Case Study: School Management System- Consultancy for Synthetic Data in Hyper-Sensitive Environments
A Case Study in Protecting Children’s Data Through Synthetic Generation for Educational Platforms
In education technology, children’s data is sacred. A school management platform serving thousands faced an impossible dilemma: develop and test with real student data containing home addresses, health records, and location traces—risking catastrophic GDPR violations and child safety breaches—or halt innovation entirely. Through consultancy, DATAMIMIC transformed their 30-schema, 200+ table environment from dangerous production copies to safe synthetic generation, enabling stakeholder demos, yearly rollovers, and load testing without touching a single real child’s record. The result: zero compliance risk, full development velocity, and the confidence to innovate in the world’s most sensitive data environment.
Customer
Confidential Education Technology Provider
Industry
Education / Public Sector
Techstack
DATAMIMIC Toolbox, Multi-Schema RDBMS (~30 schemas, 200+ tables), Data Warehouses, JSON Structures
Service
Consultancy, Data Generation, Anonymisation, Enablement & Load Testing
Challenge
The client operated a school management platform in a hyper-sensitive environment, responsible for storing and managing some of the most protected information possible:
- Children’s home addresses and travel routes.
- Health records and support needs.
- Attendance data and time/location traces.
Their data landscape was highly complex:
- Multiple data warehouses for master data.
- ~30 schemas with 200+ cross-referenced tables.
- Deep relationships between relational and nested JSON structures.
The risks of using production data were unacceptable under GDPR and child protection laws. Beyond compliance, the client needed synthetic datasets not only for daily QA but also for:
- Training datasets to demonstrate new versions and school forms to stakeholders.
- Yearly rollovers (e.g., adding a new school year) without painful manual processes.
- Mass data generation for performance and load testing at scale.
Solution
We delivered this project as a consultancy engagement, starting with a detailed analysis and followed by model-building and enablement.
Ruleset-driven generation
Data models mirrored schema logic and JSON nesting, preserving relationships across 200+ tables.
Safe substitution
Sensitive student attributes (addresses, health info, travel paths) replaced with realistic synthetic equivalents.
Training datasets
Generated “current state” and “future state” data for stakeholder demos and feature acceptance.
Yearly rollover models
DATAMIMIC extended easily to generate synthetic students and classes for new school years.
Load test datasets
Mass synthetic data generated to simulate millions of students for performance and stress testing.
Enablement
Workshops and hands-on guidance ensured the client’s staff could maintain and extend the models independently.
As consultants, the DATAMIMIC team helped us replace sensitive student data with safe, realistic synthetic datasets. We now demo new features, simulate new school years, and run performance tests—all without ever exposing real child data.
School Management System
Result
Regulatory compliance: No live student data used outside production, satisfying GDPR and child safety requirements.
Cross-schema consistency: Complex referential structures across 30 schemas and JSON documents preserved automatically.
Stakeholder-ready training datasets: Realistic data used to showcase new versions and validate new school forms.
Operational agility: New school years added quickly by extending rulesets—no manual rework required.
Performance readiness: Large-scale datasets supported full system load testing under peak conditions.
Sustainable self-sufficiency: Internal teams trained to adapt and maintain DATAMIMIC models long-term.
Massive Efficiency Gains:
Processing Speed
Achieved inline streaming anonymisation without impacting Kafka performance
PII Exposure
Significant resource optimization achieved
Compliance Reporting
Automated compliance reports delivered transparency and reduced regulatory risk.
Enhanced Operational Excellence:
Real-time anonymisation at scale: Millions of payment records per hour anonymised inline without impacting Kafka latency.
Deterministic integrity: Consistency across 140–180 column entities, fully aligned with regulatory specs.
Audit readiness: Automated compliance reports delivered transparency and reduced regulatory risk.
Bulletproof Compliance & Risk Mitigation:
Team autonomy: ACI teams trained to adapt and extend rulesets without vendor dependence.
Operational safety: Pre-production and downstream systems ran without exposure to live PII, ensuring GDPR compliance.