Synthetic Data for Nonprofits: Training AI Without Exposing Sensitive Information

Synthetic data is artificially generated information that mimics the statistical properties, patterns, and relationships found in real datasets without containing actual observations of real individuals or entities. When done well, synthetic data preserves the insights and analytical value of original data while eliminating privacy risks associated with real personal information.

The fundamental concept is statistical fidelity without individual correspondence. Imagine a nonprofit with donor data showing that 30% of donors are under age 40, average gifts are $150, and younger donors tend to prefer monthly giving while older donors favor annual gifts. Synthetic data would generate artificial donor records that preserve these overall patterns and relationships, but no synthetic record would correspond to any actual donor. The aggregate statistics, correlations, and distributions match reality, allowing meaningful analysis and model training, but individual privacy is protected because the records are entirely fabricated.

Synthetic data generation typically relies on advanced machine learning techniques. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE) are the primary methods. GANs work by training two neural networks in opposition: a generator creates synthetic data attempting to mimic real data patterns, while a discriminator tries to distinguish synthetic from real data. Through iterative training, the generator improves until it produces synthetic data the discriminator can't reliably distinguish from real data. VAEs work differently, learning compressed representations of data patterns and using these representations to generate new synthetic examples.

Quality synthetic data must satisfy three criteria. Fidelity means accurately reproducing the statistical distributions and patterns of real data. Utility means supporting the same analytical tasks and producing similar insights as real data would. Privacy means protecting sensitive information so synthetic records can't be reverse-engineered to identify real individuals. These three qualities sometimes tension with each other, as higher fidelity can reduce privacy if synthetic data becomes too similar to specific real records.

It's important to understand what synthetic data can and cannot do. Synthetic data excels at preserving aggregate patterns, statistical distributions, correlations between variables, and relationships that enable predictive modeling. It allows organizations to share data with partners, vendors, or researchers without privacy risks, train AI models without exposing sensitive information, and test systems and applications with realistic data. However, synthetic data cannot perfectly capture every nuance of real data, doesn't replace the need for real data in all contexts (particularly for detecting rare events or unusual patterns), and requires careful validation to ensure it serves the intended purpose without introducing bias or distortion.

For nonprofits ready to explore synthetic data, several accessible tools and platforms make implementation practical even for organizations without deep technical expertise. The landscape includes both open-source solutions that are free to use and commercial platforms that offer more support and capabilities at a cost.

Gretel.ai represents one of the leading commercial platforms with accessible entry points. Gretel provides APIs and models for generating privacy-preserving synthetic data across tabular data (like spreadsheets and databases), text, JSON, event sequences, and more. Gretel offers both open-source functionality for technically capable teams and commercial products with additional features and support. The platform is designed to be developer-friendly while remaining accessible to analysts and data professionals without machine learning expertise. Organizations can start with small pilot projects to generate synthetic versions of donor databases or program participant records before expanding to more comprehensive implementations.

MOSTLY AI offers another commercial option focused on privacy-first synthetic data generation. MOSTLY AI generates privacy-safe synthetic datasets that preserve the statistical properties of source data while including fairness tooling to ensure synthetic data doesn't perpetuate or amplify bias on sensitive attributes like race, gender, or age. This is particularly important for nonprofits committed to equity, as naive synthetic data generation can sometimes amplify existing biases in historical data. MOSTLY AI's emphasis on fairness makes it valuable for organizations serving diverse populations or working to address historical inequities.

For healthcare nonprofits or organizations working with health data, Synthea stands out as a leading open-source tool specifically designed for healthcare. Synthea is free to use and modify under an open-source license, making it accessible for organizations with limited budgets. Synthea has successfully developed clinical modules for various medical conditions including cerebral palsy, opioid prescribing for chronic pain, sepsis, spina bifida, and acute myeloid leukemia. Healthcare-focused nonprofits can use Synthea to generate realistic patient data for training staff, testing systems, or developing analytics without exposing protected health information.

Implementation typically follows a phased approach. Organizations should start with a focused pilot project, selecting one dataset for synthetic generation (such as a donor database segment or program participant records). The pilot allows teams to learn the technology, validate that synthetic data serves its intended purpose, and build confidence before expanding. During the pilot, organizations should rigorously test synthetic data quality by comparing statistical properties to original data, validating that models trained on synthetic data perform adequately on real data, ensuring privacy protections are effective, and confirming that synthetic data serves the intended analytical or training purposes.

Organizations implementing synthetic data should also consider data preparation requirements. Synthetic data quality depends heavily on the quality of input data. Before generating synthetic data, organizations should clean source data to remove errors and inconsistencies, handle missing values appropriately, consider whether rare categories or outliers should be preserved or smoothed, and document data preparation steps for reproducibility and validation.

While synthetic data and privacy-preserving techniques offer tremendous potential, successful implementation requires navigating several challenges and making informed decisions about trade-offs. Organizations should enter this work with realistic expectations and clear strategies for addressing common obstacles.

Quality validation remains perhaps the most critical challenge. Organizations must verify that synthetic data actually serves its intended purpose before relying on it for important decisions or model training. Validation typically involves comparing statistical properties between real and synthetic data (distributions, correlations, relationships), training models on both real and synthetic data and comparing performance, testing whether insights derived from synthetic data hold true with real data, and assessing whether synthetic data captures important edge cases and unusual patterns. Organizations should plan significant time for validation and be prepared to iterate on synthetic data generation parameters until quality meets requirements.

The balance between privacy and utility creates inherent tensions. Stronger privacy protection generally requires more aggressive transformation of data, which can reduce analytical utility. Synthetic data that preserves every subtle pattern in real data risks being so similar that privacy protection weakens. Organizations must decide how much fidelity they truly need (often less than initially assumed), what level of privacy risk is acceptable for different use cases, and how to measure and communicate the privacy-utility tradeoff to stakeholders and leadership.

Technical capacity requirements vary significantly across approaches. Open-source synthetic data tools offer powerful capabilities at no cost but require technical expertise to implement and tune. Commercial platforms reduce technical barriers but come with subscription costs that may challenge nonprofit budgets. Organizations should honestly assess their technical capacity, budget for external expertise if needed (consultants, data scientists, technical partners), and consider building technical skills gradually through pilot projects rather than attempting comprehensive implementation immediately.

Regulatory and compliance considerations add complexity in certain sectors. Healthcare nonprofits must ensure synthetic data approaches comply with HIPAA requirements, educational organizations must consider FERPA implications, and organizations serving children or vulnerable populations may face additional protections. While synthetic data generally reduces compliance burden by eliminating real personal information, organizations should validate compliance with legal counsel, particularly when using synthetic data as a substitute for de-identification under regulatory frameworks.

Stakeholder communication and trust-building are essential for successful adoption. Staff, donors, and partners may not immediately understand synthetic data concepts. Organizations should develop clear explanations of what synthetic data is and how it protects privacy, why synthetic data enables valuable AI capabilities without compromising trust, and how the organization validates synthetic data quality and makes decisions about its use. Transparency about synthetic data use builds confidence rather than undermining it.

Many organizations implementing synthetic data are also addressing broader questions about building AI champions who can guide responsible technology adoption. Privacy-preserving AI capabilities benefit significantly from champions who understand both the technical possibilities and the ethical imperatives that motivate nonprofits.