There is a general concensus that data is of great value as a key factor of production in the digital economy. However, how to leverage the value of data and realize data assetization has become an urgent issue for enterprises in the current digital transformation process. There is rare research focusing on the process of data assetization from a general and overall perspective. Given this, this paper concentrates on how to leverage the valuable data and the process of enterprise data assetization. To be specific, the definition of data assets is firstly defined based on the existing literature. Then, an engineering quality inspection company is selected as the sample of case study. Through the several rounds of workshops with the company, relevant data and information are obtained and recorded. In the case study, the coding techniques of procedural grounded theory are used to code the acquired data, and the practical exploration of data assetization in the case company is described in detail. Finally, based on the case study, this paper proposes a theoretical model for the realisation path of enterprise data assetization.

The conclusions are as follows: First, in this paper, data assets are defined as data generated or acquired by enterprises during production, operation or transaction, which have the right of ownership or use without violating the prohibitions of laws and regulations and the agreement with the collected subject, and can be expected to generate economic benefits by electronic means. Data assets have the characteristics of dynamism, diversity and reusability. Second, the theoretical model of the realisation path of enterprise data assetization is proposed, including the company’s data strategy, regulation and organizational structure establishment stage, data system and platform development and construction stage, data asset quality management stage, and data asset inventory and operation stage. Third, in the process of each stage, the guaranteed initiatives for the realisation of the path are sorted out, and the study concludes that the various stages of enterprise data assetization also require the support of corporate strategy, organization, technology, talent and other guarantee initiatives.

The contributions are that: First, the concept of data assets is clarified and the three main characteristics of data assets are discussed, which have important theoretical value. Second, this study is the first to analyse and discuss the process of enterprise data assetization from a global and overall perspective, making up for the lack of existing research. Third, based on the case study, it provides a theoretical and practical guide to the process of data assetization in enterprises by sorting out the safeguarding initiatives of the data assetization realisation path.

Based on the above findings, the following implications can be obtained: First, the realisation of data assetisation requires enterprises to continuously improve their data governance capabilities. Specifically, they need to improve their corporate data strategies, promote the development of data regulations and standards, and build technical platforms and talent teams. Second, the realisation of data assetization requires enterprises to make data assets available to specific business scenarios through data asset inventory and operation, thus helping enterprises to reduce costs, increase revenue and control risks, and realise the value of data assets.

Future directions include that: Explore theoretical models of data assetization that are suitable for different industries and enterprises; make a more detailed discussion on the data assetization process; use the empirical analysis to discuss the benefits of enterprise data assetization; and further present and demonstrate the value of data assetization.