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Data Quality Testing: Examples, Techniques & Best Practices in 2025

Updated June 18th, 2025

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Quick Answer: What is data quality testing? #


Data quality testing is the process of assessing data for accuracy, consistency, and reliability. It involves running predefined tests on datasets to identify discrepancies, errors, or inconsistencies that could impact its usability and credibility.

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The characteristics of data quality testing are:

  1. Validation of source data
  2. Data transformation accuracy
  3. Data integrity checks
  4. Consistency checks
  5. Data completeness verification
  6. Continuous monitoring

Common data quality testing techniques include null set testing, framework testing, boundary value testing, completeness testing, and more. Through data quality testing, organizations can ensure that their data is trustworthy, meets predefined standards, and aligns with intended business outcomes.

Up next, let’s explore key characteristics and components, testing techniques, best practices, and the role of a unified trust engine like Atlan.

Table of contents #

  1. Data quality testing explained
  2. What are the six key techniques of data quality testing?
  3. What is a data quality testing framework?
  4. What are the key components of a data quality testing framework?
  5. What are some real-world examples of data quality testing in action?
  6. How can you create a data quality testing framework?
  7. What are some best practices to follow when establishing a data quality testing framework?
  8. The role of a metadata-led control plane in improving your data quality testing framework
  9. Data quality testing: Summing up
  10. Data quality testing: Frequently asked questions (FAQs)
  11. Data quality testing: Related reads

Data quality testing explained #

In ETL (Extract, Transform, Load), data quality testing confirms that data, once extracted from the source and after going through transformation processes, maintains its quality before it’s loaded into the target database.

This quality assurance is pivotal for analytics, business intelligence, decision-making and an indispensable element in the broader landscape of data governance. It serves as the foundation for the movement of data from one system to another.

The characteristics of data quality testing are:

  • Validation of source data: Verifying data accuracy and format at the point of origin
  • Data transformation accuracy: Ensuring data maintains quality through processing and ETL operations
  • Data integrity checks: Protecting against corruption and unauthorized changes during data lifecycle
  • Consistency checks: Maintaining uniformity across systems, formats, and business rules as anomalies, like using “Customer ID” in one table and “Client ID” in another, can lead to confusing analytics and reporting
  • Data completeness verification: Confirming all required fields and records are present and populated
  • Continuous monitoring: Ongoing assessment of data quality metrics and trend analysis

As mentioned earlier, ensuring the quality of data through ETL processes is a cornerstone of effective data governance.

It directly impacts an organization’s ability to make informed decisions, develop reliable analytics, and gain a competitive advantage. That’s why each aspect—from transformation accuracy to completeness—requires rigorous testing and validation.

Also read → Data transformation 101 | What is data integrity? | What is data completeness? | 9 key data quality metrics for 2025

What are the six key techniques of data quality testing? #

Data quality testing is not a one-size-fits-all activity. Given the complexity and variety of data structures and sources, specialized testing methods such as null set testing and framework testing are often necessary.

These techniques are aimed at addressing unique challenges in data quality, enhancing the scope of traditional testing methods.

Here are six key techniques:

  1. Null set testing: Evaluates how systems handle empty or null fields to ensure missing values don’t break downstream processing. This technique verifies that null values are appropriately managed or replaced by default values, preventing system failures in data pipelines and AI applications.
  2. Framework testing: A meta-level approach that evaluates the robustness of the data quality framework itself. This technique assesses whether the framework can handle various data quality scenarios and remain flexible enough to accommodate future modifications and organizational growth.
  3. Boundary value testing: Examines extreme values that data fields can contain to identify potential system failures at input domain edges. This ensures systems appropriately handle minimum and maximum allowed values, preventing data overflow errors and ensuring robust processing.
  4. Completeness testing: Verifies that all expected data is present by reviewing mandatory field population and optional field handling. This method identifies missing data that could impact analytics accuracy, regulatory compliance, and business decision-making processes.
  5. Uniqueness testing: Identifies duplicate records in fields where each entry should be unique, such as customer IDs or transaction codes. This technique prevents issues like double-counting in analytics and maintains accurate customer views while preventing revenue leakage.
  6. Referential integrity testing: Validates relationships between database tables to ensure foreign keys correctly correlate to primary keys in linked tables. This maintains relational integrity and prevents orphaned records that can skew analytics and business intelligence reporting.

