This article outlines a strategy for implementing a self-service test data solution using Tonic Structural. The goal is to enable application teams to generate high-quality, realistic test data from production-like datasets while ensuring privacy, compliance, and operational efficiency.
This approach provides a framework for teams to autonomously access and generate test data in an automated fashion, safeguarding sensitive information in line with privacy regulations. The strategy includes the following key components:
- Self-service access to test data: Empower application teams to generate their own test data on-demand without requiring direct involvement from data engineers or security teams.
- Privacy safeguards: Ensure that test data is anonymized and de-identified to protect sensitive and personally identifiable information (PII), complying with organizational and regulatory requirements.
- Automation: Streamline the process of generating test data through automated pipelines to reduce manual effort and human error.
- Realistic test data: Provide data that closely resembles production environments to enable more accurate testing and improve the quality of applications.
- Scalability: Ensure the solution is scalable across various teams and use cases within the organization.
Key steps to implement a self-service test data strategy
1. Data assessment and identification of sensitive information
The first step in implementing a test data strategy is to identify the types of data that will be used, and to identify any sensitive information that needs to be protected.
Recommended actions:
- Work with business and data teams to identify relevant datasets containing PII, sensitive financial data, or other regulated data types.
- Leveraging Tonic Structural’s Privacy Hub, identify sensitive data in the source systems and determine what needs to be desensitized before use in testing and how - anonymization, masking or synthesis.
2. Integrating Tonic Structural into the data pipeline
Structural enables the creation of de-identified, privacy-preserving test data based on real production data. The integration process involves connecting Structural with your organization's data sources to generate test data that mimics production environments without exposing sensitive information.
Recommended actions:
- Data connectivity: Establish secure connections between Structural and organization's applicable data sources (e.g., databases, data lakes, or cloud storage). Similarly, identify the destination locations for the Structural-transformed data (e.g. sandbox, staging, or other lower environments).
- Tonic Ephemeral allows for the rapid provisioning of databases from either pre-generated database snapshots or ad hoc data loads from Ephemeral.
- Data profiling: Use Structural’s features to understand the data’s structure, distributions, and relationships. This ensures the data generated by Structural accurately reflects production data.
3. Privacy-first approach with data anonymization
A core benefit of using Tonic Structural is its ability to anonymize and transform sensitive data before it is used for testing and QA purposes. The platform employs a variety of privacy-preserving techniques to ensure that sensitive information is protected.
Recommended actions:
- Configure privacy rules: Work with security and compliance teams to configure Structural’s privacy safeguards (e.g., data masking, generalization, and encryption) to protect PII.
- Data generation: Use Structural’s algorithms to generate masked/synthetic data based on the real-world data profiles, ensuring it retains the statistical properties of production data but without exposing any sensitive details.
- Auditing and compliance: Utilize Structural’s audit logs to track the creation and usage of synthetic datasets for transparency and regulatory compliance.
Accelerate your release cycles and eliminate bugs in production with realistic, compliant data de-identification.
4. Automated data generation pipelines
To enable self-service capabilities, it is crucial to automate the data generation process. Structural integrates seamlessly into CI/CD pipelines, allowing teams to trigger the creation of Structural test data when needed.
Recommended actions:
- Automated triggers: Set up automated triggers within the CI/CD pipeline, so developers and testers can request Structural data automatically as part of their testing workflows. Structural provides numerous scripts and workflows designed to automate the process of fetching, anonymizing, and updating data within Structural workspaces. Building upon the code can allow you to more easily build out your desired workflows.
- API-driven requests: Enable application teams to request specific datasets using Structural’s API, providing parameters such as data volume, privacy level, and dataset attributes required for testing.
- Scheduling: Use Structural’s scheduling features to automate recurring test data creation, ensuring that each testing cycle uses fresh, up-to-date synthetic data.
5. Access control and permissions
To maintain privacy and security, only authorized personnel should be allowed to access the Structural application. Structural supports role-based access control (RBAC), allowing teams to manage permissions and data access securely.
Recommended actions:
- Role-Based Access Control: Set up roles in Structural to control who can generate synthetic data and which datasets they can access. For example, developers may only need access to specific datasets related to their application.
6. Monitoring and quality assurance
Ongoing monitoring and quality assurance are essential to ensure that the generated data is of high quality and meets the necessary privacy standards.
Recommended actions:
- Automated reviews: Set up automated checks to verify the Structural-created data before it is released for use in testing, ensuring that there are no privacy risks or anomalies.
- Regular audits: Conduct periodic audits of Structural data generation and usage, ensuring that privacy policies are adhered to and that data is being used correctly.
7. Training and enablement for application teams
To make the test data solution truly self-service, teams need training on how to access and use synthetic data from Structural effectively.
Recommended actions:
- Workshops and documentation: Provide training sessions and documentation to application teams on how to use Structural to generate test data. This should cover how to request data and integrate it into testing environments.
8. Scaling the solution
Once you’ve proven implementation is successful for one or a few teams, the solution should be scaled to support more teams and diverse use cases.
Recommended actions:
- Expand data availability: Gradually increase the scope of datasets available for Structural data generation, ensuring that each team has access to the data they need.
- Centralized management: Implement a centralized governance model for test data creation. This can ensure consistency across teams while still allowing for decentralized, self-service access to test data.
Best Practice Recommendations
- Automate privacy configurations: Leverage Tonic Structural’s privacy features to standardize how sensitive data is handled across the organization.
- Implement a data governance framework: Define roles, responsibilities, and access controls for the Structural data generation process, ensuring compliance with internal and regulatory standards.
- Collaborative approach: Encourage collaboration between security, data engineering, and application teams to ensure alignment on privacy, data quality, and testing requirements.
- Continuous feedback loop: Continuously collect feedback from application teams to refine the test data process, address challenges, and improve the solution.
Conclusion
By implementing Tonic Structural, organizations can streamline the process of generating secure, high-quality, and privacy-compliant test data. This solution enables teams to self-serve while reducing the risks associated with handling sensitive production data. To learn more about optimizing your enterprise test data strategy, connect with our team today.
A bilingual wordsmith dedicated to the art of engineering with words, Chiara has over a decade of experience supporting corporate communications at multi-national companies. She once translated for the Pope; it has more overlap with translating for developers than you might think.
