The models are improving, but the data to train them is a bottleneck. Customer records with privacy restrictions, few data points of atypical events, and regulations on privacy means enterprises can’t tap into the real-world data that they need.
What do you do when a model is demanding data that your business simply can’t release? Synthetic data generation can come to the rescue with the capacity to make up new data, generate original data and do it fast, all while preserving the beneficial statistical features of the actual data rather than its individual records. But generating data is only the beginning. Validate its accuracy, manage bias, ensure privacy and build it into your processes.
This playbook explores how to design a synthetic data pipeline that can support high-quality, large-scale AI development.
Structured vs. Unstructured Synthetic Data
Generative synthetic data is used for various use cases depending on the data requirement. For enterprise use cases, synthetic data generation differentiates between: structured data (defined by a format) and unstructured data (images, video, audio).
Structured synthetic data includes table-like data, or a series of structured records in rows, columns, and fields. Banks can apply it to train models detecting frauds on simulated transactions, and healthcare organizations can generate synthetic patient records for software testing and research. Methods like entity cloning maintain links between related records, by swapping both entities’ information, and changing sensitive information to synthetic data. It isn’t enough to swap names and ID’s. The generated records must also preserve useful patterns without exposing information about real individuals.
In this instance, unstructured synthetic data serves a completely different purpose. The self-driving cars, robots, and computer vision algorithms require visual inputs that are as close to reality as possible. Generating every such input requires a lot of effort and can be expensive, even more so when rare cases like low visibility, obstacle detection, or extreme weather conditions are involved. Tools for simulation, like NVIDIA Omniverse, allow creating virtual environment and synthetic images for testing AI performance in certain conditions.
It is important to highlight that the difference between the two types of data is in the approach to generating and validating the data. The pipeline designed for the generation of financial records is not necessarily suitable for a vision model.
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Core Synthetic Data Generation Techniques Explained
The best method for generating synthetic data can also come down to what the use case is, how much control is desired, and the type of data that the AI model will require.
- Generative AI using GANs and VAEs GANs and VAEs learn the statistical patterns of a real dataset and then use them to generate synthetic samples. These models provide excellent results for synthesizing realistic tables, along with other more intricate data types. There is a caveat though: being similar to real data does not necessarily mean being accurate or privacy-preserving.
- Rules-based engines: These build records using established constraints and business rules. For example, banks can create records with odd transactions, and software teams can generate records with invalid input and system failures. This gives the most control when teams need specific edge cases.
- Simulation and 3D rendering: Teams involved with robots and autonomous vehicles make use of simulators for images, sensors, and materials. NVIDIA simulation ecosystem enables the validation of the situations that are hard to accomplish in reality.
The above can be combined in real life to achieve the realistic versus scalable trade-offs.
The Five-Step Enterprise Playbook for Building a Synthetic Data Pipeline
An artificial data pipeline requires more than just a data generation model. There is need for enterprises to ensure that they control what is allowed to go into the pipeline, how the artificial data is generated, and if the resulting data has improved the desired AI process. Failure to do this could make a bigger data set even worse.
Step 1. Profile the Source Data Before Generation
The first step will be to examine the source data set. Detect anomalies, correlations between data and missing data. Record the sensitive information such as PII that must be protected from inclusion in the synthesized data set as well as the attributes that must be preserved, for instance, in the case of transaction data sets, where realistic transactions but not identities should be kept. Establishing an initial benchmark is useful for measuring the result.
Step 2. Choose the Generation Engine for the Use Case
Choose the approach depending on the problem, not necessarily by choosing the latest approach. Rules-based approaches are good for testing software, generative approaches can be used to generate complex patterns, and simulation is effective for visual and physical AI.
NVIDIA’s NeMo Data Designer uses schema-driven generation, controlled sampling, structured language model outputs, and LLM scoring. It gives you the ability to evaluate the generated records and filter low-quality samples prior to their being included in training and model distillation workflows. The idea is quite simple – define your quality criteria before generating anything, not afterwards.
Step 3. Generate Data at Scale Without Breaking Relationships
When your generation method is developed, generate your data at the scale, format and coverage required. For structured data, maintain referential integrity so that linked records all move together in tables and databases. For imagery data, validate that labels are correctly applied to the images and that the simulated conditions accurately reproduce the situations described.
