Big Data as Misleading Facilities

Currently, many technologies translate both empirical phenomena and human interactions into digital data. Accordingly, when processed and analysed, these data become sources of pieces of information, correlations, predictions and meanings that would otherwise remain hidden inside facts and human behaviours. Firms may use this “disclosed knowledge” for business purposes; that is, to guess not only rivals' strategies but also consumers' actual and potential wants and needs. Therefore, there is room to argue that, for bringing about better products and services, such “disclosed knowledge” is a competitive advantage. Yet, does this conclusion entail that the data revealing this “disclosed knowledge” essential facilities?We do not believe so. The logic gap between business-friendly knowledge and essential data is huge, even if the data under scrutiny are not among the many and varied ones that firms may collect, and even if some consider these data as barriers to entry that shelter the market power of some firms. In other words, we maintain that characterising big data resulting in business-friendly knowledge as an essential facility is misleading. Such a characterization misses an intermediate step: that concerning the information extracted from big data. In addition, to argue against the idea that big data could be essential facilities, we consider the many drawbacks that the connected duty to share could produce