European Data Economy: Between Competition and Regulation

Curated on Posted on by Stefaan Verhulst

Report by René Arnold, Christian Hildebrandt, and Serpil Taş: “Data and its economic impact permeates all sectors of the economy. The data economy is not a new sector, but more like a challenge for all firms to compete and innovate as part of a new wave of economic value creation.

With data playing an increasingly important role across all sectors of the economy, the results of this report point European policymakers to promote the development and adoption of unified reference architectures. These architectures constitute a technology-neutral and cross-sectoral approach that will enable companies small and large to compete and to innovate—unlocking the economic potential of data capture in an increasingly digitized world.

Data access appears to be less of a hindrance to a thriving data economy due to the net increase in capabilities in data capture, elevation, and analysis. What does prove difficult for firms is discovering existing datasets and establishing their suitability for achieving their economic objectives. Reference architectures can facilitate this process as they provide a framework to locate potential providers of relevant datasets and carry sufficient additional information (metadata) about datasets to enable firms to understand whether a particular dataset, or parts of it, fits their purpose.

Whether third-party data access is suitable to solve a specific business task in the first place ought to be a decision at the discretion of the economic actors involved. As our report underscores, data captured in one context with a specific purpose may not be fit for another context or another purpose. Consequently, a firm has to evaluate case-by-case whether first-party data capture, third-party data access, or a mixed approach is the best solution. This evaluation will naturally depend on whether there is any other firm capturing data suitable for the task that is willing to negotiate conditions for third-party access to this data. Unified data architectures may also lower the barriers for a firm capturing suitable data to engage in negotiations, since its adoption will lower the costs of making the data ready for a successful exchange. Such architectures may further integrate licensing provisions ensuring that data, once exchanged, is not used beyond the agreed purpose. It can also bring in functions that improve the discoverability of potential data providers….(More)”.

How can we measure productivity in the public sector?

Curated on Posted on by Stefaan Verhulst

Ravi Somani at the World Bank: “In most economies, the public sector is a major purchaser of goods, services and labor. According to the Worldwide Bureaucracy Indicators, globally the public sector accounts for around 25% of GDP and 38% of formal employment. Generating efficiency gains in the public sector can, therefore, have important implications for a country’s overall economic performance.  

Public-sector productivity measures the rate with which inputs are converted into desirable outputs in the public sector. Measures can be developed at the level of the employee, organization, or overall public sector, and can be tracked over time. Such information allows policymakers to identify good and bad performers, understand what might be correlated with good performance, and measure the returns to different types of public expenditures. This knowledge can be used to improve the allocation of public resources in the future and maximize the impact of the public purse.

But how can we measure it?

However, measuring productivity in the public sector can be tricky because:

  • There are often no market transactions for public services, or they are distorted by subsidies and other market imperfections.
  • Many public services are complex, requiring (often immeasurable) inputs from multiple individuals and organizations.
  • There is often a substantial time lag between investments in inputs and the realization of outputs and outcomes.

This recent World Bank publication provides a summary of the different approaches to measuring productivity in the public sector, presented in the table below.  For simplicity, the approaches are separated into: ‘macro’ approaches, which provide aggregate information at the level of an organization, sector, or service as a whole; and ‘micro’ approaches, which can be applied to the individual employee, task, project, and process.   
 

Macro and Micro Approaches to measure public-sector productivity

There is no silver bullet for accurately measuring public-sector productivity – each approach has its own limitations.  For example, the cost-weighted-output approach requires activity-level data, necessitates different approaches for different sectors, and results in metrics with difficult-to-interpret absolute levels.  Project-completion rates require access to project-level data and may not fully account for differences in the quality and complexity of projects. The publication includes a list of the pros, cons, and implementation requirements for each approach….(More)”.

