City of Copenhagen launches data marketplace

Curated on Posted on by Stefaan Verhulst

Sarah Wray at TMForum: “The City of Copenhagen has launched its City Data Exchange to make public and private data accessible to power innovation.

The City Data Exchange is a new service to create a ‘marketplace for data’ from public and private data providers and allow monetization. The platform has been developed by Hitachi Insight Group.

“Data is the fuel powering our digital world, but in most cities it is unused,” said Hans Lindeman, Senior Vice President, Hitachi Insight Group, EMEA. “Even where data sits in public, freely accessible databases, the cost of extracting and processing it can easily outweigh the benefits.”

The City of Copenhagen is using guidelines for a data format that is safe, secure, ensures privacy and makes data easy to use. The City Data Exchange will only accept data that has been fully anonymized by the data supplier, for example.

According to Hitachi Insight Group, “All of this spares organizations the trouble and cost of extracting and processing data from multiple sources. At the same time, proprietary data can now become a business resource that can be monetized outside an organization.”

As a way to demonstrate how data from the City Data Exchange could be used in applications, Hitachi Insight Group is developing two applications:

  • Journey Insight, which helps citizens in the region to track their transportation usage over time and understand the carbon footprint of their travel
  • Energy Insight, which allows both households and businesses to see how much energy they use.

Both are set for public launch later this year.

Another example of how data marketplaces can enable innovation is the Mind My Business mobile app, developed by Vizalytics. It brings together all the data that can affect a retailer — from real-time information on how construction or traffic issues can hurt the footfall of a business, to timely reminders about taxes to pay or new regulations to meet. The “survival app for shopkeepers” makes full use of all the relevant data sources brought together by the City Data Exchange.

The platform will offer data in different categories such as: city life, infrastructure, climate and environment, business data and economy, demographics, housing and buildings, and utilities usage. It aims to meet the needs of local government, city planners, architects, retailers, telecoms networks, utilities, and all other companies and organizations who want to understand what makes Copenhagen, its businesses and its citizens tick.

“Smart cities need smart insights, and that’s only possible if everybody has all the facts at their disposal. The City Data Exchange makes that possible; it’s the solution that will help us all to create better public spaces and — for companies in Copenhagen — to offer better services and create jobs,” said Frank Jensen, the Lord Mayor of Copenhagen.

The City Data Exchange is currently offering raw data to its customers, and later this year will add analytical tools. The cost of gathering and processing the data will be recovered through subscription and service fees, which are expected to be much lower than the cost any company or city would face in performing the work of extracting, collecting and integrating the data by themselves….(More)”

Selected Readings on Data Collaboratives

Curated on Posted on by Andrew Young

By Neil Britto, David Sangokoya, Iryna Susha, Stefaan Verhulst and Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of data collaboratives was originally published in 2017.

The term data collaborative refers to a new form of collaboration, beyond the public-private partnership model, in which participants from different sectors (including private companies, research institutions, and government agencies ) can exchange data to help solve public problems. Several of society’s greatest challenges — from addressing climate change to public health to job creation to improving the lives of children — require greater access to data, more collaboration between public – and private-sector entities, and an increased ability to analyze datasets. In the coming months and years, data collaboratives will be essential vehicles for harnessing the vast stores of privately held data toward the public good.

Selected Reading List (in alphabetical order)

Annotated Selected Readings List (in alphabetical order)

Agaba, G., Akindès, F., Bengtsson, L., Cowls, J., Ganesh, M., Hoffman, N., . . . Meissner, F. “Big Data and Positive Social Change in the Developing World: A White Paper for Practitioners and Researchers.” 2014. http://bit.ly/25RRC6N.

  • This white paper, produced by “a group of activists, researchers and data experts” explores the potential of big data to improve development outcomes and spur positive social change in low- and middle-income countries. Using examples, the authors discuss four areas in which the use of big data can impact development efforts:
    • Advocating and facilitating by “opening[ing] up new public spaces for discussion and awareness building;
    • Describing and predicting through the detection of “new correlations and the surfac[ing] of new questions;
    • Facilitating information exchange through “multiple feedback loops which feed into both research and action,” and
    • Promoting accountability and transparency, especially as a byproduct of crowdsourcing efforts aimed at “aggregat[ing] and analyz[ing] information in real time.
  • The authors argue that in order to maximize the potential of big data’s use in development, “there is a case to be made for building a data commons for private/public data, and for setting up new and more appropriate ethical guidelines.”
  • They also identify a number of challenges, especially when leveraging data made accessible from a number of sources, including private sector entities, such as:
    • Lack of general data literacy;
    • Lack of open learning environments and repositories;
    • Lack of resources, capacity and access;
    • Challenges of sensitivity and risk perception with regard to using data;
    • Storage and computing capacity; and
    • Externally validating data sources for comparison and verification.

