Some reasons to think about big data as data commons

The role of data in the current digitally soaked society is so important that data has been repeatedly defined in several and different spheres as the new oil, a new factor of production, and the currency of today's digital market. 90 per cent of the data present in the world today has been created in the last two years and this huge amount is expected to grow by 40 per cent annually over the next decade, a forecast consistent with the diffusion of smartphone usage and the expansion of the Internet of Things ecosystem.

The term “big data”, besides referring to the ever increasing size of this stream of information, relates to the new generation of technologies designed to collect, aggregate and analyse it quickly and cost effectively, in order to extract variably reliable prediction and decision-making patterns for whatever purpose in whatever sector, be it public or private.  Assessing and optimizing credit risk in the lending industry, implementing citizen-focused public policies, deploying more police forces where crimes are most likely to occur, or avoiding overproduction and resource waste in agriculture are just a handful of examples of big data analytics’ applications.

It is worth noting that the so-called “pure players” of the big data market (that is companies obtaining all of their revenues from the sale of big data products and services) account for just 5 per cent of the overall market, while the remainder is generated by dominant tech-companies providing to a great extent online services.

In Europe, more than the half of this sector is absorbed by US-based companies whose revenue source typically leverages a two-sided platform business model, where users’ data, as well as their “attention” to the highly-tailored advertising banners they are shown during their interactions with the platform, may be deemed as the fee to pay for the “free” service they enjoy. Google and Facebook can be considered the most representative advertising platforms, as they capture together more than a half of all the growth in global online ad spend.

Another increasingly profitable and younger data-fuelled economic sector, it too originated in the innovation kingdom of the Silicon Valley, is that of on-demand platforms. Uber and Airbnb are the most notorious and the most valued amongst a sizeable bunch of “asset-less” platforms that operate through hyper-outsourced business models, whose precariousness - especially in the case of Uber -  is corroborated by the noticeable mismatch between market capitalization and turnover.

On the whole, the digital economy presents some major challenges.

The most visible applies to privacy and security issues. The amount of data that requires protection (i.e. personally identifiable data/information) will significantly grow over the next few years. In this field, the US legal framework is far less strict than the European one. While in the EU data protection and privacy rights are considered as being fundamental rights and a so-called “omnibus approach” is applied, in the US there is no recognition of a right to privacy at the Federal level. The highest level of protection is guaranteed for sensitive personal information only, as legislators believe that privacy interests must be balanced with the right to free expression and commerce. The imposition of the GDPR on foreign firms to comply with its provisions concerning, inter alia, the right to data portability and a reviewed definition of “consent”, should fortify European citizens’ data sovereignty.

A second challenge relates to competition. Indeed, the atypical structure of the big data ecosystem makes it hard to assess potential anti-competitive behavior for several reasons. First, the multi-sidedness of platforms complicates the definition of the relevant market. Secondly, the self-reinforcing structure of data caused by network effects give big corporations competitive advantages that weaken the analysis of “equilibrium” prices. Thirdly, the concentration of data processing power and users’ data flows into few players’ hands, preventing the entrance of small but promising competitors that frequently end up getting acquired. This makes more difficult for monetary sanctions to correct problems of monopoly.

Another thorny issue relates to the labour conditions of the “gig” workers in the context of the above mentioned on-demand economy, which has converted everything in an exploitable resource, muddling boundaries that were well-marked until recently - like those between private and professional as well as those between dependent and independent employment, or even work and leisure.

Finally, the digital economy has generated a considerable fiscal distortion: companies that rely on digital business models pay on average half the effective tax rate of traditional companies, thanks to the “fluid” nature of their businesses and to the placement of their subsidiaries in countries with low tax regimes. In response thereto, the European Commission has freshly come up with two distinct legislative proposals to ensure a fair and efficient European tax system.

In the light of the above, an alternative data management for a more equal and sustainable socio-economic digital environment is not only desirable but necessary. The DECODE project is building towards this direction: a digital economy where citizen data is not stored in silos located and handled in overseas countries but rather self-controlled and available for broader communal use, with appropriate privacy protections and value distribution outcomes. The Data Commons approach centres precisely around the pooling and collective management of individually-produced streams of personal and sensor data that, combined with public data of the cities, will offer data-driven services that better respond to individual and overall community needs.

For an extensive investigation of the economic, regulatory, and value-creation issues in the framework of the data economy you can read our report and to keep updated about DECODE’s progress follow us on Twitter @decodeproject.