Wikipedia Is Finally Asking Big Tech to Pay Up

Curated on Posted on by Stefaan Verhulst

Noam Cohen at Wired: “From the start, Google and Wikipedia have been in a kind of unspoken partnership: Wikipedia produces the information Google serves up in response to user queries, and Google builds up Wikipedia’s reputation as a source of trustworthy information. Of course, there have been bumps, including Google’s bold attempt to replace Wikipedia with its own version of user-generated articles, under the clumsy name “Knol,” short for knowledge. Knol never did catch on, despite Google’s offer to pay the principal author of an article a share of advertising money. But after that failure, Google embraced Wikipedia even tighter—not only linking to its articles but reprinting key excerpts on its search result pages to quickly deliver Wikipedia’s knowledge to those seeking answers.

The two have grown in tandem over the past 20 years, each becoming its own household word. But whereas one mushroomed into a trillion-dollar company, the other has remained a midsize nonprofit, depending on the generosity of individual users, grant-giving foundations, and the Silicon Valley giants themselves to stay afloat. Now Wikipedia is seeking to rebalance its relationships with Google and other big tech firms like Amazon, Facebook, and Apple, whose platforms and virtual assistants lean on Wikipedia as a cost-free virtual crib sheet.

Today, the Wikimedia Foundation, which operates the Wikipedia project in more than 300 languages as well as other wiki-projects, is announcing the launch of a commercial product, Wikimedia Enterprise. The new service is designed for the sale and efficient delivery of Wikipedia’s content directly to these online behemoths (and eventually, to smaller companies too)….(More)”.

Using Data and Citizen Science for Gardening Success

Curated on Posted on by Stefaan Verhulst

Article by Elizabeth Waddington: “…Data can help you personally by providing information you can use. And it also allows you to play a wider role in boosting understanding of our planet and tackling the global crises we face in a collaborative way. Consider the following examples.

Grow Observatory

This is one great example of data gathering and citizen science. Grow Observatory is a European citizen’s observatory through which people work together to take action on climate change, build better soil, grow healthier food and corroborate data from the new generation of Copernicus satellites.

Twenty-four Grow communities in 13 European countries created a network of over 6,500 ground-based soil sensors and collected a lot of soil-related data. And many insights have helped people learn about and test regenerative food growing techniques.

On their website, you can explore sensor locations, or make use of dynamic soil moisture maps. With the Grow Observatory app, you can get crop and planting advice tailored to your location, and get detailed, science-based information about regenerative growing practices. Their water planner also allows small-scale growers to learn more about how much water their plants will need in their location over the coming months if they live in one of the areas which currently have available data…

Cooperative Citizen Science: iNaturalist, Bioblitzes, Bird Counts, and More

Wherever you live, there are many different ways to get involved and help build data. From submitting observations on wildlife in your garden through apps like iNaturalist to taking part in local Bioblitzes, bird counts, and more – there are plenty of ways we can collect data that will help us – and others – down the road.

Collecting data through our observations, and, crucially, sharing that data with others can help us create the future we all want to see. We, as individuals, can often feel powerless. But citizen science projects help us to see the collective power we can wield when we work together. Modern technology means we can be hyper-connected, and affect wider systems, even when we are alone in our own gardens….(More)”

Lessons from all democracies

Curated on Posted on by Stefaan Verhulst

David Stasavage at Aeon: “Today, many people see democracy as under threat in a way that only a decade ago seemed unimaginable. Following the fall of the Berlin Wall in 1989, it seemed like democracy was the way of the future. But nowadays, the state of democracy looks very different; we hear about ‘backsliding’ and ‘decay’ and other descriptions of a sort of creeping authoritarianism. Some long-established democracies, such as the United States, are witnessing a violation of governmental norms once thought secure, and this has culminated in the recent insurrection at the US Capitol. If democracy is a torch that shines for a time before then burning out – think of Classical Athens and Renaissance city republics – it all feels as if we might be heading toward a new period of darkness. What can we do to reverse this apparent trend and support democracy?

First, we must dispense with the idea that democracy is like a torch that gets passed from one leading society to another. The core feature of democracy – that those who rule can do so only with the consent of the people – wasn’t invented in one place at one time: it evolved independently in a great many human societies.

