Why is Referential Integrity Important for Test Data Management?

Establishing a pool of accurate and compliant test data is still challenging for many companies. That’s because privacy tools that modify the datasets can disrupt referential integrity. But why is referential integrity important?

To answer that question, we should discuss concepts like parent tables, foreign key rules, and anonymization. Without integrity, you may produce flawed data that can derail your development pipeline or lead to system crashes.

Our article will explain the importance of referential integrity in simple terms. We’ll discuss what it means and how it affects testing data. We’ll explain referential integrity rules to maintain integrity while achieving full compliance with privacy laws.

What Is Referential Integrity?

Real data captures true occurrences gathered directly from real-world activities and interactions. It’s sourced from production systems, vendors, public records, or other datasets that contain operational information. For example, it might include a decade-old backup with details about real individuals or transactions or a set of public records acquired for testing purposes.

Because real data mirrors actual events and interactions, it’s crucial for applications where precision and authenticity are essential. Its data points accurately represent real-world contexts, making it a reliable foundation for analytics and to train machine learning models.

However, real data has its challenges. It often includes noise, inconsistencies, and biases that reflect the messy nature of the real world. Managing real data also raises significant privacy and compliance concerns, as it frequently contains personally identifiable information (PII) that must be handled carefully under strict regulations.

The Pros and Cons of Real Data

Referential integrity is a governance property that ensures data accuracy and consistency in tables and databases. Here’s how it all works.

In a relational database, data maintain connections through primary and foreign keys:

• The primary key is a unique identifier for each record in one table that serves as a main reference point.
• The foreign key is a collection of fields that helps identify other rows or tables, basically enforcing a link between data.

Management systems enforce data integrity with rules that govern the relationships between these keys. The primary referential integrity constraints include the following:

• Insert rule: You can’t add records with a foreign key (child table) if it doesn’t match an existing value with the primary key (parent table).
• Update rule: Prevents updating records in the parent table if it doesn’t update records in the child table.
• Delete rule: Data can’t be deleted from the parent table if the child table contains matching records.

That’s it with the technicalities. Now, let’s see why integrity is critical for test data.

Why is Referential Integrity Important in Database Management?

Referential integrity ensures the reliability of database management systems, including tools for test data management. This framework keeps the relationship between tables consistent as you modify or migrate the data.

Data integrity allows compliance teams to maintain high data quality while upholding regulatory requirements. All companies must follow data protection laws, such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA), that require companies to protect personally identifiable information (PII) of their clients.

To use data freely for testing, companies use privacy-enhancing technologies (PETs) that strip the PII from their data. And here comes the problem. Without the means of maintaining data integrity, the tools may introduce inconsistencies and errors, such as:

• Orphaned records (child records that reference non-existent parent records)
• Broken data relationships
• Conflicting and duplicated (redundant) data.
• Missing key data

In addition, under GDPR, pseudonymized data is still considered personal data, meaning that maintaining its referential integrity is essential to avoid legal risks. In contrast, anonymized data, once fully de-identified, is exempt from GDPR obligations. Without referential integrity, inconsistent or orphaned records can lead to compliance violations, broken data relationships, or duplicated data, which may result in system failures or loss of critical information.

Data integrity is a similar concept to database normalization. Both help uphold data quality in tables. However, database normalization focuses on organizing data to minimize redundancy and dependencies, whereas referential integrity keeps elements consistent.

Lack of integrity may result in system crashes, application errors, and unexpected system behavior. It may also impact your business if you lose customer data relationships.

How Does Referential Integrity Affect Test Data?

Maintaining referential integrity is necessary for realistic testing environments. Ideally, developers and testers need data that mirrors the structure of production data. However, commonly used PETs can disrupt the relationships between tables.

Most problems come from broken links between primary key and foreign key values. For example, applications may fail to retrieve related data during testing, leading to difficult-to-diagnose errors. You may also encounter unpredictable behavior because of missing values and inconsistencies in the modified test data.

These issues can be caused by modern techniques like data pseudonymization, anonymization, and subsetting.

