“The future is already here – it’s just not evenly distributed” is a quote widely attributed to William Gibson, the science fiction author who provoked much debate on the manifestation of our future society. I was one of many to apply this quote to the adoption of technology; the technology is already invented – it’s just not widely adopted. Many world-changing technologies have taken years or decades to become mainstream, while others may never see broad usage. For example, in the early 2010s, datafication driven by digital transformation led to an ecosystem of data that could be exploited to transform our world. Data-driven innovation powered by ‘big data’ held a clear competitive advantage for many organisations. However, European organisations were lagging in the adoption of such innovation. Big data has since come to the fore in EU investment, and the even distribution of data-driven innovation was a key motivation for the European Public-Private Partnership on Big Data Value (Curry, Metzger, Zillner, Pazzaglia, & García Robles, 2021). Today the European Data Economy is supported by an ecosystem that creates the conditions for marketplace competition, allowing collaboration among diverse, interconnected participants that depend on each other for mutual benefit. While this initiative is a step in the right direction, the fact remains that small organisations still struggle to acquire the necessary resources for big data. 

Data Equality

Going back to Gibson’s quote, future innovation is not only a matter of adoption; access and autonomy are other key factors. The future is here, but only a few can access it. Or the future is here, and someone else will define it for you. This perspective raises profound questions on the type of society we aspire to create and its possible inequalities. As we look to the evolution of data-driven innovation, this is a perspective worth considering. Artificial Intelligence (AI) and Data Science are revolutionising many industries – transportation and logistics, security, manufacturing, energy, healthcare, and agriculture – by providing intelligence to improve efficiency, quality, and flexibility. Large quantities of high-quality data are critical in creating these competitive AI solutions. However, a significant barrier to the adoption of these solutions, is the high upfront costs associated with data collection, integration, and sharing.  

For decades we have seen the consequences of data silos within Enterprises. It is estimated that 50–80% of the costs of data projects go to data integration and preparation activities. With few exceptions, large-scale data and AI innovation are beyond the reach of many small organisations that cannot deal with the complexity of data management and the high costs associated with data infrastructure. This limits large-scale data-driven projects to those with the necessary expertise and resources. The future is here, but only if you have the scale and expertise to manage the data. This situation needs to change to enable everyone, especially smaller stakeholders, to engage and leverage the value of data-driven AI solutions. The realisation of a shared digital infrastructure will take careful negotiation from all sides to build a consensus – policymakers, researchers, educators, industry, engineers, and those who create the data.

Data Spaces:  A Public Digital Infrastructure

A platform strategy is an approach to entering a market in which distinct groups derive benefits from one another’s participation. Platform approaches have proved successful in many areas of technology, from platforms which support transactions among buyers and sellers in marketplaces, to innovation platforms which provide a foundation for the development of complementary products or services. The idea of large-scale “data” platforms has been touted as a viable way to overcome excessive costs associated with data sharing and providing equal data access.  

Forward-thinking societies must see the provision of digital infrastructure as a shared societal service, like that of water, sanitation, education, and healthcare. It is clear we desperately need new approaches to support the complex data ecosystems that our “smart” society is creating. This vision demands a fundamental shift in how we deliver large-scale data infrastructure to unlock the power of data. We also need to consider the role of government, citizens, and the private sector. Different regions of the world are tackling this in different ways, from the provision of digital infrastructure by the private sector to government-owned infrastructure. Within Europe, we are taking more of a societal approach, as a partnership between public and private organisations. 

The term “dataspace” or “data space” can now be seen as an umbrella term categorising several closely related concepts, including data platform and data marketplace. A data space can provide a clear framework to support sharing within a data ecosystem, while addressing concerns including technical issues, governance, social interactions, and business processes. For example, industrial data spaces can support the trusted and secure sharing and trading of commercial data assets; controls on legal compliance are automated and robust, and data owners are remunerated. Personal data spaces enforce legislation and allow data subjects and owners to control their data and its subsequent use. The data space concept has gained traction recently with several groups exploring its usefulness for managing data from different domains and regions within a global ecosystem.

The transition to data space technology will not, however, be a fast one. A decade may pass before we understand the methods and the means of mature data spaces. In comparison, the World Wide Web took from the mid-1990s to well beyond 2000 to develop into the everyday tool we use today to search for information and order weekly groceries. Moreover, the development of data spaces requires the engagement and agreement of our society. In the same way agreement preceded a common approach for the electricity grid, we need negotiation and collective agreement from industry large and small, policymakers, educators, researchers, and society, to create the basis of the data economy and common European data spaces.  

Gibson’s quote continues to resonate and provoke debate today. Data spaces are already here – it’s time to evenly distribute them

The European Data Strategy: Common European Data Spaces

In her 2020 State of the Union Address, President of the European Commission, Ursula von der Leyen called for Europe to lead the way on digital in Data and Artificial Intelligence. The European Commission vision within their Data Strategy is to establish common European Data Spaces as a mechanism to support the sharing and exchange of data within a single market for data that will ensure Europe’s global competitiveness and data sovereignty (European Commission, 2020). The aim is to make data available in the economy and society, while keeping companies and individuals who generate the data in control, and respecting European values and regulation (Communication: A European Strategy for Data, 2020). Furthermore, common European data spaces are vital in implementing the European AI Strategy, as the potential for AI can only be fully exploited with access to large volumes of high-quality data. Data for AI is at the centre of the new €2.6 billion European Partnership on AI, Data and Robotics (Zillner et al., 2020) – an effort towards European global leadership through researching, developing, and deploying value-driven, trustworthy AI.

Our Digital Society 

Data spaces offer the foundations to design a new digital society where individuals and organisations can share data in a trusted and controlled environment. A responsible method for discovering solutions to societal problems with the help of data-driven AI. It is an ambitious goal and a systemic change to our society that requires solid scientific, technical, and social foundations. We at NUI Galway’s Insight SFI Research Centre for Data Analytics have been at the leading-edge in researching data space technology for the last decade. We have focused in particular on semantics and linguistics, knowledge graphs, linked data, and the Internet of Things in domains including Enterprise, Health, City, Water, and Energy (Curry, 2020). 

References

Curry, E. (2020). Real-time Linked Dataspaces: A Data Platform for Intelligent Systems Within Internet of Things-Based Smart Environments. In Real-time Linked Dataspaces (pp. 3–14). Cham: Springer International Publishing. (Open Access PDF)

Curry, E., Metzger, A., Zillner, S., Pazzaglia, J.-C., & García Robles, A. (Eds.). (2021). The Elements of Big Data Value. Cham: Springer International Publishing. (Open Access PDF)

European Commission. Communication: A European strategy for data (2020).

Zillner, S., Bisset, D., Milano, M., Curry, E., Hahn, T., Lafrenz, R., … O’Sullivan, B. (2020). Strategic Research, Innovation and Deployment Agenda – AI, Data and Robotics Partnership. Third Release (Third). Brussels: BDVA, euRobotics, ELLIS, EurAI and CLAIRE. (PDF)