Images and audio get all the generative model attention, but tabular data is what most ML teams actually train on. And tabular data is significantly harder to synthesize correctly than images. The reasons are structural, not just a matter of model complexity — and understanding them matters if you're evaluating whether a synthetic data tool will actually work for your use case.
Heterogeneous column types break homogeneous generative models
A typical business tabular dataset might contain: continuous numeric columns (amount, age, duration), discrete integer columns (count of transactions, number of items), categorical columns with low cardinality (status, region, category), categorical columns with high cardinality (merchant_id, user_id), boolean columns, datetime columns, and text columns (description, notes).
Image models are trained on homogeneous pixel grids — every input element has the same type and the same approximate range. Standard deep generative models (GANs, VAEs, diffusion models) make architectural assumptions suited to homogeneous continuous inputs. Applying them directly to mixed-type tabular data requires type-specific preprocessing for each column and careful reconstruction during generation, and even then the model doesn't have natural inductive biases for the categorical or temporal structure.
For categorical columns, the generative model needs to produce valid discrete values from the column's cardinality. Generating a continuous value and rounding it doesn't work for merchant_id — the rounded value might not correspond to a valid merchant that exists in the reference catalog. Generating a softmax over all possible categories works for low cardinality but becomes intractable for columns with thousands or millions of valid values.
Referential integrity: the constraint that most generators ignore
Real business data has foreign key relationships. A transaction record has a user_id that references a user record. A claim record has a provider_id that references a provider record. An order line has a product_sku that references a product catalog.
Naive synthetic generation treats each column independently (or models correlations across columns in the same row). It doesn't model the relationship between generated values and values in other tables. If you generate synthetic transactions and synthetic user records separately, the user_id values in the transaction table won't correspond to any user in the user table. The generated data fails referential integrity checks and can't be loaded into any system that enforces constraints — including most ML pipeline databases.
This problem compounds in multi-table schemas. A healthcare schema might have patients, encounters, diagnoses, procedures, and medications as related tables. Generating each table independently produces data where a diagnosis references a non-existent encounter, a procedure references a non-existent patient, and the temporal relationships between tables (encounter before diagnosis, diagnosis before treatment) are violated.
Solving referential integrity in generation requires either: generating tables in dependency order (generate patients first, then generate encounters that reference valid patient IDs, then generate diagnoses that reference valid encounters), or using a constraint solver that enforces foreign key validity during generation. Both approaches add significant complexity relative to single-table generation.
Business rules: the implicit constraints that documentation never fully captures
Beyond schema constraints, real tabular data encodes domain-specific business rules that aren't formalized anywhere but produce invalid combinations when violated. In financial transaction data: transaction amount can't be negative for a purchase type; refund_amount can't exceed original purchase_amount; transaction_date can't precede account open_date. In medical records: discharge_date must be after admit_date; certain procedure codes are only valid for certain diagnosis codes; pediatric records can't have adult-onset condition diagnoses.
These rules don't live in the database schema — they live in application logic, domain expertise, and business process documentation. A generator that learns only from the statistical distribution of training data will occasionally produce rows that violate these rules, because the training data contains the correlations that encode these rules implicitly, but the generator may not faithfully reproduce them in generation.
The practical approach in Twynvex is a constraint configuration layer: users specify post-generation constraints as expressions over column values. These constraints are evaluated after each row is generated, and rows that fail constraints are rejected and regenerated. For high-rate constraint violations, we optimize the generation configuration to produce fewer invalid rows upfront. This is less elegant than a solver that enforces constraints during generation, but it's more debuggable — you can see exactly which constraints are failing and at what rate.
Datetime columns and temporal coherence
Datetime columns add a dimension that continuous numeric columns don't have: temporal ordering. In a customer record, signup_date, first_purchase_date, last_purchase_date, and churn_date have an implicit temporal ordering that must be preserved. A generator that treats these as four independent continuous columns will produce records where first_purchase_date is after last_purchase_date, or where churn_date precedes the signup_date.
Temporal coherence also matters across rows. If you're generating a time series of transactions for a user, the inter-arrival times should be plausible (no transactions separated by 0.001 seconds in a manual payment system). The aggregate statistics of the time series (transaction frequency, seasonal patterns) should match the real user behavior distribution.
This is an area where GAN-based approaches specifically designed for time-series generation have an advantage over statistical modeling approaches — they can learn temporal dependencies that are difficult to model in a factored marginal+correlation framework. For Twynvex, generating realistic time-series of events for individual entities is a harder problem than generating independent cross-sectional rows, and we handle it with sequence-specific generation modes rather than the standard independent-row pipeline.
GAN-based vs. statistical approaches: when each is appropriate
For tabular data specifically, the tradeoff between GAN-based and statistical generation approaches is more nuanced than the "deep learning is always better" intuition suggests.
GAN-based tabular synthesizers (CTGAN, TVAE, TabDDPM) can learn complex multi-column dependencies that statistical approaches miss. They perform better on datasets with high-cardinality categoricals and complex conditional distributions. But they require more training data to generalize (statistical approaches can work from a few thousand rows; GANs typically need 10K+ rows to produce usable output), they're less interpretable (you can't directly inspect what correlation structure the model learned), and they can mode-collapse or memorize training examples — which creates privacy risk for sensitive data.
Statistical approaches (marginal fitting + copula-based joint distribution modeling) are more interpretable, auditable, and privacy-controlled. They work from smaller samples. But they miss higher-order dependencies and handle high-cardinality categoricals poorly.
We're not saying one approach is uniformly better. For the use cases we focus on — tabular ML training data with privacy constraints, where the sample is a few thousand to a few hundred thousand rows — statistical modeling with constraint solving covers most needs. For generation from large samples where high-cardinality categoricals dominate the schema, the GAN-based approaches are worth evaluating. The choice depends on your data, your privacy requirements, and how much training data you have for the generator itself.