Ansell, C. and Gash, A. “Collaborative Governance in Theory and Practice.” Journal of Public Administration Research and  Theory 18 (4), 2008. http://bit.ly/1RZgsI5.

  • This article describes collaborative arrangements that include public and private organizations working together and proposes a model for understanding an emergent form of public-private interaction informed by 137 diverse cases of collaborative governance.
  • The article suggests factors significant to successful partnering processes and outcomes include:
    • Shared understanding of challenges,
    • Trust building processes,
    • The importance of recognizing seemingly modest progress, and
    • Strong indicators of commitment to the partnership’s aspirations and process.
  • The authors provide a ‘’contingency theory model’’ that specifies relationships between different variables that influence outcomes of collaborative governance initiatives. Three “core contingencies’’ for successful collaborative governance initiatives identified by the authors are:
    • Time (e.g., decision making time afforded to the collaboration);
    • Interdependence (e.g., a high degree of interdependence can mitigate negative effects of low trust); and
    • Trust (e.g. a higher level of trust indicates a higher probability of success).

Ballivian A, Hoffman W. “Public-Private Partnerships for Data: Issues Paper for Data Revolution Consultation.” World Bank, 2015. Available from: http://bit.ly/1ENvmRJ

  • This World Bank report provides a background document on forming public-prviate partnerships for data with the private sector in order to inform the UN’s Independent Expert Advisory Group (IEAG) on sustaining a “data revolution” in sustainable development.
  • The report highlights the critical position of private companies within the data value chain and reflects on key elements of a sustainable data PPP: “common objectives across all impacted stakeholders, alignment of incentives, and sharing of risks.” In addition, the report describes the risks and incentives of public and private actors, and the principles needed to “build[ing] the legal, cultural, technological and economic infrastructures to enable the balancing of competing interests.” These principles include understanding; experimentation; adaptability; balance; persuasion and compulsion; risk management; and governance.
  • Examples of data collaboratives cited in the report include HP Earth Insights, Orange Data for Development Challenges, Amazon Web Services, IBM Smart Cities Initiative, and the Governance Lab’s Open Data 500.

Brack, Matthew, and Tito Castillo. “Data Sharing for Public Health: Key Lessons from Other Sectors.” Chatham House, Centre on Global Health Security. April 2015. Available from: http://bit.ly/1DHFGVl

  • The Chatham House report provides an overview on public health surveillance data sharing, highlighting the benefits and challenges of shared health data and the complexity in adapting technical solutions from other sectors for public health.
  • The report describes data sharing processes from several perspectives, including in-depth case studies of actual data sharing in practice at the individual, organizational and sector levels. Among the key lessons for public health data sharing, the report strongly highlights the need to harness momentum for action and maintain collaborative engagement: “Successful data sharing communities are highly collaborative. Collaboration holds the key to producing and abiding by community standards, and building and maintaining productive networks, and is by definition the essence of data sharing itself. Time should be invested in establishing and sustaining collaboration with all stakeholders concerned with public health surveillance data sharing.”
  • Examples of data collaboratives include H3Africa (a collaboration between NIH and Wellcome Trust) and NHS England’s care.data programme.

de Montjoye, Yves-Alexandre, Jake Kendall, and Cameron F. Kerry. “Enabling Humanitarian Use of Mobile Phone Data.” The Brookings Institution, Issues in Technology Innovation. November 2014. Available from: http://brook.gs/1JxVpxp

  • Using Ebola as a case study, the authors describe the value of using private telecom data for uncovering “valuable insights into understanding the spread of infectious diseases as well as strategies into micro-target outreach and driving update of health-seeking behavior.”
  • The authors highlight the absence of a common legal and standards framework for “sharing mobile phone data in privacy-conscientious ways” and recommend “engaging companies, NGOs, researchers, privacy experts, and governments to agree on a set of best practices for new privacy-conscientious metadata sharing models.”

