Uncovering Data Hidden Value: How Data is Being Assessed and Priced through FAME Marketplace
Large organizations like Motor Oil Hellas (MOH) have, over many years, collected data from various sources, including sensors monitoring components in large production lines. This data has primarily been used for its original purposes, such as managing facility infrastructure and refinery operations. The FAME project explores the substantial hidden value that data itself may hold- assessing, pricing, and trading it like other valuable assets.
MOH, as the leader of the industrial pilot (Pilot #7), along with its partners (INNOV, LXS, IBM, Eviden), focuses on investigating the additional insights this collected data provides and estimating its worth. This is achieved through a combination of tools organized within the FAME marketplace, where the data asset is described, priced, and potential buyers are identified. The platform thus provides the infrastructure for valuing data and facilitates potential trading.
Before assigning any value or initiating trading, data quality is carefully assessed. This includes examining the volume of data, its completeness, the locality and context of its origin, and other attributes such as accuracy and consistency. This detailed quality assessment offers stakeholders clear insights into the strengths and weaknesses of the data.
Following this, MOH, as the data provider, estimates a possible monetary value for the data. Since data differs from physical objects with fixed prices, valuation involves analysing several factors:
- Potential usage – Identifying data that can improve outcomes or resource allocation tends to increase its value.
- Current market demand – Evaluating whether there is market interest in the data helps guide pricing.
- Availability – Data that is unique and not publicly accessible generally holds greater value.
Machine learning capabilities are also leveraged to uncover hidden patterns, make predictions, and identify subtle quality differences that are difficult for humans to detect manually or would require significant resources. These systems help predict potential quality degradation and discover new applications that may enhance data value.
Pilot 7 represents an exciting exploration into a largely untapped opportunity by examining amassed organizational data assets. It aims to uncover value beyond the original collection purposes, assist strategic decision-making for future data infrastructure, and enable advanced analytics to generate insights and predictive tools.
Though the concept of “trading data” once seemed abstract, Pilot 7 applies familiar valuation principles alike to those used in other asset markets. This case could significantly influence how organizations manage their data—recognizing it not only as an operational resource but as a valuable business asset.
Author: Aristotelis Ntafalias (MOH)
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