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Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities

Maria F. Davila R., Sven Groen, Fabian Panse, Wolfram Wingerath

TL;DR

The paper tackles the challenge of selecting appropriate tabular data synthesis (TDS) tools in the face of data scarcity and privacy concerns by formalizing 12 functional/non-functional requirements, surveying 36 state-of-the-art tools, and delivering a practical decision guide. It categorizes models into imputation, sampling, discriminative, generative (shallow and deep), probabilistic-database-based, and hybrid approaches, highlighting their capabilities and trade-offs, particularly regarding integrity constraints and temporal dependencies. The authors provide two evaluation matrices and identify key research gaps, such as preserving integrity constraints and inter-table correlations across multi-table schemas, and propose a benchmarking-driven path toward more reliable fitness-for-use assessments. Overall, the work offers a structured framework for users to assess TDS tools for specific use cases and guides future benchmarking efforts to improve practical utility in real-world data synthesis tasks.

Abstract

In an era of rapidly advancing data-driven applications, there is a growing demand for data in both research and practice. Synthetic data have emerged as an alternative when no real data is available (e.g., due to privacy regulations). Synthesizing tabular data presents unique and complex challenges, especially handling (i) missing values, (ii) dataset imbalance, (iii) diverse column types, and (iv) complex data distributions, as well as preserving (i) column correlations, (ii) temporal dependencies, and (iii) integrity constraints (e.g., functional dependencies) present in the original dataset. While substantial progress has been made recently in the context of generational models, there is no one-size-fits-all solution for tabular data today, and choosing the right tool for a given task is therefore no trivial task. In this paper, we survey the state of the art in Tabular Data Synthesis (TDS), examine the needs of users by defining a set of functional and non-functional requirements, and compile the challenges associated with meeting those needs. In addition, we evaluate the reported performance of 36 popular research TDS tools about these requirements and develop a decision guide to help users find suitable TDS tools for their applications. The resulting decision guide also identifies significant research gaps.

Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities

TL;DR

The paper tackles the challenge of selecting appropriate tabular data synthesis (TDS) tools in the face of data scarcity and privacy concerns by formalizing 12 functional/non-functional requirements, surveying 36 state-of-the-art tools, and delivering a practical decision guide. It categorizes models into imputation, sampling, discriminative, generative (shallow and deep), probabilistic-database-based, and hybrid approaches, highlighting their capabilities and trade-offs, particularly regarding integrity constraints and temporal dependencies. The authors provide two evaluation matrices and identify key research gaps, such as preserving integrity constraints and inter-table correlations across multi-table schemas, and propose a benchmarking-driven path toward more reliable fitness-for-use assessments. Overall, the work offers a structured framework for users to assess TDS tools for specific use cases and guides future benchmarking efforts to improve practical utility in real-world data synthesis tasks.

Abstract

In an era of rapidly advancing data-driven applications, there is a growing demand for data in both research and practice. Synthetic data have emerged as an alternative when no real data is available (e.g., due to privacy regulations). Synthesizing tabular data presents unique and complex challenges, especially handling (i) missing values, (ii) dataset imbalance, (iii) diverse column types, and (iv) complex data distributions, as well as preserving (i) column correlations, (ii) temporal dependencies, and (iii) integrity constraints (e.g., functional dependencies) present in the original dataset. While substantial progress has been made recently in the context of generational models, there is no one-size-fits-all solution for tabular data today, and choosing the right tool for a given task is therefore no trivial task. In this paper, we survey the state of the art in Tabular Data Synthesis (TDS), examine the needs of users by defining a set of functional and non-functional requirements, and compile the challenges associated with meeting those needs. In addition, we evaluate the reported performance of 36 popular research TDS tools about these requirements and develop a decision guide to help users find suitable TDS tools for their applications. The resulting decision guide also identifies significant research gaps.
Paper Structure (26 sections, 3 figures, 5 tables)

This paper contains 26 sections, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Purposes of Tabular Data Synthesis
  4. Tabular Data Synthesis Challenges
  5. User Needs: TDS Requirements
  6. TDS Tool Capabilities
  7. Imputation-based Models
  8. Sampling Models
  9. Discriminative Models
  10. Generative Models
  11. Shallow Generative Models
  12. Bayesian Networks
  13. Markov Random Fields
  14. Hidden Markov Models
  15. Deep Generative Models
  16. ...and 11 more sections

Figures (3)

  • Figure 1: Classification of the data-driven TDS models included in our study.
  • Figure 2: Taxononomy of metrics for evaluating synthetic tabular data identified in our research.
  • Figure 3: Decision guide resulting from the assessment of 36 TDS tools, based on their reported performance on the functional requirements identified in our work.