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

TL;DR

This paper surveys current methods for synthesizing tabular data, analyzing user needs, tool capabilities, and challenges, and provides a decision guide to help select appropriate tools while highlighting research gaps.

Contribution

It offers a comprehensive survey of TDS tools, defines user requirements, evaluates tool performance, and introduces a decision guide to aid tool selection and identify research gaps.

Findings

Evaluation of 36 TDS tools against user requirements

Identification of key challenges in tabular data synthesis

Development of a decision guide for tool selection

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.