Eckartz, Silja M., Hofman, Wout J., Van Veenstra, Anne Fleur. “A decision model for data sharing.” Vol. 8653 LNCS. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014. http://bit.ly/21cGWfw.

  • This paper proposes a decision model for data sharing of public and private data based on literature review and three case studies in the logistics sector.
  • The authors identify five categories of the barriers to data sharing and offer a decision model for identifying potential interventions to overcome each barrier:
    • Ownership. Possible interventions likely require improving trust among those who own the data through, for example, involvement and support from higher management
    • Privacy. Interventions include “anonymization by filtering of sensitive information and aggregation of data,” and access control mechanisms built around identity management and regulated access.  
    • Economic. Interventions include a model where data is shared only with a few trusted organizations, and yield management mechanisms to ensure negative financial consequences are avoided.
    • Data quality. Interventions include identifying additional data sources that could improve the completeness of datasets, and efforts to improve metadata.
    • Technical. Interventions include making data available in structured formats and publishing data according to widely agreed upon data standards.

Hoffman, Sharona and Podgurski, Andy. “The Use and Misuse of Biomedical Data: Is Bigger Really Better?” American Journal of Law & Medicine 497, 2013. http://bit.ly/1syMS7J.

  • This journal articles explores the benefits and, in particular, the risks related to large-scale biomedical databases bringing together health information from a diversity of sources across sectors. Some data collaboratives examined in the piece include:
    • MedMining – a company that extracts EHR data, de-identifies it, and offers it to researchers. The data sets that MedMining delivers to its customers include ‘lab results, vital signs, medications, procedures, diagnoses, lifestyle data, and detailed costs’ from inpatient and outpatient facilities.
    • Explorys has formed a large healthcare database derived from financial, administrative, and medical records. It has partnered with major healthcare organizations such as the Cleveland Clinic Foundation and Summa Health System to aggregate and standardize health information from ten million patients and over thirty billion clinical events.
  • Hoffman and Podgurski note that biomedical databases populated have many potential uses, with those likely to benefit including: “researchers, regulators, public health officials, commercial entities, lawyers,” as well as “healthcare providers who conduct quality assessment and improvement activities,” regulatory monitoring entities like the FDA, and “litigants in tort cases to develop evidence concerning causation and harm.”
  • They argue, however, that risks arise based on:
    • The data contained in biomedical databases is surprisingly likely to be incorrect or incomplete;
    • Systemic biases, arising from both the nature of the data and the preconceptions of investigators are serious threats the validity of research results, especially in answering causal questions;
  • Data mining of biomedical databases makes it easier for individuals with political, social, or economic agendas to generate ostensibly scientific but misleading research findings for the purpose of manipulating public opinion and swaying policymakers.

Krumholz, Harlan M., et al. “Sea Change in Open Science and Data Sharing Leadership by Industry.” Circulation: Cardiovascular Quality and Outcomes 7.4. 2014. 499-504. http://1.usa.gov/1J6q7KJ

  • This article provides a comprehensive overview of industry-led efforts and cross-sector collaborations in data sharing by pharmaceutical companies to inform clinical practice.
  • The article details the types of data being shared and the early activities of GlaxoSmithKline (“in coordination with other companies such as Roche and ViiV”); Medtronic and the Yale University Open Data Access Project; and Janssen Pharmaceuticals (Johnson & Johnson). The article also describes the range of involvement in data sharing among pharmaceutical companies including Pfizer, Novartis, Bayer, AbbVie, Eli Llly, AstraZeneca, and Bristol-Myers Squibb.

Mann, Gideon. “Private Data and the Public Good.” Medium. May 17, 2016. http://bit.ly/1OgOY68.

    • This Medium post from Gideon Mann, the Head of Data Science at Bloomberg, shares his prepared remarks given at a lecture at the City College of New York. Mann argues for the potential benefits of increasing access to private sector data, both to improve research and academic inquiry and also to help solve practical, real-world problems. He also describes a number of initiatives underway at Bloomberg along these lines.    
  • Mann argues that data generated at private companies “could enable amazing discoveries and research,” but is often inaccessible to those who could put it to those uses. Beyond research, he notes that corporate data could, for instance, benefit:
      • Public health – including suicide prevention, addiction counseling and mental health monitoring.
    • Legal and ethical questions – especially as they relate to “the role algorithms have in decisions about our lives,” such as credit checks and resume screening.
  • Mann recognizes the privacy challenges inherent in private sector data sharing, but argues that it is a common misconception that the only two choices are “complete privacy or complete disclosure.” He believes that flexible frameworks for differential privacy could open up new opportunities for responsibly leveraging data collaboratives.

