Beyond Text: Using GenAI to Create High-Fidelity Synthetic Tabular Data

Today’s economy relies heavily on data. The demand for high-quality, diverse, and privacy-compliant information is greater than ever. In this context, synthetic data generation has become one of the most powerful tools available to businesses.

Rather than depending solely on sensitive or expensive real-world datasets, which also tend to be sensitive, organizations can create artificial datasets. These synthetic datasets keep the same statistical patterns and structures as the originals, making sure there’s no correlation problem in the ultimate results. The outcome is data that acts like real-world information but does not bring with it the risks of data leak or compliance. In this blog, we’ll take a look at everything there is to know about synthetic data generation and how generative AI is a force behind it.

What is Synthetic Data Generation?

Synthetic data generation is very impactful, especially in areas where structured or tabular datasets are common and are an absolute need. Some of these industry-specific work areas include entering and managing financial records, healthcare data, retail transactions, and manufacturing logs. 

Synthetic datasets have been adopted the most by organizations in such sectors. The global synthetic data generation market was valued at USD 310.5 million in 2024 and is expected to expand at a robust CAGR of 35.2% from 2025 to 2034. (Source)

This type of data has helped them not just build data sets, but test them, validate them via AI and machine learning systems. All of these together have given businesses the newfound confidence that the synthetic results will apply to real-life scenarios as well. While earlier methods depended mainly on statistical sampling or simulation, the rise of generative AI for tabular data has completely changed the field. This shift has allowed for more realistic and flexible synthetic datasets.

Does Generative AI Enable Synthetic Tabular Data Creation?

Generative AI has already changed fields like natural language processing, computer vision, and code generation. Now, it is moving into structured data areas. Here’s how Gen AI does it.

• It uses machine learning models that learn from real datasets and then lets dedicated teams within organizations create generative ai tabular data with the same complexity and variety as the original records.
• Unlike older data generation methods that depended on set statistical rules, synthetic data generation with generative AI lets machines operate large scull but not without the subtle dependencies.
• This means that when we need to understand how different factors in a dataset are connected, such as how income affects spending habits or how supply chain issues affect delivery times, a GenAI model can create a synthetic dataset that reflects these relationships without the need for a human.
• Unlike original datasets, synthetic data contains millions of realistic rows of generative AI tabular data much faster than it would take to even gather comparable real-world samples.
• This progress is run by deep learning methods like Generative Adversarial Networks and variational autoencoders. These models not only replicate the averages of a dataset but also enable the underlying systems that help identify rare cases.

Benefits of Synthetic Data Generation with Generative AI

Here’s a complete side by side comparison of the advantages of Gen AI Synthetic data generation.

Use Cases of Synthetic Tabular Data Across Industries

Generative AI Tabular Data can be used in a wide variety of industries.

• Healthcare is an area where synthetic data can be employed, to train predictive diagnostic models, while preserving the privacy of the patients.
• Financial services organizations can create realistic transaction data to improve fraud detection systems and stress test risk models.
• In retail and consumer goods, synthetic datasets can help retail companies understand customer purchase behavior and simulate recommendation systems. When it comes to retail, success stories aren’t new to Tredence, as we a leading retailer to adopt a global holdout strategy with AI-driven customer insights, boosting campaign ROI and engagement. The approach delivered a 1–3% lift in returns, engagement, and conversions.
• In the telecommunications industry, synthetic call records and usage logs enable companies to optimize network performance and predict demand spikes.
• Synthetic test data generation also has very high potential, for use cases including manufacturing, supply chain, etc.
• Companies can simulate predictive maintenance algorithms or logistics optimization models with a wide range of simulated artificial sensor data streams.

This capability of creating volumes of realistic data to spec has the potential to allow organizations to investigate more options than would never be practical with just real-world data alone. Nowadays, even AI agents for data analytics are emerging as valuable tools, working with synthetic data to discover new patterns, automate reporting, and guide business strategies in real time.

Synthetic vs. Real Data: Key Differences and When to Use Each

It is worth noting that synthetic and real datasets should not be adversarial, and in fact the two frameworks should act in a complementary fashion. Of course, real data is still invaluable in cases when you need absolute accuracy and provenance that can be verified, as in the case of regulatory filings or on-ground data samples. On the other hand, in many contexts, such as those that demand huge numbers quickly, wanting privacy, including data varieties, is where synthetic data becomes more interesting.

