Data is the new Oil, but how much is it worth?

In today's digital world, data is often called the "new oil." It powers our technology, from the apps on our phones to the decisions made by large companies and even governments. But how does one price data? Is it the same as pricing everyday products like a bag of chips or a concert ticket?

Researchers from the Indian Institute of Technology Kanpur (IIT Kanpur) and the Indian Institute of Science (IISc) are diving into this question. In a new study, they have created a framework so data can be fairly traded and valued, similar to physical products

Data isn't like a regular product. It's intangible, meaning you can't physically touch or see it. Yet, it holds immense value because of the insights and information it can provide. Think about the way Netflix suggests movies for you to watch based on your past viewing habits. However, while data is incredibly useful, it's non-rivalrous, meaning multiple people can use it at the same time without depleting it. This unique characteristic makes pricing data a tricky affair. Unlike selling apples, where the more apples you sell, the fewer you have, data doesn't run out, and it can even generate more value the more it's used.

Based on current literature, the researchers identified five distinct themes based on which data can be priced; these are data characteristics-based pricing, which depends on the nature of the data, such as how detailed it is or how it's formatted; quality-based pricing, which involves pricing data based on its accuracy, completeness, and reliability; query-based pricing, where the cost depends on how many questions the data is expected to answer; privacy-based pricing which considers how personal or sensitive the data is; and organizational value-based pricing where the data gets its price from the value it brings to a company or organisation.

The researchers also identified eight key factors influencing these pricing strategies: customer needs, the customer's perceived value, market maturity, market structure, data usability, data quality, the seller's reputation, and the seller's aims. The researchers then developed a framework for data trading, which is a comprehensive model that outlines the factors that influence data pricing. It is specifically designed to facilitate fair and efficient data exchange and takes into account key dimensions and attributes that impact how data should be priced and traded within a marketplace.

Although the researchers don't explicitly define a new technological platform, the framework suggests attributes and dimensions that such a platform would need to incorporate to ensure fair trading, including dynamic pricing mechanisms, transparency and standardisation,  and seller-buyer matchmaking.

However, the research is not without its limitations. One significant challenge is the complex and dynamic nature of data marketplaces, which are still evolving. The study provides a good foundation, but it's primarily based on qualitative analysis. It lacks detailed quantitative analysis, which might offer a more precise understanding of how these pricing models operate in real-world scenarios. The study also limits itself by not including the complex nuances of exchange/trading, like customer information availability or market competition intensity, which needs to be addressed for a comprehensive framework for data trading

Future research could consider a wider database with diverse data to understand the fine print in data pricing and trading. Furthermore, as data privacy becomes more crucial, understanding how to integrate privacy concerns into data pricing models will be key. Real-world tests and case studies comparing pricing strategies under varying conditions and in different market maturities could provide additional insights.

The framework sets a theoretical basis for how data trading platforms should operate, emphasising the vital role of clear pricing methodologies that consider diverse factors. It advocates for the valuation of data as a strategic asset, acknowledging complexities like privacy concerns, which also need integration within these systems. This framework could guide tech companies or governmental bodies looking to establish or regulate data exchanges, ensuring data is traded with fairness and transparency.

This research news was partly generated using artificial intelligence and edited by an editor at Research Matters