One of the key tools which enables data trading and monetization in FAME is the Pricing Advisory Tool (PAT). PAT is a web based tool which provides price recommendations for offering data assets on FAME. The PAT leverages transactional data along with textual data and questionnaire based data associated with data assets in Federated Data Assets Catalogue (FDAC) of FAME to perform similarity analysis with the focal data asset in two phases. This structured approach enables focused asset comparisons for price recommendation.
On November 27, 2024, Jana Peliova from University of Economics in Bratislava, Slovakia and Samrat Gupta from University of Agder, Norway (and Indian Institute of Management Ahmedabad), discussed the strategies for pricing data assets within the FAME Webinar Series. This webinar provided an engaging platform for discussing the complexities and innovations surrounding pricing of data assets in today’s AI-driven landscape.
Key Aspects of Strategies for Pricing Data Assets in the Era of AI
In this era marked by rapid advancements in AI, pricing data assets has become increasingly complex. The webinar focused on several key aspects:
Data as a new factor of production: The webinar began with a discussion on how data has emerged as a new factor of production (with reference to traditional factors of production namely land, labor and capital) emphasizing its significance in driving digital economy and decision making.
Challenges in pricing of data assets: The webinar explored why it is difficult to decide an appropriate price for a data asset. The novel characteristics of data assets such as non-depletion, heterogeneity of value of data, non-exclusiveness, intangibility, and value uncertainty which prevent the development of pricing methods and data marketplaces were discussed.
Market Dynamics: The webinar discussed how digital transformation and a surge in online platforms is necessitating new pricing strategies (such as value-based pricing, competitive benchmarking amongst others) and business models (such as freemium model, subscription model, amongst others) that are flexible and responsive to real-time data.
Role of AI in pricing of data assets: The webinar also focused on how the advancements in AI and deeper insights into data utility and market demand resulting from the influence of AI on market behaviors and consumer expectations can enhance pricing techniques.
FAME’s analytical approach for pricing data assets: Finally, the two-phase approach for pricing advisory tool (PAT) of FAME was discussed. The first phase uses sentence based BERT (SBERT) to enable text similarity computations by transforming text into dense vector representations that capture semantic meaning in context. Subsequently, in the second phase, similarity analysis tool (SAT) which employs vector space modelling and a composite similarity metric for heterogeneous data typed responses on a questionnaire (based on the notions of hamming distance, cosine distance) are used to match the data assets. The transactional history of data assets is synergistically used along with the aforementioned two-phase approach for recommending price of a focal data asset.
An integrative understanding of aforementioned aspects is crucial to navigate the challenges and opportunities presented by AI in pricing of data assets.
Overview of the Webinar Experience
The webinar was structured in two parts wherein the first part focused on theoretical aspects while the second part detailed the technical aspects of pricing data assets. Overall, the webinar provided an engaging environment and attracted researchers and practitioners who were keen to deepen their understanding of pricing data assets. Participants expressed appreciation for the discussion of practical examples and theoretical concepts, which illustrated strategies for monetizing data assets in an age dominated by AI technologies.
The insights shared during the webinar were directly aligned with FAME’s mission to create a decentralized marketplace that fosters trust and collaboration among stakeholders in embedded finance. By focusing on effective pricing strategies for data assets, FAME can enhance its framework for facilitating secure and efficient transactions between data providers and consumers. The insights from the webinar also align with FAME’s goal of decentralizing data management. By developing a data-driven pricing strategy, FAME can ensure fair and equitable engagement between data providers and consumers within its marketplace. In conclusion, this webinar not only highlighted essential strategies for pricing data assets but also reinforced FAME’s dedication to fostering a trusted and innovative ecosystem for embedded finance.
We encourage you to watch the recorded session here to further explore these insights and consider their implication for your work within this exciting field:
Author: Samrat Gupta
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