Towards a Taxonomy for the Use of Synthetic Data in Advanced Analytics
This work addresses data scarcity issues for organizations in business analytics, but it is incremental as it focuses on taxonomy development rather than new methods.
The paper tackles the problem of limited data availability hindering advanced analytics by proposing a taxonomy for using synthetic data, identifying application scenarios to assess adoption and reveal missed opportunities.
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.