• 43-data-reliability/data-lineage
• 42-cost-engineering/cost-observability