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

arXiv: https://arxiv.org/abs/2212.02622 · HTML: https://mlsanity.com/paper/2212.02622/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 125 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 20 of 20)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [DAG-GNN: DAG Structure Learning with Graph Neural Networks](https://mlsanity.com/paper/1904.10098/) (2019)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [VAE with a VampPrior](https://mlsanity.com/paper/1705.07120/) (2017)
- [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification](https://mlsanity.com/paper/1801.02385/) (2018)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [Synthetic Data for Deep Learning](https://mlsanity.com/paper/1909.11512/) (2019)
- [Semi-Amortized Variational Autoencoders](https://mlsanity.com/paper/1802.02550/) (2018)
- [Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications](https://mlsanity.com/paper/2005.02144/) (2020)
- [RarePlanes: Synthetic Data Takes Flight](https://mlsanity.com/paper/2006.02963/) (2020)
- [Data augmentation using synthetic data for time series classification with deep residual networks](https://mlsanity.com/paper/1808.02455/) (2018)
- [Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks](https://mlsanity.com/paper/2007.07124/) (2020)
- [On the Applicability of Synthetic Data for Face Recognition](https://mlsanity.com/paper/2104.02815/) (2021)
- [Measuring the quality of Synthetic data for use in competitions](https://mlsanity.com/paper/1806.11345/) (2018)
- [Wikipedia Reader Navigation: When Synthetic Data Is Enough](https://mlsanity.com/paper/2201.00812/) (2022)
- [A Taxonomy and Archetypes of Business Analytics in Smart Manufacturing](https://mlsanity.com/paper/2110.06124/) (2021)

## Built on by (most cited first, 2 of 2)

- [Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics](https://mlsanity.com/paper/2401.06883/) (2024)
- [Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities](https://mlsanity.com/paper/2405.20959/) (2024)

## 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.