Specialized data quality testing methods like null set testing and framework testing add another layer of assurance in data quality efforts. These techniques, when used in conjunction with general testing methods, offer a comprehensive approach to ensuring data quality.

Tailoring a mix of these testing methods to the specific data challenges of an organization can substantially improve the overall integrity, reliability, and usability of its data assets.

What is a data quality testing framework? #

A data quality testing framework is an enterprise-wide approach that establishes standardized processes for validating data fitness across the entire data lifecycle. It provides organizations with a systematic methodology to ensure data meets business requirements before entering critical analytics, AI applications, and decision-making processes.

Implementing a robust framework is essential for maintaining consistent data trust, reducing operational risk, and enabling scalable data operations that support business growth and competitive advantage.

What are the key components of a data quality testing framework? #

A well-designed data quality framework transforms data quality from a reactive cost center into a proactive business enabler, supporting faster time-to-insight and reducing the risk of poor decisions based on unreliable data.

Let’s look at the key components of a data quality framework:

  1. Start node: The entry point where the data quality testing process initiates.
  2. Initialize test environment: This step involves setting up a separate test environment that mimics real-world conditions. This includes provisioning compute resources, security configurations, and network access required for comprehensive testing.
  3. Integrate data sources: Ensure that the framework can seamlessly connect to various data sources, including cloud data warehouses (Snowflake, Databricks), legacy systems, APIs, and streaming data sources. This component ensures comprehensive coverage of the organization’s data estate.
  4. Test case design: Outline the kinds of tests to be conducted, from field-level validations to complex business rule checks. This includes field-level validations, cross-system consistency checks, regulatory compliance tests, and custom business rule validations tailored to specific use cases.
  5. Test execution: Run the designed test cases, either manually or through automated processes.
  6. Result reporting and monitoring: The test outcomes are logged, and reports are generated for analysis, with actionable insights into data health, trend analysis, and quality scorecards. Includes automated alerting for critical quality failures and integration with existing monitoring infrastructure.
  7. Maintenance and update cycle: The framework must be regularly updated to adapt to new data structures, business requirements, or technologies.

What are some real-world examples of data quality testing in action? #

Data quality testing applies across diverse business scenarios, from basic validation to complex pattern detection. Here are key examples that demonstrate practical applications:

  1. Checking for duplicates in a customer database: To check for duplicates, cross-reference email addresses, phone numbers, and customer IDs to identify and merge duplicate customer records. Use advanced matching algorithms to consolidate customer profiles across multiple touchpoints and systems.
  2. Validating data types in a product inventory: Ensure product IDs follow defined formats (integers or specific strings) and prices maintain decimal formatting for accurate financial calculations. For format compliance, validate that each field complies with expected data types, flagging anomalies that could disrupt automated processes.
  3. Geographical consistency in a shipping database: Verify that zip codes, cities, and states/provinces align correctly to prevent shipping errors and delivery failures. For standardizing addresses, cross-validate address components against postal databases to ensure deliverability.
  4. Temporal validity in time-series data: Confirm timestamps follow correct formats and sequences with no missing or duplicate entries in financial trading data. For chronological integrity, ensure time-based analyses maintain accuracy for regulatory reporting and trend analysis.
  5. Referential integrity in relational databases: Validate that foreign key relationships remain intact (e.g., Order records reference valid Customer IDs) to prevent orphaned records and analytical errors. For building consistency across tables, ensure data relationships maintain logical coherence across interconnected database tables.
  6. Pattern recognition for credit card fraud detection: Identify anomalous transaction patterns that deviate from established customer behavior baselines. For risk scoring, apply heuristic rules to flag suspicious activities (e.g., large foreign transactions) for immediate review and investigation.

These testing scenarios demonstrate how data quality validation protects business operations, ensures regulatory compliance, and maintains the integrity of data-driven decision-making across enterprise functions.

How can you create a data quality testing framework? #

A well-designed framework helps in identifying and rectifying data quality issues early on, leading to more informed and effective business strategies.

