Top 8 Synthetic Data Generation Tools for Smarter Market Modeling and Forecasting
For enterprises large and small, smart market modeling and forecasting are essential for making informed strategic decisions.
If you want to launch a new product, or you need to optimize inventory levels across multiple regions, it becomes much harder to predict demand accurately and allocate resources effectively without the right models in place. These insights give your business the ability to adapt and flourish, so any opportunity to make them more reliable is worth considering.
Synthetic data generation helps with exactly that. By creating diverse and realistic datasets, it allows market and forecasting models to learn from a wider range of scenarios and edge cases than historical data usually provides. This, in turn, helps models anticipate trends and support better-informed decisions.
So what synthetic data generation tools are out there, and what makes them useful for market modeling and forecasting?
1. K2view
K2view Synthetic Data Generation tools are a standalone solution that manage the entire synthetic data lifecycle, including source data extraction, subsetting, pipelining, and synthetic test data operations. For market modeling and forecasting, this means you can connect to operational systems, pull the necessary business data, and generate realistic synthetic market records without breaking referential integrity between entities.
K2view uses both GenAI and rules-based data generation methods, supported by a unique architecture that maintains referential integrity. It creates a schema that serves as a blueprint for the data model, so customers, products, locations, orders, and other market entities all stay correctly linked as data is synthesized. That is especially valuable when forecasting depends on multi-table relationships, such as promotions tied to specific regions, channels, or segments.
The platform also includes dozens of built-in masking and anonymization methods, which means you can safely blend masked production data with synthetic data when preparing training sets. Combined with seamless integration into CI/CD pipelines, K2view can generate and refresh synthetic datasets as part of your regular release and MLOps cycles, so forecasting models are always tested and trained on up-to-date, policy-compliant data.
K2view is best suited to enterprises with large, complex data environments that need self-service provisioning of data blended from multiple, heterogeneous sources. Configuration and deployment require planning, and it is generally not aimed at very small businesses. For organizations that treat forecasting and market modeling as critical, ongoing capabilities, K2view provides a comprehensive platform from extraction and ingestion through to synthetic data output.
2. Mostly AI
Mostly AI generates synthetic datasets that mirror real data while protecting privacy. It focuses on tabular and multi-relational data, and its interface supports relatively fast dataset creation for AI and analytics use cases.
For market modeling and forecasting, Mostly AI can be used to create synthetic demand, pricing, and customer datasets that follow the same distributions as real data. This helps expose models to a wider variety of patterns than historical records alone, without directly using sensitive transactional information. Features such as privacy-safe generation and de-identification, fidelity metrics comparing real and synthetic data, and API-based workflows contribute to this.
It is generally a better fit for mid-size to large companies that already have data science teams in place and need an additional tool to expand training data. Control over complex hierarchical data can be limited, and it is less flexible for intricate relationships, which may require additional modeling work around the platform for advanced forecasting scenarios.
3. Gretel
Gretel, particularly through Gretel Workflows, offers a developer-focused platform for embedding synthetic data generation directly into pipelines. It supports pipeline scheduling and automation, structured and unstructured data, and no-code/low-code workflow options, with an emphasis on privacy-safe dataset creation.
In market modeling contexts, Gretel can synthesize event streams or transactional data that resemble user behavior and market activity, while masking or replacing sensitive details. This can be useful for building benchmark datasets to test how forecasting models behave under different activity patterns.
Gretel is best suited to engineering teams that want to integrate synthetic data into CI/CD, Dev/Test, and ML workflows. It relies heavily on cloud infrastructure and tends to be oriented toward developers, so organizations without strong technical resources may find it less convenient and may need complementary tools to cover broader data management needs.
4. Hazy
Hazy, now part of SAS Data Maker, focuses on privacy-preserving synthetic data generation for regulated industries. It emphasizes differential privacy and anonymization, with enterprise-grade integration and support, and can be deployed on-premises or in the cloud.
For market forecasting, Hazy can simulate complex relational structures involving customers, orders, returns, and promotions, while keeping compliance needs in view. This is relevant when forecasting models require multiple linked tables and when organizations must adhere to strict regulatory standards around data use.
The platform can automate workflows that combine masking and synthetic generation while preserving referential integrity. However, setup can be complex, and the cost and effort involved are typically easier to justify for larger enterprises. Smaller teams may find the implementation overhead high relative to their market modeling requirements.
5. YData
YData Fabric unifies data profiling and synthetic data generation to enhance AI model performance, and supports tabular, relational, and time-series data. Its feature set includes multi-type data generation, automated data quality assessment, integrated ML pipeline workflows, and both no-code and SDK options.
In market modeling and forecasting, YData can be used to create synthetic time-series that represent alternative market conditions, demand curves, or seasonal effects. It can also help identify and correct data quality issues before models are trained.
YData is generally best suited to firms building machine learning models across multiple domains, where teams can take advantage of its data profiling and ML integration features. It requires data science expertise, and it may not align with every data privacy regulation without additional configuration, so teams need to be comfortable managing governance and compliance on top of the platform.
6. GenRocket
GenRocket began as a synthetic test data solution for software development and QA and has expanded to support analytics and model training. It uses design-driven data generation and integrates with pipelines so data creation can be part of automated processes.
For market modeling and forecasting, GenRocket can generate synthetic datasets that align with predefined schemas and business rules, and then feed those into models as part of CI/CD or MLOps workflows. This is helpful when forecasting models are updated frequently and need consistent test data for each new version.
Because its origins and strengths are in test data, adapting GenRocket to forecasting scenarios may require additional design and configuration effort from technical teams. It is more appropriate for organizations that already rely on pipeline-based testing and want to extend that approach to include synthetic data for analytics.
7. Syntho
Syntho provides a self-service engine for generating privacy-compliant synthetic data. It aims to create realistic datasets for analytics, testing, and model development, with a focus on maintaining statistical properties while removing direct identifiers.
In forecasting use cases, Syntho can be used to include both common and less frequent scenarios in training data. When planning for markets where rare events, such as sudden demand shocks or unusual sales patterns, have outsized impact, Syntho can help simulate those patterns so models do not only learn from typical behavior.
Using Syntho effectively assumes that teams can define the events and distributions they want to emphasize. It is more suitable for organizations with technical and analytical expertise that want a dedicated generation engine rather than a broader data lifecycle platform.
8. SDV
SDV (Synthetic Data Vault) is an open-source Python library for generating tabular, relational, and time-series synthetic data. It supports multiple generative models, offers relational data and constraint support, and integrates naturally with Python-based data science workflows.
For market modeling and forecasting, SDV allows smaller or more technical teams to experiment with synthetic datasets that reproduce complex correlations and temporal patterns. It is flexible and cost-effective, which makes it practical for rapid experimentation or research projects.
However, SDV requires manual setup and ongoing maintenance, and it does not include the enterprise-level features or support that larger organizations may expect. It is generally best for smaller data science teams or academic environments, rather than as a primary platform for enterprise forecasting and market modeling.
Conclusion
Market modeling and forecasting rely on data that reflects both everyday behavior and less frequent, high-impact events. Synthetic data generation helps close gaps in historical records, creating richer training and testing datasets that better represent the range of conditions your models may face.
Across the tools in this list, K2view stands out for its end-to-end coverage of the synthetic data lifecycle, from extraction and masking to referentially intact generation and CI/CD integration. The other vendors can play useful roles in specific scenarios, especially when teams have particular technical preferences or constraints. As synthetic data moves from niche innovation to operational necessity, the choice of platform will increasingly influence how effectively organizations can plan, forecast, and adapt to changing markets.