 Moreover, the privatisation of data has serious implications for the world of research and the knowledge it produces. Firstly, it affects which data are disseminated, and with which expectations. Corporations usually only release data that they regard as having lesser commercial value and that they need public sector assistance to interpret. This introduces another distortion on the sources and types of data that are accessible online while more expensive and complex data are kept secret. Even many of the ways in which citizens -researchers included - are encouraged to interact with databases and data interpretation sites tend to encourage participation that generates further commercial value. Sociologists have recently described this type of social participation as a form of exploitation (Prainsack &amp; Buyx 2017; Srnicek 2017). In turn, these ways of exploiting data strengthen their economic value over their scientific value. When it comes to the commerce of personal data between companies working in analysis, the value of the data as commercial products -which includes the evaluation of the speed and efficiency with which access to certain data can help develop new products - often has priority over scientific issues such as for example, representativity and reliability of the data and the ways they were analysed. This can result in decisions that pose a problem scientifically or that simply are not interested in investigating the consequences of the assumptions made and the processes used. This lack of interest easily translates into ignorance of discrimination, inequality and potential errors in the data considered. This type of ignorance is highly strategic and economically productive since it enables the use of data without concerns over social and scientific implications. In this scenario the evaluation on the quality of data shrinks to an evaluation of their usefulness towards short-term analyses or forecasting required by the client. There are no incentives in this system to encourage evaluation of the long-term implications of data analysis. The risk here is that the commerce of data is accompanied by an increasing divergence between data and their context. The interest in the history of the transit of data, the plurality of their emotional or scientific value and the re-evaluation of their origins tend to disappear over time, to be substituted by the increasing hold of the financial value of data.