Here are eleven essential steps to create such a framework:

  1. Needs assessment: Identify specific organizational needs regarding data quality, including data goals and required quality standards. Engage key stakeholders across business units to understand their data quality expectations and use case requirements. Then, conduct a thorough assessment of existing data quality challenges, pain points, and gaps in current processes to establish a baseline for improvement.
  2. Select tools and technologies: Choose appropriate tools, platforms, and technologies for data quality testing, including ETL tools, database systems, and specialized data quality software. Ensure selected tools integrate seamlessly with existing data infrastructure and support required data sources. Consider scalability requirements to accommodate growing data volumes and organizational complexity over time.
  3. Define metrics and KPIs: Establish comprehensive metrics covering accuracy, completeness, consistency, reliability, timeliness, and validity that align with business objectives. Define custom quality indicators specific to your business processes and regulatory requirements. Set acceptable quality thresholds and escalation triggers for different data criticality levels to guide automated decision-making.
  4. Set up test environment: Create dedicated test environments that replicate production conditions while preventing interference with live operations. Provision appropriate compute, storage, and network resources to support comprehensive testing at scale. Implement proper access controls and security measures to protect sensitive data during testing processes.
  5. Data source integration: Integrate various data sources including cloud warehouses, legacy systems, APIs, and streaming platforms into your testing framework. Ensure secure, authenticated connections between the framework and all data sources. Validate data format compatibility and transformation requirements across different source systems to prevent integration issues.
  6. Design test cases: Develop comprehensive test cases covering all quality dimensions based on previously defined data quality metrics and KPIs. Create tests for custom business logic, regulatory compliance, and industry-specific requirements. Design scenarios for boundary conditions, error states, and exceptional data patterns to ensure robust validation coverage.
  7. Test execution: Execute test cases through automated processes where possible to ensure consistency and efficiency, while maintaining manual validation for complex business rules. Systematically document all test results, anomalies, and remediation actions for audit trails. Establish clear procedures for handling test failures and escalating critical quality issues.
  8. Analyze results: Analyze test results to identify systemic data quality issues and root causes of data degradation across your data estate. Evaluate the business impact of identified quality issues on downstream processes and decision-making capabilities. Develop a prioritization framework to rank quality issues by severity, business impact, and remediation complexity.
  9. Report and monitor: Generate comprehensive reports summarizing data quality status for leadership and stakeholder communication. Implement continuous monitoring tools to track quality trends and detect emerging issues before they impact business operations. Establish automated alerts for critical quality failures requiring immediate attention and response.
  10. Review and update: Regularly assess framework effectiveness against business outcomes and quality improvement goals. Update test cases, metrics, tools, and processes to accommodate evolving business requirements and new technologies. Continuously refine testing procedures based on lessons learned and operational efficiency gains.
  11. Feedback loop: Actively collect feedback from business users, data consumers, and technical teams on framework effectiveness and usability. Incorporate stakeholder suggestions to enhance framework business value and operational efficiency.

What are some best practices to follow when establishing a data quality testing framework? #

Ensuring high data quality is not just a one-time task but an ongoing practice that requires well-defined procedures and methodologies. These include:

  1. Define clear quality standards and metrics
  2. Prioritize based on data usage and impact
  3. Involve business stakeholders as data quality isn’t just an IT concern
  4. Automate where possible to speed up testing and reduce human error
  5. Use a variety of testing methods, such as data profiling, anomaly detection, and validation checks
  6. Implement continuous monitoring to track data quality metrics over time
  7. Documentation and reporting for greater accountability and comprehensive audits
  8. Ensure data security and compliance to avoid legal complications and data breaches

The role of a metadata-led control plane in improving your data quality testing framework #

Metadata serves as the foundational layer that enables effective data quality testing by providing essential context, lineage, and business rules that guide validation processes.

Without comprehensive metadata, data quality testing becomes reactive and fragmented, lacking the business context needed to determine true data fitness.

Atlan’s metadata-led approach provides a unified trust engine, combining comprehensive active metadata management with advanced quality validation capabilities. Atlan addresses the fundamental limitation of existing testing frameworks that operate without sufficient business context.

