The most advanced societies depend heavily on intangible assets based on the increased capabilities of data intensive technologies to process information exploiting a qualitative and quantitative data accretion related to the spread of applications whose success is based on the use of (personal) data, and the personalization of production processes. Pervasive and data analytical technologies, such as Internet of Things (IoT), Big Data and Data Mining, are implementing the so-called Fourth Revolution [see Luciano Floridi in The fourth revolution: how the infosphere is reshaping human reality] in which materialistic processes are gradually absorbed by informational ones with increased inferential capacity. In this scenario, this contribute originates from two main questions: if and how can the concept of sustainability be recalibred? Secondly, what are the new barriers to the sustainability? The reasoning methodology follows 3 steps: a)to frame the concept of sustainability in the context of data abundance rather than the scarcity; b)to intercept the causes of threats to the sustainability; c)to clarify how the user-centric control of data quality principles can be a proactive countermeasure. Unlike the materialistic processes, the fuel of intangible processes is represented by an “abundant” resource that is not consumed when used: (digital) data can be copied infinitely, used, reused and distributed globally to generate value and discover previously unknown semantic relationships among data. Nowadays, this cycle has ever lower costs and, generally, it is an exclusive prerogative of the data mining providers that intermediates between the knowledge to be released and the data subject that produces and disclosures initial data. The exploitation of data can cause degradation of information if the mediation of data mining schema is not neutral, namely if it breaks the original bilateral information asymmetry. This is not a quantitative issue of information loss but a qualitative issue of information erosion. The main implication brings out competitive advantage to the data mining processor and raising negative externality to the data subject in the form of negative or distorted personalization of production process, discrimination, false truths, loss of opportunities for the user. The result is a conflictual informational environment with a decline in trust among data subjects that undermines sustainability of continued information disclosure. The data quality principles of accuracy, completeness, consistency, credibility, correctness, accessibility, precision, understandability, portability, data minimization and purpose limitation can guarantee maintenance or recovery of neutrality if they are embedded in control actions by the subjects who produced the data or to which the data concern: this user-centric control is the privacy. It is the resultant of transparent data access actions limited to what is necessary to specific purpose for which data are processed, in absence of which, to protect their information asymmetry, users would use alternative measures such as to reduce or mislead information sharing and quit services. Privacy, as a user-centric control on data quality, contributes to the maintenance of bilateral information asymmetry. This paper aims to highlight how in the pervasive (big) data analytical technologies the sustainability is critically dependent on unilateral information asymmetry between the data mining processor and the data subject. Data quality principles, data minimization and purpose limitation embedded in designing systems as data protection by default paradigm can represent a proactive measure to the sustainability of informational processes.
KeyWords Sustainable informational systems; data intensive analytical technologies, data quality principles, privacy by design.
1c. Assessing sustainability (indicators and reporting)
