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Synthetic Data generation

YData Fabric's Synthetic data Generation capabilities leverages the latest generative models to create high-quality artificial data that replicates real-world data properties. Regardless it is a table, a database or a tex corpus this powerful capability ensures privacy, enhances data availability, and boosts model performance across various industries. In this section discover how YData Fabric's synthetic data solutions can transform your data-driven initiatives.

What is Synthetic Data?

Synthetic data is artificially generated data that mimics the statistical properties and structure of real-world data without directly copying it. It is created using algorithms and models designed to replicate the characteristics of actual data sets. This process ensures that synthetic data retains the essential patterns and relationships present in the original data, making it a valuable asset for various applications, particularly in situations where using real data might pose privacy, security, or availability concerns. It can be used for:

  • Guaranteeing privacy and compliance when sharing datasets (for quality assurance, product development and other analytics teams)
  • Removing bias by upsampling rare events
  • Balancing datasets
  • Augment existing datasets to improve the performance of machine learning models or use in stress testing
  • Smartly fill in missing values based on context
  • Simulate new scenarios and hypothesis

The benefits of Synthetic Data

Leveraging synthetic data offers numerous benefits:

  • Privacy and Security: Synthetic data eliminates the risk of exposing sensitive information, making it an ideal solution for industries handling sensitive data, such as healthcare, finance, and telecommunications.
  • Data Augmentation: It enables organizations to augment existing data sets, enhancing model training by providing diverse and representative samples, thereby improving model accuracy and robustness.
  • Cost Efficiency: Generating synthetic data can be more cost-effective than collecting and labeling large volumes of real data, particularly for rare events or scenarios that are difficult to capture.
  • Testing and Development: Synthetic data provides a safe environment for testing and developing algorithms, ensuring that models are robust before deployment in real-world scenarios.

Synthetic Data in Fabric

YData Fabric offers robust support for creating high-quality synthetic data using generative models and/or through bootstrapping. The platform is designed to address the diverse needs of data scientists, engineers, and analysts by providing a comprehensive set of tools and features.

Data Types Supported:

YData Fabric supports the generation of various data types, including:

  • Tabular Data: Generate synthetic versions of structured data typically found in spreadsheets and databases, with support for categorical, numerical, and mixed data types.
  • Time Series Data: Create synthetic time series data that preserves the temporal dependencies and trends, useful for applications like financial forecasting and sensor data analysis.
  • Multi-Table or Database Synthesis: Synthesize complex databases with multiple interrelated tables, maintaining the relational integrity and dependencies, which is crucial for comprehensive data analysis and testing applications.
  • Text Data: Produce synthetic text data for natural language processing (NLP) tasks, ensuring the generated text maintains the linguistic properties and context of the original data.