Google Cloud’s ZenteiQ case study reported a 4.07× increase in aggregate inference throughput for a synthetic data-generation pipeline. Its daily output rose from 1.47 billion to 5.97 billion tokens per 1,000 accelerators. The case illustrates the infrastructure needed to scale generation, although throughput alone does not establish data quality.
Step 4. Validate Fidelity, Utility, and Failure Points
The synthetic data should imitate the actual data set with respect to the characteristics that matter for the application. Comparison should be made between distribution, relationships between fields, unusual examples coverage, and performance of models based on synthetic data. Privacy concerns should also be tested. The objective here is not necessarily to match every instance with some mathematical precision to real data; rather, it is to retain valuable patterns without reproducing any confidential data.
For example, an AWS study on industrial safety used 750 synthetic images to increase the person-detection mean average precision at 50 from 0.051 to 0.134. On the other hand, the use of 1,000 synthetic images decreased the value to 0.106. AWS blamed this on the quality of image generation. This underscores the need to validate every instance of the dataset.
Step 5. Integrate the Pipeline into Development Workflows
A manual process will not be able to handle teams that retune and evaluate their models frequently. Tying up the process of generating synthetic data with CI/CD processes will help teams generate, validate, version control, and deliver datasets as part of the development process.
Microsoft Research found that iteratively generating fine-tuning samples based on the current model’s state improved performance over static generation under a fixed sample or teacher-model compute budget. Its evaluation covered mathematical and logical reasoning tasks across four datasets and four small language models. This supports a feedback-driven pipeline in which model results guide the next generation cycle, rather than leaving teams to produce data in bulk and hope it works.
Tackling the Big Three of Privacy, Quality, and Bias
Synthetic data can be a tool for enterprises facing data issues, but it can also introduce legacy issues to the next generation of AI models. The crux is in managing what the pipeline repeats and what it introduces.
Bias is introduced through filtering or lack of sampling. If historical data for your customer set or events is limited, then synthetic data creation will need to make more examples to get better coverage. But, biased oversampling of a minority set doesn’t intrinsically rid bias, you’ll need to see if the generated example resembles reality.
Privacy needs more than removing names. Differential privacy brings mathematical protections that restrict how much output from a dataset can reveal about individuals. Companies must ensure that their generated records are free from sensitive values and similar to the original records. Synthetic data is not anonymous just because it is synthetic data.
Quality depends on the real world. When teams continuously train models using synthetic outputs without sufficient new real-world data, it is possible for them to generate errors and lose diversity leading to model collapse. At NVIDIA, synthetic data evaluation consists of two categories: Synthetic Quality Score (SQS), which measures statistical utility, and Data Privacy Score (DPS), which measures privacy risk including membership and attribute inference.
Both evaluations solve different issues, and hence companies should not equate one with overall security. There is a need for different types of assessment before deploying synthetic data to the production environment.
Real-World Enterprise Use Cases for Synthetic Data
Synthetic health data may help healthcare organizations work together and carry out research even when there is a border involved and the privacy of health data is maintained. The organizations should be able to assess the privacy risks and make sure that HIPAA laws are followed.
In finance, synthetic frauds allow teams to train detection models on patterns that might be infrequent in historical data. It provides them with greater testing data without involving entirely customer-generated data.
Automotive OEMs can take these scenarios of heavy rain, low visibility, burstings, and more while driving in challenging conditions and automotive OEMs. This data allows the teams working on autonomous driving to evaluate how their model handles scenarios that are costly or hazardous to drive in.
Conclusion and Next Steps
Generating synthetic data can help enterprises overcome data shortages, privacy limitations and testing scenarios. Still, the pipeline is just the beginning. This data is only of value if it actually benefits model performance while not creating new privacy, bias or quality concerns.
The logical next step is to measure where the increasing difficulty in accessing data is hindering AI advancement. Select one use case, set specific quality and privacy requirements, and run a pilot. Compare the pilot’s performance to actual benchmarks before scaling up. Scaling up without validating the use case will simply exacerbate our existing problems.