  • Business-first quality definition: Unlike traditional tools that focus on technical validation, Atlan enables business teams to define quality expectations in plain language or SQL, ensuring testing aligns with actual business requirements.
  • Native cloud execution: Atlan pushes quality checks down to execute natively in Snowflake Data Metric Functions and Databricks, keeping data in place while scaling to petabyte volumes.
  • Unified trust signals: Atlan aggregates quality signals from multiple tools including Anomalo, Monte Carlo, and Soda, providing a single pane of glass for data health across the entire data estate. Trust badges and quality scores surface directly in Atlan and BI tools, enabling proactive decision-making about data usage.
  • AI-ready governance: Atlan ensures every model training dataset, vector search, and AI prompt is backed by data that business teams have pre-approved as safe and fit-for-purpose. This capability addresses the critical challenge where AI systems fail when trained on poor-quality or inappropriate data.
  • Comprehensive metadata foundation: Atlan’s metadata lakehouse captures context, lineage, and quality signals across the modern data stack, enabling quality testing that understands business impact and data relationships. This foundation transforms reactive quality management into proactive trust engineering embedded throughout data operations.

Organizations implementing Atlan alongside existing testing frameworks see immediate improvements in quality coverage, business alignment, and operational efficiency.

For a major audio content platform, the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. This improved data search and discovery while reducing the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

Data quality testing: Summing up #

Data quality testing validates data accuracy, consistency, and reliability throughout ETL processes, serving as the foundation for trusted analytics and AI applications. Key techniques include null set testing, boundary value testing, completeness validation, and referential integrity checks.

An effective data quality testing framework requires needs assessment, tool selection, metric definition, and continuous monitoring.

Metadata plays a crucial role by providing business context and enabling automated rule generation. Organizations can enhance existing frameworks with metadata-led solutions like Atlan, which combines comprehensive metadata management with native cloud execution to create unified trust engines for enterprise-scale data quality management.

Book your personalized demo today to find out how Atlan can help your organization in improving your organization’s data quality.

Data quality testing: Frequently asked questions (FAQs) #

1. What is data quality testing? #


Data quality testing is the process of evaluating datasets for accuracy, consistency, and reliability. It involves predefined tests to identify errors and discrepancies, ensuring the data meets specific standards before use.

2. Why is data quality testing important? #


Data quality testing ensures that data is accurate, reliable, and fit for decision-making. Poor data quality can lead to incorrect insights, negatively affecting business decisions and operational efficiency.

3. How do I test data quality effectively? #


To test data quality effectively, organizations use a combination of automated and manual testing methods. Key steps include defining quality metrics, running validation tests, and continuously monitoring data for anomalies.

4. What are the key metrics for measuring data quality? #


Key metrics include accuracy, completeness, consistency, timeliness, and validity. These metrics help assess whether the data meets the required standards for specific business needs.

5. How do you create a data quality testing framework? #


Creating a framework involves eleven steps: needs assessment, tool selection, defining metrics and KPIs, setting up test environments, integrating data sources, designing test cases, executing tests, analyzing results, reporting and monitoring, reviewing and updating, and establishing feedback loops.

The process requires collaboration between business and technical teams to ensure quality standards align with business objectives.

6. How often should data quality testing be performed? #


Data quality testing should be continuous and automated wherever possible, with real-time monitoring for critical data streams.

Batch testing frequency depends on data update cycles and business criticality—daily for transactional systems, weekly for analytical datasets, and monthly for reference data. The key is establishing monitoring that detects quality degradation before it impacts business operations.

7. What are the biggest challenges in implementing data quality testing? #


Common challenges include lack of business context for defining quality standards, tool fragmentation across multiple platforms, scaling testing to handle large data volumes, getting stakeholder buy-in for quality initiatives, and maintaining test coverage as data sources evolve.

Success requires treating data quality as a business enabler rather than just a technical compliance requirement.

8. What role does metadata play in data quality testing? #


Metadata provides essential business context, data lineage, and validation rules that guide quality testing processes. It enables automated rule generation, meaningful quality metrics, and impact analysis when issues are identified.

Without comprehensive metadata, testing becomes reactive and lacks the business context needed to determine true data fitness for specific use cases.

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