The Economic Value of Data in Transfer Pricing

The digital economy has brought about a fundamental transformation in the way multinational enterprises create value. While traditional business models relied on tangible assets and intellectual property rights, such as patents and trademarks, modern companies increasingly depend on data to improve decision-making, develop products, enhance operational efficiency, and elevate customer experience. As data becomes increasingly important as a key source of profitability, transfer pricing rules face growing challenges in determining how the returns generated from data should be allocated among related entities within a group.

Although the Organisation for Economic Co-operation and Development (OECD) Transfer Pricing Guidelines recognize a wide range of intangible assets, they do not provide sufficient guidance on the treatment of commercially valuable data. Unlike traditional intangible assets, data is generated continuously through business activities, is often produced across multiple jurisdictions, and its value depends on its ongoing collection, analysis, updating, and use. This raises important questions regarding data ownership, valuation mechanisms, and the allocation of profits generated from data within multinational groups.

The Distinct Economic Nature of Data

Data differs from traditional intangible assets in several key respects. While patents and trademarks are developed through specific processes and enjoy legal protection, data is generated continuously through customer interactions, operational processes, and digital platforms. Its value also increases as data accumulates and is analyzed, particularly when artificial intelligence and advanced data analytics technologies are employed.

Another important characteristic is that data can be used simultaneously by multiple entities within a group without being consumed or losing value. Marketing, finance, logistics, and research and development departments in different countries may all rely on the same database. In addition, large, high-quality databases increase in value over time as a result of continuous updating and network effects, making data a dynamic economic asset rather than merely a static intellectual asset.

Data within the OECD Transfer Pricing Framework

Data may be viewed within the transfer pricing framework as one of the most important sources of value in modern digital business models, while at the same time being one of the most difficult assets to measure and allocate for tax purposes. The OECD Guidelines are based on the arm’s length principle, meaning that transactions between related parties should be priced as if they were conducted between independent parties under comparable circumstances. This requires accurately delineating the actual transaction by analyzing the contractual terms, functions performed, assets used, and risks assumed by each party within the multinational group.

The value of data is not limited to its mere collection or legal possession. Rather, it often arises through an integrated chain of activities that includes collecting data, cleaning it, classifying it, linking it with other sources, analyzing it, protecting it, and then using it to improve products, target customers, develop algorithms, or sell analytical insights. OECD studies on the value of data indicate that digitalization has enabled the separation of the locations where data is collected, aggregated, analyzed, stored, and exploited, so that each stage may take place in a different country. This makes determining where value is created more complex.

Accordingly, applying transfer pricing rules to data requires moving beyond the question of “Who legally owns the data?” to the deeper question of “Who creates the economic value associated with the data?” The parent company may be the legal owner of the database or digital platform, but local subsidiaries in market jurisdictions may be the entities that collect customer data, bear the costs of compliance with privacy laws, and manage the commercial relationship with users. Conversely, technology or analytics centers may play a fundamental role in transforming raw data into a valuable asset through artificial intelligence models, predictive analytics, and the development of processing and storage infrastructure.

This analysis becomes particularly important when applying the DEMPE framework used in the transfer pricing of intangible assets, namely the functions of development, enhancement, maintenance, protection, and exploitation. The OECD Guidelines emphasize that members of a group should receive compensation that corresponds to the functions they perform, the assets they use, and the risks they assume in developing, enhancing, maintaining, protecting, and exploiting intangible assets. Legal ownership of an intangible asset alone is not sufficient to retain the economic returns if other entities perform key functions or control the relevant risks.

Applying this to data, it may be said that an entity that merely collects data may be entitled to a routine return if it performs a limited operational function and does not assume significant risks. By contrast, an entity that sets the data strategy, develops algorithms, controls data quality, or assumes cybersecurity and regulatory compliance risks may be entitled to a greater return because it contributes to the creation of core value. Likewise, if data only becomes valuable after being combined with other data or analyzed using technology owned by the group, the return should not be attributed entirely either to the entity collecting the data or to the legal owner alone.

Several economic risks must therefore be incorporated into the transfer pricing analysis, including privacy breach risks, information security risks, data quality risks, non-compliance with local laws, and the risk that analytical models or algorithms fail to achieve commercial results. An entity that actually assumes these risks and has the ability to manage them and make decisions related to them should not be treated merely as an entity performing routine services. However, if it formally bears the costs without having the authority to control the risks, it may be inappropriate to grant it a significant economic return.

In practice, the appropriate pricing method may vary depending on the nature of each party’s contribution. If a subsidiary provides limited data collection or technical support services, the cost-plus method with an appropriate margin may be more suitable. However, if the data or analytics constitute a unique asset for which it is difficult to find comparable independent transactions, the profit split method may be more appropriate, especially where value arises from integrated contributions by several entities within the group. In other cases, if comparable independent licenses or transactions exist for the sale or provision of similar databases, the comparable uncontrolled price method may be used, taking into account differences in quality, exclusivity, scope of use, and legal restrictions.

Transfer Pricing Challenges

The main challenge in the transfer pricing of data is not limited to determining an independent price for the data itself. Rather, it lies in accurately mapping the digital value chain within the multinational group. This requires identifying the roles performed by different entities, such as collecting, processing, and analyzing data, developing the technological tools necessary to exploit it, as well as identifying who assumes privacy and cybersecurity risks and who has the right to commercially exploit the results.

This analysis is increasingly important because the legal or contractual ownership of data does not necessarily reflect the entity that created its economic value. Value may arise from multiple and integrated contributions, such as improving data quality, combining it with other sources, or using it to develop algorithms and predictive models. Therefore, the allocation of profits should be based on the substantive economic functions and actual risks borne by each entity, not merely on the legal possession of the data.

The allocation of returns generated from data is one of the most significant practical challenges in transfer pricing, due to the scarcity of comparable independent transactions and the difficulty of relying on cost as an indicator of true value. In cases where several entities participate in creating value, profit split methods, particularly the residual profit split method, may be more appropriate because they reflect the shared and complex nature of data-driven digital business models.

Conclusion

Data has become one of the most important economic assets relied upon by multinational enterprises, creating a need to develop transfer pricing rules in a manner that reflects its unique economic characteristics. Although the current principles provide a suitable general framework, they do not sufficiently address the complexities of data-based business models. Therefore, future transfer pricing analysis should focus not only on the legal ownership of data, but also on the functions performed in collecting, managing, and exploiting it. Recognizing data as an independent economic intangible asset would contribute to a fairer and more consistent allocation of profits, in line with the arm’s length principle and the economic reality of the digital economy.

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