Over several millennia and across multiple continents, early democracy was an institution in which rulers governed jointly with councils and assemblies of the people. From the Huron (who called themselves the Wendats) and the Iroquois (who called themselves the Haudenosaunee) in the Northeastern Woodlands of North America, to the republics of Ancient India, to examples of city governance in ancient Mesopotamia, these councils and assemblies were common. Classical Greece provided particularly important instances of this democratic practice, and it’s true that the Greeks gave us a language for thinking about democracy, including the word demokratia itself. But they didn’t invent the practice. If we want to better understand the strengths and weaknesses of our modern democracies, then early democratic societies from around the world provide important lessons.

The core feature of early democracy was that the people had power, even if multiparty elections (today, often thought to be a definitive feature of democracy) didn’t happen. The people, or at least some significant fraction of them, exercised this power in many different ways. In some cases, a ruler was chosen by a council or assembly, and was limited to being first among equals. In other instances, a ruler inherited their position, but faced constraints to seek consent from the people before taking actions both large and small. The alternative to early democracy was autocracy, a system where one person ruled on their own via bureaucratic subordinates whom they had recruited and remunerated. The word ‘autocracy’ is a bit of a misnomer here in that no one in this position ever truly ruled on their own, but it does signify a different way of organising political power.

Early democratic governance is clearly apparent in some ancient societies in Mesopotamia as well as in India. It flourished in a number of places in the Americas before European conquest, such as among the Huron and the Iroquois in the Northeastern Woodlands and in the ‘Republic of Tlaxcala’ that abutted the Triple Alliance, more commonly known as the Aztec Empire. It was also common in precolonial Africa. In all of these societies there were several defining features that tended to reinforce early democracy: small scale, a need for rulers to depend on the people for knowledge, and finally the ability of members of society to exit to other locales if they were unhappy with a ruler. These three features were not always present in the same measure, but collectively they helped to underpin early democracy….(More)”

The Techlash and Tech Crisis Communication

Curated on Posted on by Stefaan Verhulst

Book by Nirit Weiss-Blatt: “This book provides an in-depth analysis of the evolution of tech journalism. The emerging tech-backlash is a story of pendulum swings: We are currently in tech-dystopianism after a long period spent in tech-utopianism. Tech companies were used to ‘cheerleading’ coverage of product launches. This long tech-press honeymoon ended, and was replaced by a new era of mounting criticism focused on tech’s negative impact on society. When and why did tech coverage shift? How did tech companies respond to the rise of tech criticism?

The book depicts three main eras: Pre-Techlash, Techlash, and Post-Techlash. The reader is taken on a journey from computer magazines, through tech blogs to the upsurge of tech investigative reporting. It illuminates the profound changes in the power dynamics between the media and the tech giants it covers.

The interplay between tech journalism and tech PR was underexplored. Through analyses of both tech media and the corporates’ crisis responses, this book examines the roots and characteristics of the Techlash, and provides explanations to ‘How did we get here?’. Insightful observations by tech journalists and tech public relations professionals are added to the research data, and together – they tell the story of the TECHLASH. It includes theoretical and practical implications for both tech enthusiasts and critics….(More)”.

A new approach to problem-solving across the Sustainable Development Goals

Curated on Posted on by Stefaan Verhulst

Alexandra Bracken, John McArthur, and Jacob Taylor at Brookings: “The economic, social, and environmental challenges embedded throughout the world’s 17 Sustainable Development Goals (SDGs) will require many breakthroughs from business as usual. COVID-19 has only underscored the SDGs’ central message that the underlying problems are both interconnected and urgent, so new mindsets are required to generate faster progress on many fronts at once. Our recent report, 17 Rooms: A new approach to spurring action for the Sustainable Development Goals, describes an effort to innovate around the process of SDG problem-solving itself.

17 Rooms aims to advance problem-solving within and across all the SDGs. As a partnership between Brookings and The Rockefeller Foundation, the first version of the undertaking was convened in September 2018, as a single meeting on the eve of the U.N. General Assembly in New York. The initiative has since evolved into a two-pronged effort: an annual flagship process focused on global-scale policy issues and a community-level process in which local actors are taking 17 Rooms methods into their own hands.

