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Getting a Grip on That Data

In societies transformed by the digital revolution, the questions of governance revolve primarily around data — the governance of data and new forms of governance with data.

Emerging data governance models
By Max Craglia & Henk Scholten

We are not sure when it started to change. In the past, governments were the main collectors of data about lands, people and resources. Today, the commercial sector collects vast amounts of real-time data and knows more about society than governments — not just at the aggregate level, but down to the behavior, preferences and real-time location of individuals. So, how does this shift in knowledge, and therefore power, among these key actors (the state, the commercial sector, people and machines) affect the governance of human society? How can we shape our futures in an increasingly fast-evolving technological, social and physical environment? More fundamentally, can we shape our future at all, or are we just passive objects of decisions taken elsewhere?

We started with these questions about four years ago with a small group of enthusiasts in setting up the Digitranscope [1] project at the Centre for Advanced Studies of the European Commission Joint Research Centre (JRC). We have since involved more than 300 colleagues from governments, academia, industry and civil society to try and find some of the answers that we have now published in the Digitranscope Report [2] on the Governance of Digitally-Transformed Society. We realized very quickly that in societies transformed by the digital revolution, the questions of governance revolve primarily about data: The governance of data and new forms of governance with data.

Why is this important?

Because you will all be familiar with the increasing political and media attention given to Artificial Intelligence (AI), which is seen as a key enabling technology for the future development of societies and nations. Whilst many countries are developing their national strategies to become world-leaders in AI, [3] controlling the data upon which AI applications and products are developed is just as important as mastering the development of AI algorithms.

Why now?

In the past 20 years, data publication for reuse, via catalogues and portals, was seen as the end of the process of data collection, analysis and use by the (public sector) data custodian. It was often also perceived as a burden because the organization publishing the data was not a direct beneficiary of the value subsequently generated by third parties or accrued by society as a whole in terms of greater transparency and accountability. Observing the Big Data platforms at work, it became noticeable that to them data publication was the beginning of the value-creation chain, not the end. In fact, social media platforms and search engines do not even create the original data, they let the users do so. They then integrate the users’ data, add value through analytics, repackage into products or services, and sell to third parties, thus monetizing the added value created. This shift in the datafication paradigm has led to an increasing call for public authorities to also shift from publishing datasets in portals to open access via machine-to-machine Application Protocol Interfaces (APIs) and add value to the data they publish by adding the intelligence via analytics on who uses the data for what purpose. [4]

From data analytics to digital twins

Digital twins, which are digital replicas of living or non-living things, have been used in the industry for several decades — largely as an extension of computer-aided design. They allow simulation and testing of artefacts before moving into production. With the vast increase in sensors networks and computer processing, digital twins have seen a significant growth in every sector, particularly in the environmental domain and urban management [5]. While the growth of Big Data has required a closer integration of data and processing into virtual platforms, digital twins integrate simulation into these platforms strengthening the move towards dynamic and interactive environments based on data streams and feedback-loops [6]. So, technology and policy environments are changing rapidly, posing challenges for the governance of society and also offering new opportunities.

Governance of data

In the current digital society, the dominant model for the governance of personal data is the one established by a few ‘big-tech’ companies that are collecting, aggregating and financially exploiting massive amounts of personal data. Yet, other actors beyond those companies are progressively becoming involved in controlling personal data and producing value from it through different practices. These actors include public bodies (such as local administrations), private entities (comprising small businesses and startups), scientific and civil society organizations, activists, social entrepreneurs, and citizens themselves. We explored some of the models for the governance of data that are emerging from the practices of these actors.

In particular, we identified four key models: data sharing pools; data co-operatives; public data trusts; and personal data sovereignty. The goal of our analysis was to investigate to what extent they support different, more balanced power relations, and how they redistribute more equitably the value generated from data across society, compared to the current practices of ‘big-tech’ corporations.

Our analysis shows that particularly data co-operatives and public data trusts seem to be good models to share more equally the value generated through data analytics to all the parties involved. They are in their infancy, but we see a shift in policy, at least in Europe, to encourage these alternative models of data governance and increase the social value derived from data. The Data Governance Act [7] proposed by the European Commission goes in this direction.

Whilst many countries are developing their national strategies to become world-leaders in AI, controlling the data upon which AI applications and products are developed is just as important as mastering the development of AI algorithms

New forms of governance with data

We ran some experiments to see how we could use digital twins at national and urban level, and in gaming environments to reach more effectively different audiences. For city administrators and analysts, we leveraged the digital twins of the cities of Amsterdam and Duisburg to develop a city operating system that would integrate the different data flows pertaining to mobility and energy and visualize the real-time analytics and simulations via city dashboards. The experiments allowed to interact with city officials and decision-makers, and develop a mutual understanding of what is needed and what is possible. The explorations were successful and there is already a request from both cities for the implementation of the overall system.

In another experiment, we were able to leverage the Digital Twin of The Netherlands developed by the Vrije Universiteit of Amsterdam, Geodan, the Dutch Cadastre and the Dutch Ministry of Infrastructure and Water using MinecraftTM to raise awareness through gaming of young adults in two schools in Amsterdam and Warsaw on the trade-offs needed in the energy transition. Moreover, we were able to contribute to a big event in the stadium of the Ajax football team in Amsterdam, where 500 children used the Digital Twin of their city to design a new sustainable neighborhood. On that occasion, the UN Environment Program (UNEP) signed a partnership agreement with the Dutch EduGIS Foundation.

Last but not least, we used AI and modeling to explore how governments could design personalized policies focusing on those who are in the greatest need, like digital platforms use consumer profiling to develop personalized services. We used synthetic population to avoid using personal data, and applied this technique successfully in the context of the COVID-19 pandemic to model the impact of reopening different economic activities.

Shaping the future

We started with the fundamental questions: can we shape our future? The answer is a resounding yes! Governments have a key role to play in shaping the regulatory environment, companies have, of course, a big role as engines of innovation and growth, but all of us as individuals have a key role too by exercising our rights and becoming empowered citizens of the digital society. If there is a take-away message from the Digitranscope journey, it is that the governance of our digitally transforming society is challenging and complex, full of opportunities and pitfalls, but then ultimately it is up to all of us to shape it. We cannot afford to leave it to others.

Max Craglia, Is Senior Expert at European Commission Joint Research Centre’s Digital Economy Unit

Henk Scholten, Is Director Innovation at Geodan; and Professor of Spatial Informatics at School of Business & Economics, Vrije Universiteit Amsterdam

  1. https://ec.europa.eu/jrc/communities/en/community/digitranscope
  2. https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/digitranscope-governance-digitally-transformed-society
  3. See for example https://knowledge4policy.ec.europa.eu/ai-watch/national-strategies-artificial-intelligence_en for the EU and https://oecd.ai/countries-and-initiatives for a more global perspective.
  4. For an overview of AIs in government see https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/application-programming-interfaces-governments-why-what-and-how
  5. For a survey of digital twins see https://publications.jrc.ec.europa.eu/repository/handle/JRC122457
  6. See on this IoT 2.0 and the INTERNET of TRANSFORMATION (Web of Things and Digital Twins): A multi-facets analysis.
  7. https://digital-strategy.ec.europa.eu/en/library/data-governance-act

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