On 10 December last year, The Times published an article about the treasure trove of customer data gathered by Tesco and Sainsbury’s through their respective Clubcard and Nectar schemes. 

Such data reflects customer preferences and behaviours, and is a valuable asset that can be sold to brands for targeted online advertising. It is an example of the burgeoning value-creation model driven by internally generated data resources, which has significantly influenced corporations with data-driven business models. 

This surge in value creation is met with a lag in corresponding accounting treatment, presenting challenges due to complexities in mechanisms for data to yield economic benefits, ownership rights and measurement reliability of data.

In addressing the challenges brought by internally generated intangibles, two main viewpoints emerge: enhanced disclosure and enhanced recognition. 

The former advocates for more disclosure of internally generated intangibles in financial statements. This aligns with recommendations from the International Accounting Standards Board, which emphasises the value of management teams disclosing their business models, value-creation strategies and significant economic resources. 

In contrast, enhanced recognition seeks amendments to existing accounting standards, particularly IAS 38, advocating for the recognition of more internally generated intangibles, including corporate data, in financial statements.

Joint research by ICAEW and the China Accounting Standard Committee (CASC) delves into the intricacies of accounting for corporate data resources, exploring their unique characteristics and the challenges they present. Here, Dr Eugene Wu Yujun, Assistant Professor at the Harbin Institute of Technology in Shenzhen, proposes a comprehensive framework for their treatment in financial statements.

Economic benefits for reporting entities

Corporations in traditional industries might leverage data and digital technologies to enhance future economic benefits and competitiveness, known as a ‘data enhanced model’. 

For example, during the routine operation of a power grid for many years, corporation A accumulates and forms a relevant database of corporate users’ electricity consumption. 

It creates a data analysis tool, which can develop a more accurate forecast of future electricity consumption trends through the historical analysis of electricity consumption in different seasons, periods, geographical areas and other dimensions. 

The database and analysis tool can be used for its operation and management activities, such as setting up new power facilities and scheduling power distribution networks more reasonably, which is conducive to improving the operational efficiency of corporation A.

By accumulating and analysing corporate user electricity consumption data, corporation A enhances operational efficiency and management activities. 

Another model pertains to new-economy industries that base strategies on data and new digital technologies. The data’s value-creation processes become a core element of their business strategy and business model, referred to as a ‘data enabled model’. 

A credit rating corporation such as Equifax, for example, generates its core product, the credit rating, based on big data collected from various dimensions and combined with proprietary data algorithms developed by corporations. Its corporate strategic value depends entirely on the big data it collects and the continuous update of related algorithms. 

Under rapid digital transformation, corporations are becoming more inclined to adopt a value creation business model through data-based products or services.

Comparisons of data with other assets

The complexity of data assets extends to issues of ownership and measurement. The entire value-creation process of data might involve generation, collection, recording, cleaning, pre-processing, storage, analysis and utilisation. In cases where legal ownership rights over particular data are challenging to determine (like data on customer purchasing behaviour), the reporting entity’s control over other economic rights of data is vital. 

As the actual data controller, the reporting entity dominates this data utilisation process. To realise the value of the data, the entity often makes corresponding investments in data resources and bears corresponding costs. Ultimately, the entity is in possession of the processed data or data products developed. 

Some standard-setters (like CASC) propose establishing a separate property rights system of data, distinguishing between the legal ownership, processing rights and management rights of data products. Under this separation, corporations could de facto control the data without distinctly owning it. As a result, the economic rights of the data may be attributed to the controller, aligning with the principles of ‘controllability’ and ‘inflow of economic benefits’ in accounting concepts. 

The external trading market for data assets is still in its early stage of development. The market-based pricing mechanisms of data are relatively immature. Measuring the fair value of data assets requires considering various factors such as quality, quantity, application scenarios and risk. Therefore, adopting a cost-based approach for the initial accounting measurement of data is a recommendation from our research. 

Proposed accounting treatment of data

To address these challenges, the research proposes a comprehensive framework for the accounting treatment of corporate data resources using existing standards. First, data assets should be classified based on their value-creation mechanisms into internally used data assets, tradable data assets, strategic value data resources, and other data resources. 

Second, specific recognition conditions for internally used and tradable data assets in financial statements should be applied. For instance, internally used data assets are recognised only when they meet ‘economic benefit inflow’, ‘controllability’ and ‘measurability’ conditions, similar to the treatment of research and development expenses under IAS 38.

For internally acquired data assets, a cost-based measurement method might be applied. For tradable data assets, consider the reference transaction price of similar assets for initial measurement, leveraging valuation models based on market or income approaches.

Subsequent measurement of data assets involves estimating their service life for amortisation and continuously monitoring for impairment. An appropriate amortisation method for data assets with a limited service life should be selected. Regular impairment testing is necessary for those with uncertain service lives.

The presentation of data assets in financial statements is also crucial. Including the net value of data assets as a distinct line item under the intangible assets account on the balance sheet provides a clear indication of their role in corporate assets. In the research, reporting entities were encouraged to provide comprehensive information on the formation, investment, impairment, and write-off of data assets, their contribution to corporate value, their relationship with corporation business models, and potential risks.

Finally, the time-varying nature of data assets makes their value highly dependent on application scenarios. Once application scenarios change or factors such as policies and competition lead to the external sharing of data assets, their value experiences significant fluctuations. 

The research suggests that reporting entities need a well-structured data governance strategy and data management system to manage these challenges effectively, including optimising organisational structures and refining business processes, enhancing the transparency, reliability, and comparability of financial reporting related to corporate data assets.

In addition, the research recommends that reporting entities are encouraged to adopt a comprehensive approach to facilitate this process, such as conducting a thorough internal examination of data resources, a well-structured data management system, compliance with data ownership and privacy rights, prototypes of data products, and a valuation analysis of data assets, among other things.

The accounting treatment of data assets is a multifaceted challenge that requires a holistic approach. As the digital economy evolves, the research suggests that it is an ongoing challenge for accounting standard-setters and reporting entities to adapt. The solutions outlined in the research are aimed at ensuring that the digital economy’s impact on corporate operations is accurately reflected.