In practical terms, 17 Rooms consists of participants from disparate specialist communities each meeting in their own “Rooms,” or working groups, one for each SDG. Each Room is tasked with a common assignment of identifying cooperative actions they can take over the subsequent 12-18 months. Emerging ideas are then shared across Rooms to spot opportunities for collaboration.

The initiative continues to evolve through ongoing experimentation, so methods are not overly fixed, but three design principles help define key elements of the 17 Rooms mindset:

  1. All SDGs get a seat at the table. Insights, participants, and priorities are valued equally across all the specialist communities focused on individual dimensions of the SDGs
  2. Take a next step, not the perfect step. The process encourages participants to identify—and collaborate on—actions that are “big enough to matter, but small enough to get done”
  3. Conversations, not presentations. Discussions are structured around collaboration and peer-learning, aiming to focus on what’s best for an issue, not any individual organization

These principles appear to contribute to three distinct forms of value: the advancement of action, the generation of insights, and a strengthened sense of community among participants….(More)”.

Establishment of Sustainable Data Ecosystems

Curated on Posted on by Stefaan Verhulst

Report and Recommendations for the evolution of spatial data infrastructures by S. Martin, Gautier, P., Turki, and S., Kotsev: “The purpose of this study is to identify and analyse a set of successful data ecosystems and to address recommendations that can act as catalysts of data-driven innovation in line with the recently published European data strategy. The work presented here tries to identify to the largest extent possible actionable items.

Specifically, the study contributes with insights into the approaches that would help in the evolution of existing spatial data infrastructures (SDI), which are usually governed by the public sector and driven by data providers, to self-sustainable data ecosystems where different actors (including providers, users, intermediaries.) contribute and gain social and economic value in accordance with their specific objectives and incentives.

The overall approach described in this document is based on the identification and documentation of a set of case studies of existing data ecosystems and use cases for developing applications based on data coming from two or more data ecosystems, based on existing operational or experimental applications. Following a literature review on data ecosystem thinking and modelling, a framework consisting of three parts (Annex I) was designed. An ecosystem summary is drawn, giving an overall representation of the ecosystem key aspects. Two additional parts are detailed. One dedicated to ecosystem value dynamic illustrating how the ecosystem is structured through the resources exchanged between stakeholders, and the associated value.

Consequently, the ecosystem data flows represent the ecosystem from a complementary and more technical perspective, representing the flows and the data cycles associated to a given scenario. These two parts provide good proxies to evaluate the health and the maturity of a data ecosystem…(More)”.

Policy 2.0 in the Pandemic World: What Worked, What Didn’t, and Why

Curated on Posted on by Stefaan Verhulst

Blog by David Osimo: “…So how, then, did these new tools perform when confronted with the once-in-a-lifetime crisis of a vast global pandemic?

It turns out, some things worked. Others didn’t. And the question of how these new policymaking tools functioned in the heat of battle is already generating valuable ammunition for future crises.

So what worked?

Policy modelling – an analytical framework designed to anticipate the impact of decisions by simulating the interaction of multiple agents in a system rather than just the independent actions of atomised and rational humans – took centre stage in the pandemic and emerged with reinforced importance in policymaking. Notably, it helped governments predict how and when to introduce lockdowns or open up. But even there uptake was limited. A recent survey showed that of the 28 models used in different countries to fight the pandemic were traditional, and not the modern “agent-based models” or “system dynamics” supposed to deal best with uncertainty. Meanwhile, the concepts of system science was becoming prominent and widely communicated. It became quickly clear in the course of the crisis that social distancing was more a method to reduce the systemic pressure on the health services than a way to avoid individual contagion (the so called “flatten the curve” project).

Open government data has long promised to allow citizens and businesses to build new services at scale and make government accountable. The pandemic largely confirmed how important this data could be to allow citizens to analyse things independently. Hundreds of analysts from all walks of life and disciplines used social media to discuss their analysis and predictions, many becoming household names and go-to people in countries and regions. Yes, this led to noise and a so-called “infodemic,” but overall it served as a fundamental tool to increase confidence and consensus behind the policy measures and to make governments accountable for their actions. For instance, one Catalan analyst demonstrated that vaccines were not provided during weekends and forced the government to change its stance. Yet it is also clear that not all went well, most notably on the supply side. Governments published data of low quality, either in PDF, with delays or with missing data due to spreadsheet abuse.

