concept#Data#Analytics#Data Processing#Data Quality
Data Validation
Data validation is the process of verifying and ensuring the accuracy and quality of data.
Data validation is crucial for ensuring data integrity.
Maturity
Established
Cognitive loadMedium
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeDesign
- Organizational maturityAdvanced
Technical context
Integrations
Database Management SystemsAPI InterfacesWeb Applications
Principles & goals
Input data should be validated.Errors should be detected early.Data integrity must be ensured.
Value stream stage
Build
Organizational level
Team
Use cases & scenarios
Use cases
Scenarios
Compromises
Risks
- Failures due to incorrect validation rules.
- Data loss due to incorrect validation.
- Increased effort due to manual validation.
Best practices
- Regular review of validation rules.
- Automation of validation processes.
- Documentation of all validation steps.
I/O & resources
Inputs
- Input data from users
- CSV file
- API data
Outputs
- Validated data
- Error log
- Integrity reports
Description
Data validation is crucial for ensuring data integrity. It is used to verify input data before processing, minimizing errors and enhancing the reliability of analysis and reporting systems.
✔Benefits
- Increased data quality.
- Minimization of errors.
- Improved decision-making.
✖Limitations
- Not all data can be validated.
- Validation rules can be complex.
- Could impact performance.
Trade-offs
Metrics
- Error Rate
The percentage of invalid data.
- Validation Time
The time required for validation.
- Data Integrity
The degree to which data is accurate and consistent.
Examples & implementations
Input Field Validation
Validation of web forms to ensure input accuracy.
CSV Data Safeguarding
Ensuring data quality by validating CSV uploads.
API Data Validation
Real-time validation of data between API services.
Implementation steps
1
Define validation requirements.
2
Implement validation logic.
3
Conduct test runs for validation.
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated validation logic.
- Insufficient documentation of rules.
- Lack of automated tests.
Known bottlenecks
Consistency checks can be time-consuming.Complex validation rules can be erroneous.Higher training requirements for staff.
Misuse examples
- Manual validation without proper standards.
- Ignoring user feedback.
- Arbitrary changes to validation rules.
Typical traps
- Overly rigid validation rules can hinder user experience.
- Lack of staff training leads to mistakes.
- Validation is not applied consistently.
Required skills
Knowledge of database managementUnderstanding of data validation rulesProgramming skills
Architectural drivers
Requirements for data integrity.Standardization of data formats.Security requirements for data processing.
Constraints
- • Input data must conform to specific formats.
- • Validation rules must be documented.
- • Technical resources required for validation.