Statistics Offices
- Privacy-first data sharing
- Simulation-ready insights
- AI-powered innovation
- Future-proof collaboration
- Regulatory compliance
Granular Data.
National Statistical Offices sit on a goldmine of granular data – census records, health surveys, energy consumption patterns, labor market insights. Yet this treasure trove remains largely locked away, constrained by privacy laws and disclosure risks. Meanwhile, policymakers urgently need richer, more granular data to tackle complex challenges like aging populations, climate transitions, and public health crises.
Synthetic data breaks this impasse. By generating statistically accurate datasets that contain no real personal records, statistics offices can finally open their vaults – enabling research, powering AI development, and supporting evidence-based policymaking at unprecedented scale, all while safeguarding citizen privacy.
Challenges.
Statistics offices face mounting pressure to democratize access to microdata while upholding ironclad confidentiality obligations. Traditional anonymization techniques fall short: they either strip away too much detail to be useful, or leave residual disclosure risks that prevent data release. The result? Too little data exchange with researchers, universities, and the private sector – precisely when demand for data-driven insights is exploding.
Beyond access challenges, today’s policy questions require forward-looking analysis that historical data alone cannot provide. How will healthcare demand evolve as populations age? What happens if we accelerate the energy transition? Which interventions work best in a future pandemic? These “what-if” scenarios demand simulation capabilities that go far beyond traditional statistical reporting. Yet building simulation-ready datasets from confidential microdata has been nearly impossible – until now.
Use cases
Sharing data for research and policy making
Release synthetic public-use microdata files that researchers, journalists, and regional officials can analyze freely—enabling innovation without compromising individual privacy.
Policy simulation
Run "what-if" scenarios on synthetic populations to model health crises, pension reforms, climate policies, and demographic transitions before implementing them in reality.
AI development
Train machine learning models for fraud detection, labor market prediction, and disease outbreak forecasting on synthetic data—avoiding sensitive data exposure while achieving comparable accuracy.
Testing systems
Provide vendors and IT teams with realistic synthetic test datasets for developing new software, dashboards, and analytical tools—eliminating legal barriers to system development.
Health crisis preparedness
Simulate pandemic response strategies, hospital capacity planning, and vaccination rollouts using synthetic patient populations with realistic comorbidities and demographic distributions.
Energy transition planning
Generate synthetic smart meter data and household energy profiles to model grid impacts of electric vehicles, solar adoption, and demand-side management—without exposing consumer usage patterns.
Demographic aging analysis
Create synthetic populations aged forward in time to forecast pension burdens, healthcare demand, workforce composition, and social support needs under various policy scenarios.
Cross-border data exchange
Share synthetic datasets with international statistical organizations and research networks, bypassing legal restrictions on cross-border transfer of personal data.
Educational and training purposes
Provide students, researchers, and internal staff with realistic practice datasets for developing analytical skills without accessing sensitive records.
Preliminary analysis sandbox
Let researchers develop code, test hypotheses, and refine models on synthetic data before applying for access to confidential real data—streamlining research workflows and reducing burden on secure access facilities.
What are the Benefits?
Transform confidential microdata into shareable synthetic datasets that enable collaboration with universities, ministries, and international partners—without privacy trade-offs.
Shift from reporting what happened to simulating what could happen—empowering policymakers with scenario analysis and evidence-based decision support.
Meet the strictest confidentiality standards with differential privacy guarantees, turning privacy compliance from a barrier into an enabler of data use.
Get inspired by our cases.
"Our strategic use of synthetic data has delivered remarkable success, showcasing its transformative potential in data innovation while ensuring privacy and transparency. This milestone not only strengthens our leadership in advanced analytics but also sets a pioneering model for future growth and scalability."