mlsanityData to 7 Oct 2026

Towards a Taxonomy for the Use of Synthetic Data in Advanced Analytics

arXiv 2212.02622 ↗First posted 5 Dec 2022cs.LG cs.AIcited by 2

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Abstract (arXiv)

The proliferation of deep learning techniques led to a wide range of advanced analytics applications in important business areas such as predictive maintenance or product recommendation. However, as the effectiveness of advanced analytics naturally depends on the availability of sufficient data, an organization's ability to exploit the benefits might be restricted by limited data or likewise data access. These challenges could force organizations to spend substantial amounts of money on data, accept constrained analytics capacities, or even turn into a showstopper for analytics projects. Against this backdrop, recent advances in deep learning to generate synthetic data may help to overcome these barriers. Despite its great potential, however, synthetic data are rarely employed. Therefore, we present a taxonomy highlighting the various facets of deploying synthetic data for advanced analytics systems. Furthermore, we identify typical application scenarios for synthetic data to assess the current state of adoption and thereby unveil missed opportunities to pave the way for further research.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 125 references that are in the map. First posted on arXiv 5 Dec 2022.

Builds on

20 of 125 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2024: 2.

2 papers.

  1. 2024Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analyticscited by 18
  2. 2024Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilitiescited by 8