Data Validation – The Data Community

Below is a glossary that includes 100 common “Data Quality & Data Validation” terms and phrases in alphabetical order. Enjoy!

Term	Definition & Example
Business Rule	Business-defined constraint on data. Example: Credit limit approval rules.
Check Constraint	SQL rule enforcing condition. Example: Age > 0.
Constraint	Rule enforced at database level. Example: NOT NULL constraint.
Continuous Validation	Ongoing automated validation. Example: Streaming pipelines.
Corrective Control	Fixes identified errors. Example: Data reload.
Data Accuracy	Degree to which data correctly represents reality. Example: Correct customer addresses.
Data Accuracy Rate	Percentage of correct values. Example: 99.5% accurate.
Data Anomaly	Unexpected or suspicious data value. Example: Sudden traffic spike.
Data Bias	Systematic data distortion. Example: Sampling bias.
Data Certification	Marking trusted datasets. Example: Certified gold tables.
Data Cleansing	Correcting or removing invalid data. Example: Fixing malformed phone numbers.
Data Completeness	Presence of all required data elements. Example: No missing customer IDs.
Data Completeness Rate	Percentage of populated fields. Example: 97% filled.
Data Confidence	Trust users have in data. Example: Executive reporting trust.
Data Conformance	Adherence to standards or schemas. Example: ISO country codes.
Data Consistency	Uniformity of data across systems. Example: Same currency code everywhere.
Data Deduplication	Removing duplicate records. Example: Merge customer profiles.
Data Defect	Specific instance of poor quality. Example: Invalid customer record.
Data Drift	Gradual change in data patterns. Example: Customer behavior shifts.
Data Enrichment	Enhancing data with additional attributes. Example: Adding demographic data.
Data Error	Incorrect or invalid data value. Example: Misspelled city name.
Data Exception	Approved rule deviation. Example: Legacy records.
Data Exception Handling	Process for managing violations. Example: Manual review.
Data Freshness	How current the data is. Example: Last updated timestamp.
Data Governance	Framework overseeing data quality. Example: Stewardship model.
Data Imputation	Filling missing values. Example: Replacing null with average.
Data Integrity	Accuracy and consistency over the lifecycle. Example: Foreign key relationships enforced.
Data Issue	Identified quality problem. Example: Missing values.
Data Latency	Delay between event and availability. Example: 2-hour ingestion lag.
Data Lineage	Tracking data flow and transformations. Example: Source to dashboard.
Data Matching	Identifying records referring to same entity. Example: Customer record linkage.
Data Noise	Irrelevant or misleading data. Example: Test records in prod.
Data Observability	Visibility into data health and behavior. Example: Pipeline monitoring.
Data Ownership	Accountability for data quality. Example: Business owner.
Data Precision	Level of detail in data. Example: Decimal places.
Data Profiling	Analyzing data to understand structure and quality. Example: Null percentage analysis.
Data Quality	Measure of how fit data is for its intended use. Example: Accurate sales totals in reports.
Data Quality Alert	Notification of quality issue. Example: Slack alert.
Data Quality Audit	Formal assessment of data quality. Example: Quarterly review.
Data Quality Automation	Automated quality processes. Example: CI/CD checks.
Data Quality Backlog	Tracked list of quality issues. Example: Jira tickets.
Data Quality Benchmark	Comparison standard. Example: Industry averages.
Data Quality Dashboard	Visual view of quality metrics. Example: Completeness trends.
Data Quality Dimension	Category used to measure quality. Example: Accuracy, completeness.
Data Quality Framework	Structured quality approach. Example: DAMA dimensions.
Data Quality Incident	Major quality failure. Example: Incorrect financial report.
Data Quality KPI	Metric tracking quality performance. Example: Duplicate rate.
Data Quality Maturity	Level of quality capability. Example: Reactive vs proactive.
Data Quality Monitoring	Ongoing quality measurement. Example: Daily freshness checks.
