DaDaDa: A Dataset for Data Pricing in Data Marketplaces
High-quality data drives machine learning, but pricing data products is challenging due to near-zero marginal cost and unpredictable revenue. Traditional pricing methods fail; the sales comparison approach lacks benchmarks. Researchers introduce DaDaDa, the first dataset for data product pricing, containing metadata on 16,147 products from 9 major marketplaces. It enables training pricing models, establishing benchmarks, and supports classification and retrieval tasks. Experiments show effectiveness.
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[Submitted on 13 Jun 2026]
Title:DaDaDa: A Dataset for Data Pricing in Data Marketplaces
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Abstract:High-quality data drives machine learning advances across industries. Recognizing the value of data, data transactions are increasingly common, giving rise to many data marketplaces, e.g., AWS Marketplace, Databricks, and Datarade. However, determining the appropriate prices for data products remains a significant challenge due to the unique properties of data products. Traditional pricing methods in economics can be categorized into the cost approach, the income approach, and the sales comparison approach. The cost approach fails in data pricing due to near-zero marginal cost from data replication, and the income approach fails due to inherently unpredictable data revenue. The sales comparison approach remains viable, yet its application is hindered by the absence of standardized pricing benchmarks for data products across marketplaces. To address this challenge, we introduce \texttt{DaDaDa}, the first dataset for data product pricing, containing metadata for 16,147 data products from 9 major data marketplaces worldwide. \texttt{DaDaDa} enables the training of pricing models, thereby establishing price benchmarks for new data products. In addition, \texttt{DaDaDa} can be utilized for other important tasks in data markets, such as data product classification and retrieval. Experiments and a retrieval prototype demonstrate the effectiveness of \texttt{DaDaDa} for pricing, classification, and retrieval of data products. The dataset and code are available at this https URL.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.08785 [cs.LG]
(or arXiv:2607.08785v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.08785
arXiv-issued DOI via DataCite
Submission history
From: Jinfei Liu [view email] [v1] Sat, 13 Jun 2026 12:19:37 UTC (1,161 KB)
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