For some, purely synthetic data might be too risky and insufficient by itself, so a mix of synthetic and real data is the best of both worlds for them. This hybrid approach has the potential to retain the factual grounding of real records while taking advantage of the flexibility and expandability of synthetic ones. This ‘best of’ approach is often slowly becoming a best practice on enterprise AI adaptation.

Synthetic Data Generation Techniques and Methods

The following are the top synthetic data generation methods:

Technique 1 - Rule-based simulation

This approach relies on predefined logical conditions and rules to fill in data. It is suitable for simple or well-structured problems but struggles to replicate realistic behavior in complex environments.

Technique 2 - Statistical models

Synthetic data is created by building distributions based on probability and sampling from them. This method works well when it comes to capturing general data characteristics but it is said to fail while representing complex relationships or dependencies.

Technique 3 - Machine learning–based generative models

Machine learning used advanced techniques such as Generative Adversarial Networks, Variational Autoencoders (GANs & Vae), and transformer-based architectures to learn directly from real datasets. These models capture multiple dependencies of different varieties to produce highly realistic synthetic records.

Technique 4 - Hybrid methods

Many organizations combine statistical modeling with machine learning to balance interpretability with fidelity, making sure the method matches up to the sector specific requirements.

Generative AI Tabular Data vs. Traditional Data Simulation Approaches

Sometimes, conventional simulation techniques tend to result in datasets that look realistic but lack the complexity and diversity required for more in-depth analysis. For instance, basic regression-based methods exhibit trouble while trying to capture long-tail distributions.

So, in order to combat that, generative AI tabular data adopts a more comprehensive strategy. More than averages, these Gen AI models also reproduce nonlinear relationships, hidden correlations, and uncommon cases that are missed by conventional approaches. This is especially useful in fields where precision in edge scenarios has a direct bearing on results, like identifying rare diseases in healthcare or spotting fraudulent credit card transactions in financial services.

Because of this, generative AI for synthetic data is not merely a development of conventional simulation methods; rather, it completely alters the way businesses prepare and scale datasets for machine learning.

Key Features of Effective Synthetic Data Generation Tools

When making technological decisions, organizations that are thinking about creating synthetic data should prioritize tools that can provide both easy accessibility and trust. 

1. Since fidelity is a top concern, generative AI tabular data must retain the original dataset's statistical characteristics despite being entirely synthetic. 
2. Given that companies usually require millions of synthetic data rows in order to adequately test systems, how fast it scales is also a crucial factor.
3. Domain-specific customizing is another crucial prerequisite. Certain domains, such as retail, telecommunications, and health, require particular features in the synthetic data. 
4. Every tool must be built with privacy in mind, making sure that no personal information leaks happen within synthetic outputs.

Challenges in Generating High-Fidelity Synthetic Tabular Data

While generative AI tabular data has some great advantages, generating it with high quality in mind comes with its own set of challenges. One of the most critical risks is overfitting, where models memorize the original data instead of creating new ones. This not only limits utility but also compromises the privacy that synthetic datasets are supposed to protect.

Another pressing concern is what experts call bias replication. If the underlying training data contains imbalances, such as underrepresentation of certain demographics or rare events, generative models will reproduce the same flaws. This will mean that the output is unfair with very low reliability.

Other than technical problems, organizational pushback also plays a role, with stakeholders often showing reluctance to trust artificial data for mission-critical decisions. Overcoming these barriers requires not only a better understanding of how Gen AI works but also a change in culture within organizations.

Here’s a summary of the challenges for quick understanding

• Overfitting - Models memorizing real data, reducing privacy and usefulness.
• Bias replication - Synthetic data inheriting biases from training datasets.
• Validation complexity - Difficulty in proving real-world implications.
• Trust barriers - Hesitation among decision-makers to rely on synthetic datasets.
• Resource demands - High investment needed for the generation of spotless synthetic data.

As these challenges are addressed, new technologies like agentic AI is all set to emerge, where systems trained on synthetic data will  go beyond analysis to autonomous decision-making

Best Practices in Synthetic Data Generation Using Generative AI

Organizations that succeed in their endeavor of creating synthetic datasets typically follow the best practices only. Here’s how they typically ensure the best outcomes.

• They begin by making sure that the real datasets used for training generative models are both representative and of high quality, since poor input inevitably produces poor synthetic output.
• Domain expertise should always be included to ensure that the outputs are realistic and usable.
• Combining synthetic data with real-world data usually leads to better results than using either one by itself.
• Transparency is also important. This means applying explainability frameworks so that stakeholders can understand how synthetic datasets are created and validated.