Data Quality Ownership Matrix	Mapping quality responsibility. Example: RACI chart.
Data Quality Program	Organization-wide quality initiative. Example: Enterprise DQ strategy.
Data Quality Regression	Reintroduced quality issue. Example: After schema change.
Data Quality Rule Engine	System executing validation rules. Example: Automated checks.
Data Quality Rule Violation	Failure to meet a rule. Example: Negative balance.
Data Quality Score	Numeric representation of data quality. Example: 98% completeness.
Data Quality SLA	Quality expectations agreement. Example: 99% accuracy target.
Data Quality SLA Breach	Failure to meet quality targets. Example: Accuracy below SLA.
Data Quality Trend	Quality performance over time. Example: Monthly improvement.
Data Reconciliation	Comparing datasets for consistency. Example: Finance system vs warehouse.
Data Reliability	Consistent data performance over time. Example: Stable metrics.
Data Remediation	Fixing data quality issues. Example: Reprocessing failed loads.
Data Sampling	Checking subset of data. Example: Random record review.
Data Standardization	Transforming data into a common format. Example: Converting dates to ISO format.
Data Steward	Role responsible for data quality. Example: Customer data steward.
Data Threshold	Acceptable quality limit. Example: ≤ 1% nulls.
Data Timeliness	Data availability within required timeframes. Example: Daily data refresh by 6 AM.
Data Trust Score	Composite measure of reliability. Example: Internal trust index.
Data Uniqueness	No unintended duplicates exist. Example: One row per customer.
Data Validation	Process of checking data against rules. Example: Rejecting invalid dates.
Data Validation Pipeline	Automated validation process. Example: Ingestion checks.
Data Validity	Data conforms to defined formats and rules. Example: Email follows standard pattern.
Data Verification	Confirming data accuracy. Example: Source system comparison.
Detective Control	Finds errors after entry. Example: Quality audits.
Domain Validation	Restricting values to a set. Example: Status = Active/Inactive.
Downstream Validation	Validating analytical outputs. Example: Dashboard totals.
Duplicate Detection	Identifying duplicate records. Example: Same email address twice.
Error Rate	Proportion of invalid records. Example: 2% failures.
Foreign Key	Reference to another table. Example: Order → Customer.
Format Validation	Ensuring correct data format. Example: YYYY-MM-DD dates.
Golden Dataset	Highest-quality dataset version. Example: Curated finance data.
Hard Validation	Blocking invalid data. Example: Reject invalid IDs.
Null Check	Ensuring required fields are populated. Example: Order ID not null.
Outlier Detection	Identifying abnormal values. Example: Negative revenue amounts.
Pattern Matching	Validating via regex patterns. Example: Postal code validation.
Post-Load Validation	Checks after data load. Example: Row count comparisons.
Pre-Load Validation	Checks before data ingestion. Example: File schema validation.
Preventive Control	Stops errors before entry. Example: Input validation.
Primary Key	Unique record identifier. Example: CustomerID.
Quality Gate	Mandatory validation checkpoint. Example: Before publishing data.
Range Validation	Checking values fall within limits. Example: Age between 0 and 120.
Referential Integrity	Valid relationships between tables. Example: Orders reference valid customers.
Root Cause Analysis	Identifying source of data issues. Example: ETL failure investigation.
Schema Validation	Checking data structure against schema. Example: Column data types.
Soft Validation	Warning without rejecting data. Example: Flag unusual values.
Source System Validation	Checking upstream data. Example: CRM record checks.
Statistical Validation	Using statistics to validate data. Example: Distribution checks.
Trusted Dataset	Data approved for consumption. Example: Executive KPIs.
Validation Coverage	Proportion of data checked. Example: 100% of critical fields.
Validation Rule	Condition data must satisfy. Example: Quantity must be ≥ 0.
Validation Threshold	Limit triggering failure. Example: >5% nulls.