Referential integrity in pseudonymized and anonymized data

Data pseudonymization and anonymization tools are often used to produce compliant data for testing. Maintaining referential data integrity while anonymizing data helps safeguard personal information from unauthorized access or exposure during testing.

Pseudonymization is a de-identification tool that replaces PII, protected health information, and other financial information with mock data (pseudonyms). Anonymization tools transform direct and indirect identifiers using more advanced techniques.

Both these techniques carry risks. Pseudonymized data is reversible under controlled conditions (usually with additional information, like a decryption key). Even anonymized data can be exploited to restore the original information.

Maintaining consistent mapping is complex, especially in larger databases with complex tables. Anonymization and pseudonymization can disrupt the relationships if they alter the identifiers used as keys.

How to enforce referential integrity in anonymized (pseudonymized) data:…

How to enforce referential integrity in anonymized (pseudonymized) data:

• Use deterministic masking techniques: Deterministic masking replaces sensitive data with consistent values. For example, if the ID of “John D.” is consistently masked to “ABCDE” across customer tables, the relationship remains intact.

• Apply an encryption or hash function to identifiers: Rather than deleting key identifiers, you encrypt or mask them. Doing so preserves integrity while protecting sensitive information.
• Map elements and relationships: Keep a secure mapping table to use as a reference between the original and modified (usually pseudonymized) data.
• Enforce foreign key constraints: Use database management systems that automatically check and enforce integrity during anonymization.

It’s necessary to take action if you find integrity errors. Remove orphaned records, add missing primary keys, and update foreign key values to avoid compounding issues.

Referential integrity in subsetting

Subsetting transforms production databases into smaller, representable portions of datasets for testing. This technique is also used for database normalization.

Ideally, larger data sets are reduced to representative portions that are easier to handle. However, selecting only certain records may result in broken foreign key relationships in a related table. An example would be a transaction record that references a non-existing customer table.

How to enforce referential integrity in subsetting:

• Apply constraints on cascading actions: Use constraints on DELETE CASCADE and UPDATE CASCADE to avoid inconsistencies. This ensures that the corresponding foreign keys in the child sets are automatically deleted or updated with the parent set.
• Prioritize ACID-compliant transactions: Execute subsetting operations within the Atomicity, Consistency, Isolation, and Durability (ACID) framework. If any part of the subsetting process fails, the transaction is rolled back.
• Implement monitoring tools: Companies should use automated tools that detect missing references and integrity violations to address them on time.

With specialized tools, like synthetic data generation platforms with subsetting functionality, companies can greatly reduce manual work and the risks of inconsistencies.

How Syntho’s Synthetic Data Platform Enforces Integrity

Synthetic data is artificially generated mock data that simulate real data’s characteristics without using actual sensitive information.

The synthetic data tools generate mock data from scratch based on real datasets. Platforms like Syntho utilize advanced algorithms that capture the underlying distributions, correlations, and structures of the original data. This provides several business benefits:

• Consistency across tables: Our model preserves the relationships between primary and foreign keys, ensuring that every database remains consistent.
• Comprehensive compliance: Unlike anonymization (pseudonymization) technologies, synthetic data is made from scratch. It doesn’t contain any direct or indirect identifiers, exempting the generated datasets from regulatory oversight.
• High-quality data: Synthetic data can help uncover issues that might only surface under real-world data conditions.
• Built-in features: Your teams can use various features to create compliant test data. In particular, the PII scanner detects PII within tables, and the subsetting feature allows you to produce small representative datasets.

Last but not least, Syntho integrates with other automation software and database management tools. You can embed our synthetic generation tool within your CI/CD pipeline, so your team can create up-to-date test data when needed.

Conclusion

It should now be clear why referential integrity is important in all aspects of database management. Some anonymization methods may disrupt integrity, which will reduce the data’s usefulness.

Luckily, companies have the means to maintain integrity. Advanced algorithms and specialized tools can produce volumes of compliant, functional, and error-free testing data.

Do you want to learn more about our synthetic generation platform? Consider reading our product documentation or contact us for a demo.