Valuing big data: An analysis of current regulations and proposal of frameworks

Abstract

The increasing prevalence of big data, large stores of data that enable businesses to generate previously inaccessible insights, has resulted in performance gains for the firms that harness its capabilities. However, the impact that big data has on a firm’s profitability and financial position is rarely captured in its financial statements due to big data’s status as an internally generated intangible asset. As per the current IFRS regulations, big data is not eligible for recognition.

We argue that the true impact of big data on a firm is not appropriately reflected in a firm’s financial statements. The IFRS as currently written does not allow firms to capitalize data assets, thus compromising the goal of financial statements: to provide relevant and reliable information.

Analysis of the current standards reveal gaps in measuring the future economic benefit and costs of big data, primarily attributable to the lack of clarity surrounding the unit of account used to measure big data. Furthermore, IAS 38′s dichotomization of research and development does not accurately represent the applicability of big data assets.

Therefore, we propose a conceptual framework for understanding the economic value of big data. These include the use of a database as a unit of account, costing methods derived from user-based metrics, and a renewed focus on the intention to apply data assets.