If you are a FinTech company dealing with the difficulty of pricing proprietary market datasets — this project developed a multi-axis valuation methodology that uses cost, income, and market approaches to set fair prices.
Secure Platform for Trading and Monetizing Corporate Data Assets and AI Insights
Imagine a secure digital marketplace where companies can sell their data like stocks or digital art. Instead of just sharing raw files, they can sell high-value insights and analytics while keeping strict control over who sees what. It uses a digital ledger to ensure every transaction is fair, transparent, and automatically tracked.
What needed solving
Companies possess valuable data but cannot monetize it because they lack trust in buyers, struggle to price the data accurately, and fear losing control of their proprietary assets.
What was built
A federated data trading platform featuring AI-driven quality assessment, DLT-based data contracts, and a multi-axis valuation engine for pricing data assets.
Who needs this
Who can put this to work
If you are a logistics operator dealing with fragmented data silos across partners — this project developed a federated data management system that allows secure peer-to-peer transfer and usage monitoring.
If you are an IoT firm dealing with low-quality raw sensor data — this project developed AI-driven quality assessment and certification tools to turn raw data into high-value derivative assets.
Quick answers
How is the price of the data determined?
The platform uses a multi-axis methodology that recommends target values based on the cost approach, the income approach, and the market approach.
Can this be scaled across different industries?
Yes, it uses a Network of Data Models designed for cross-sector compatibility and semantic interoperability to ensure data can be understood across different fields.
Who owns the intellectual property and how is licensing handled?
Based on available project data, the system uses data NFTs and on-chain storage of multi-party data contracts to define clear ownership stakes and usage rights.
How does the system ensure the data is actually useful?
It implements AI-driven data quality assessment, observability, and certification mechanisms to ensure data meets required standards.
What is the timeline for implementation?
The project is active from 2023-01-01 to 2026-06-30, indicating the technology is currently in development and testing phases.
Who built it
The consortium is heavily weighted toward commercial application, with 21 industry partners (68% of the group) and 11 SMEs. This strong industrial presence, combined with 6 research centers and 3 universities across 11 countries, suggests the project is driven by market demand rather than pure academic curiosity.
Fraunhofer Gesellschaft zur Förderung der Angewandten Forschung
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