New Dataset for Data Pricing in Marketplaces Released

The increasing demand for high-quality data in machine learning has led to the proliferation of data marketplaces like AWS Marketplace and Databricks. However, accurately pricing data products remains a significant challenge due to their unique characteristics, such as near-zero marginal replication costs and unpredictable revenue generation. Traditional economic pricing models often fall short in this context. To address this, a new dataset called DaDaDa has been created. It compiles metadata for 16,147 data products sourced from nine prominent global data marketplaces. This comprehensive dataset is intended to facilitate the development of machine learning models for data pricing, thereby establishing much-needed benchmarks for valuing new data offerings. Beyond pricing, DaDaDa can also support other crucial tasks within data markets, including data product classification and retrieval. Initial experiments and a prototype demonstrate its effectiveness across these applications, providing a valuable resource for researchers and practitioners in the evolving data economy.