Organizations that use these practices find that synthetic data generation using generative AI not only speeds up processes but also improves the overall trust on their AI initiatives.

Integrating Synthetic Data Pipelines with Enterprise AI/ML Systems

For generative AI to work it needs to be blended within an organization’s data and AI infrastructure. Synthetic data must integrate automatically with data stored in data lakes and data warehouses to avoid the creation of segregated silos. Apart from just that, synthetic data must be automatically injected into the training bench where it is used alongside real data to refine the model.

Enterprises are looking at synthetic data generation as an ongoing activity for AI as opposed to a one-off task. Different synthetic datasets must be fine-tuned to make the data available according to the business needs and what degree of regulation that governs them. Embedding synthetic datasets into automated workflows enables enterprises to better respond to constant change and complexity in AI initiatives, ensuring long-term adaptability and ease of use.

Compliance, Privacy, and Governance in Synthetic Data Generation

The global regulations GDPR and HIPAA have made it increasingly difficult to collect and process real-world data. Synthetic data generation AI on the other hand, is an out-and-out compliance enabler. Since synthetic datasets have no direct links to personally identifiable information, the risks of data breaches and regulatory violations are next to none.

Even with these advantages, governance is still essential. Organizations need to clearly define frameworks that will govern how processes of generating, storing, and integrating synthetic data into workflows will work. Strong governance is always better as it makes sure regulatory requirements are met while also maintaining enterprise standards of quality.

As these governance frameworks are established, synthetic data generation techniques will increasingly help balance compliance while taking on AI as it arrives. 

Future Trends in Synthetic Data with LLMs and Deep Learning

The future trends in generative AI, especially synthetic data generation using deep learning, depend on the development of foundation models and large language models. Researchers are already looking into LLM synthetic data generation, where models designed for text are being updated to create structured tabular records. 

Future improvements will likely feature more accurate transformer models aimed at generative ai tabular data. We may also see adjustable systems that create balanced datasets in real time, along with a stronger focus on clear synthetic data generation processes that help build trust with stakeholders.

All these developments suggest a future where generating synthetic data for AI will be more automated and deeply integrated into enterprise analytics.

Synthetic Data: Soon to be Gold Standard in Predictive Analytics

We are currently witnessing a fundamental change in how businesses are handling information. A move away from real-world datasets and toward the mindful adoption of synthetic data generation is already on the cards for enterprises and mid-sized businesses. Organizations can now access expandable and affordable solutions that can speed up experimentation, while maintaining compliance. All thanks to the introduction of generative AI for tabular data.

When you integrate synthetic data pipelines into your workflows, you get to deliver concrete business outcomes such as faster development and reduced costs while staying confident about privacy. If you often face challenges like limited real-world data, slow experimentation, or regulatory constraints, synthetic data can give you the freedom to test and scale without the usual bottlenecks.

Don’t let data limitations slow you down. It’s 2025, if your business isn’t already AI-friendly, you’re already falling behind. Partner with Tredence to build the AI strategies that prepare you for synthetic data and future-ready growth.

FAQs

What is synthetic data generation in machine learning?

Synthetic data generation is the process of creating artificial datasets that are exactly like real-world data. In machine learning, it is used to address data scarcity, reduce biases, and improve privacy while maintaining performance. Generative models such as GANs, variational autoencoders, and transformers are capable of producing realistic  tabular data, image, and text data for training purposes.

What are the different types of synthetic data used in enterprise AI?

Enterprises apply synthetic data across multiple formats depending on their needs. Generative AI Tabular data is often used in industries such as finance, healthcare, and retail to support predictive analytics. Image, video, and text data are used in computer vision and natural language applications, while sensor and time-series data play an important role in IoT, robotics, and autonomous systems.

What are the limitations of synthetic data generation using deep learning?

The main challenges include overfitting, where models memorize original records instead of generating new ones, and bias replication, where existing biases in training data are carried forward. Validation is also difficult because maintaining realism, fairness, and diversity requires sophisticated evaluation methods.

Is synthetic data compliant with privacy regulations?

Yes. Properly generated synthetic data does not contain personally identifiable information, which allows it to comply with regulations such as GDPR and HIPAA. This makes it a safe option for innovation in sensitive domains like healthcare and finance while still preserving data utility.

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Editorial Team
Tredence