Business capacity to collect and process digitalized information (data) at unprecedented scale and speed is transforming economies around the globe. One aspect of this transformation is the relevance of data as a 'resource' for relatively recent advancements in artificial intelligence (AI) technology in various forms of machine learning, most notably 'deep learning'. The theoretical foundations for this kind of AI go back to the 1950s, but only the availability of novel and larger datasets led to the end of a long 'AI winter' and the dawn of an 'AI spring'. 1 The growing but unevenly distributed ability to capture information about the world in digital form is a complex phenomenon. The public discourse surrounding data seems somewhat detached from the sophisticated ways in which scholars have theorized the relationship between data, information, knowledge, and wisdom. 2 The lack of adequate terminology to capture the phenomena caused by the gradual digitalization of economies and societies is evidenced by the vain search for metaphorical equivalents. 3 The effort to assess the effects of digitalization on the economy is severely hindered by a paradoxical lack of data about data, since the commercial value of data is reflected neither in balance sheets nor in the conventional metrics used to assess the state of the economy or trade. 4 Yet, it seems misguided to attribute this lamentable state of affairs solely to the notorious intransparency of global digital corporations or the inertia of accountants, statisticians,