Extending GouDa: Generation of Universal Datasets with (and without) Errors for Data Quality Benchmarking

2026-07-22

AI Summary

This work addresses the challenge that existing synthetic data generation methods struggle to simultaneously ensure realism, support multiple data formats, enable controlled error injection, and produce corresponding error-free ground-truth data. To this end, we propose GouDa, a novel data generator that unifies support for both relational and non-relational data formats within an extensible framework. GouDa leverages customizable attribute distributions and structured error models to flexibly generate high-fidelity synthetic datasets. Its key innovation lies in the synchronized production of data instances containing controllable errors alongside their error-free counterparts, significantly enhancing the utility of synthetic data in edge-case scenarios and providing robust support for data quality evaluation and machine learning tasks.

Abstract

Synthetic data is extremely important in areas such as data quality, data cleaning, and machine learning. It enables the analysis of use cases in which real data is insufficient, unavailable, or distorted. However, generating synthetic data also presents challenges: The data must be as realistic as possible, but at the same time cover edge cases. It must be possible to insert controlled errors, and at the same time, an error-free version of the data is usually required. Additionally, it is necessary to consider numerous data formats, such as tabular data, but also NoSQL data models. To this end, we present our data generator GouDa. GouDa precisely meets these requirements - it is suitable for different data formats, enables the controlled insertion of errors, and generates ground truth. A wide range of different generation functions and the option to add your own lists of possible attribute values allow the generation of realistic data that covers many different use cases.

Problem

• synthetic data
• data quality
• error injection
• ground truth
• data formats

Innovation

• synthetic data generation
• data quality benchmarking
• controlled error injection
• ground truth dataset
• multi-format data support