Catalog
method#Data#Quality Assurance#Security#Software Engineering

Data Validation

Method for systematic verification of data quality and conformity using rules, validation pipelines, and error handling.

Data validation is a structured method to verify and ensure correctness, completeness and consistency of data across pipelines and interfaces.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

JSON Schema validators (e.g. AJV)API gateways for request validationETL tools with validation stages

Principles & goals

Early validation: validate inputs at the earliest possible point.Single source of truth: define rules centrally and reuse them.Fail fast and clear error communication.
Build
Team, Domain

Use cases & scenarios

Compromises

  • Overly strict rules block legitimate inputs
  • Missing or inconsistent rules create silent data errors
  • Security gaps with inadequate input sanitation
  • Central rule library with versioning
  • Combine client- and server-side validation
  • Clear error format and consistent status codes

I/O & resources

  • Data feeds or API payloads
  • Schema definitions or validation rules
  • Context information (user, version, source)
  • Validated data or error reports
  • Metrics and dashboards for data quality
  • Audit logs and remediation tasks

Description

Data validation is a structured method to verify and ensure correctness, completeness and consistency of data across pipelines and interfaces. It defines rules, formats and thresholds, combining automated checks with feedback and error handling. Applicable to APIs, databases and ETL processes.

✔

  • Reduced error rates and less rework
  • Improved data quality and reliable aggregations
  • Faster fault localization through standardized reports

✖

  • Validation alone does not fix incorrect business logic
  • High effort for heterogeneous legacy systems
  • Performance overhead for very large datasets

  • Validation error rate

    Percentage of invalid records relative to total input.

  • Validation pipeline throughput

    Number of processed entries per second.

  • MTTR for data incidents

    Mean time to remediate detected data issues.

API validator in order service

An e-commerce team used JSON Schema to validate order payloads and reduced backend error cases by 40%.

ETL quality checks for marketing data

Marketing data were automatically checked before aggregation; inconsistencies triggered automated remediation steps and notifications.

Migration validation during CRM migration

Validation rules were used during migration to find mapping errors and minimize rollbacks.

1

Gather requirements and data models

2

Define validation rules and schemas

3

Implement and integrate validation components

4

Set up automated tests and monitoring

5

Organize operation and continuous rule maintenance

⚠️ Technical debt & bottlenecks

  • Hard-coded validation logic across multiple services
  • Old rule versions without migration path
  • No test suites for validation rules
Inconsistent schemasLate-binding validationLack of observability
  • Blocking all non-exact matching formats without fallback
  • Ignoring data security checks during validation
  • Relying on human review instead of automated checks
  • Defining rules too restrictively and hard to loosen later
  • Unconsidered variants of input formats
  • Lack of observability conceals root causes
Knowledge of data models and schemasExperience with validation libraries and testingBasic understanding of data security
Data integrity across system boundariesScalability of validation processesSecurity and compliance requirements
  • • Legacy formats and non-standardized interfaces
  • • Real-time requirements with low latency
  • • Regulatory requirements for data retention