In most cases, there was little demand for sophisticated data publishing solutions such as “linked” or “FAIR” data, although particularly significant was the uptake of these kinds of solutions when it came time to share crucial research data. Experts argue that the trend towards open science has accelerated dramatically and irreversibly in the last year, as shown by the portal https://www.covid19dataportal.org/ which allowed sharing of high quality data for scientific research….

But other new policy tools proved less easy to use and ultimately ineffective. Collaborative governance, for one, promised to leverage the knowledge of thousands of citizens to improve public policies and services. In practice, methodologies aiming at involving citizens in decision making and service design were of little use. Decisions related to lockdown and opening up were taken in closed committees in top down mode. Individual exceptions certainly exist: Milan, one of the cities worst hit by the pandemic, launched a co-created strategy for opening up after the lockdown, receiving almost 3000 contributions to the consultation. But overall, such initiatives had limited impact and visibility. With regard to co-design of public services, in times of emergency there was no time for prototyping or focus groups. Services such as emergency financial relief had to be launched in a hurry and “just work.”…

Citizen science promised to make every citizen a consensual data source for monitoring complex phenomena in real time through apps and Internet-of-Things sensors. In the pandemic, there were initially great expectations on digital contact tracing apps to allow for real time monitoring of contagions, most notably through bluetooth connections in the phone. However, they were mostly a disappointment. Citizens were reluctant to install them. And contact tracing soon appeared to be much more complicated – and human intensive – than originally thought. The huge debate between technology and privacy was followed by very limited impact. Much ado about nothing.

Behavioural economics (commonly known as nudge theory) is probably the most visible failure of the pandemic. It promised to move beyond traditional carrots (public funding) and sticks (regulation) in delivering policy objectives by adopting an experimental method to influence or “nudge” human behaviour towards desired outcomes. The reality is that soft nudges proved an ineffective alternative to hard lockdown choices. What makes it uniquely negative is that such methods took centre stage in the initial phase of the pandemic and particularly informed the United Kingdom’s lax approach in the first months on the basis of a hypothetical and unproven “behavioural fatigue.” This attracted heavy criticism towards the excessive reliance on nudges by the United Kingdom government, a legacy of Prime Minister David Cameron’s administration. The origin of such criticisms seems to lie not in the method shortcomings per se, which enjoyed success previously on more specific cases, but in the backlash from excessive expectations and promises, epitomised in the quote of a prominent behavioural economist: “It’s no longer a matter of supposition as it was in 2010 […] we can now say with a high degree of confidence these models give you best policy.”

Three factors emerge as the key determinants behind success and failure: maturity, institutions and leadership….(More)”.

2030 Compass CoLab

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About: “2030 Compass CoLab invites a group of experts, using an online platform, to contribute their perspectives on potential interactions between the goals in the UN’s 2030 Agenda for Sustainable Development.

By combining the insight of participants who posses broad and diverse knowledge, we hope to develop a richer understanding of how the Sustainable Development Goals (SDGs) may be complementary or conflicting.

Compass 2030 CoLab is part of a larger project, The Agenda 2030 Compass Methodology and toolbox for strategic decision making, funded by Vinnova, Sweden’s government agency for innovation.

Other elements of the larger project include:

  • Deliberations by a panel of experts who will convene in a series of live meetings to undertake in-depth analysis on interactions between the goals. 
  • Quanitative analysis of SDG indicators time series data, which will examine historical correlations between progress on the SDGs.
  • Development of a knowledge repository, residing in a new software tool under development as part of the project. This tool will be made available as a resource to guide the decisions of corporate executives, policy makers, and leaders of NGOs.

The overall project was inspired by the work of researchers at the Stockholm Environment Institute, described in Towards systemic and contextual priority setting for implementing the 2030 Agenda, a 2018 paper in Sustainability Science by Nina Weitz, Henrik Carlsen, Måns Nilsson, and Kristian Skånberg….(More)”.