Pastor Escuredo, D., Morales-Guzmán, A. et al, “Flooding through the Lens of Mobile Phone Activity.” IEEE Global Humanitarian Technology Conference, GHTC 2014. Available from: http://bit.ly/1OzK2bK

  • This report describes the impact of using mobile data in order to understand the impact of disasters and improve disaster management. The report was conducted in the Mexican state of Tabasco in 2009 as a multidisciplinary, multi-stakeholder consortium involving the UN World Food Programme (WFP), Telefonica Research, Technical University of Madrid (UPM), Digital Strategy Coordination Office of the President of Mexico, and UN Global Pulse.
  • Telefonica Research, a division of the major Latin American telecommunications company, provided call detail records covering flood-affected areas for nine months. This data was combined with “remote sensing data (satellite images), rainfall data, census and civil protection data.” The results of the data demonstrated that “analysing mobile activity during floods could be used to potentially locate damaged areas, efficiently assess needs and allocate resources (for example, sending supplies to affected areas).”
  • In addition to the results, the study highlighted “the value of a public-private partnership on using mobile data to accurately indicate flooding impacts in Tabasco, thus improving early warning and crisis management.”

* Perkmann, M. and Schildt, H. “Open data partnerships between firms and universities: The role of boundary organizations.” Research Policy, 44(5), 2015. http://bit.ly/25RRJ2c. 

  • This paper discusses the concept of a “boundary organization” in relation to industry-academic partnerships driven by data. Boundary organizations perform mediated revealing, allowing firms to disclose their research problems to a broad audience of innovators and simultaneously minimize the risk that this information would be adversely used by competitors.
  • The authors identify two especially important challenges for private firms to enter open data or participate in data collaboratives with the academic research community that could be addressed through more involvement from boundary organizations:
    • First is a challenge of maintaining competitive advantage. The authors note that, “the more a firm attempts to align the efforts in an open data research programme with its R&D priorities, the more it will have to reveal about the problems it is addressing within its proprietary R&D.”
    • Second, involves the misalignment of incentives between the private and academic field. Perkmann and Schildt argue that, a firm seeking to build collaborations around its opened data “will have to provide suitable incentives that are aligned with academic scientists’ desire to be rewarded for their work within their respective communities.”

Robin, N., Klein, T., & Jütting, J. “Public-Private Partnerships for Statistics: Lessons Learned, Future Steps.” OECD. 2016. http://bit.ly/24FLYlD.

  • This working paper acknowledges the growing body of work on how different types of data (e.g, telecom data, social media, sensors and geospatial data, etc.) can address data gaps relevant to National Statistical Offices (NSOs).
  • Four models of public-private interaction for statistics are describe: in-house production of statistics by a data-provider for a national statistics office (NSO), transfer of data-sets to NSOs from private entities, transfer of data to a third party provider to manage the NSO and private entity data, and the outsourcing of NSO functions.
  • The paper highlights challenges to public-private partnerships involving data (e.g., technical challenges, data confidentiality, risks, limited incentives for participation), suggests deliberate and highly structured approaches to public-private partnerships involving data require enforceable contracts, emphasizes the trade-off between data specificity and accessibility of such data, and the importance of pricing mechanisms that reflect the capacity and capability of national statistic offices.
  • Case studies referenced in the paper include:
    • A mobile network operator’s (MNO Telefonica) in house analysis of call detail records;
    • A third-party data provider and steward of travel statistics (Positium);
    • The Data for Development (D4D) challenge organized by MNO Orange; and
    • Statistics Netherlands use of social media to predict consumer confidence.

Stuart, Elizabeth, Samman, Emma, Avis, William, Berliner, Tom. “The data revolution: finding the missing millions.” Overseas Development Institute, 2015. Available from: http://bit.ly/1bPKOjw

  • The authors of this report highlight the need for good quality, relevant, accessible and timely data for governments to extend services into underrepresented communities and implement policies towards a sustainable “data revolution.”
  • The solutions focused on this recent report from the Overseas Development Institute focus on capacity-building activities of national statistical offices (NSOs), alternative sources of data (including shared corporate data) to address gaps, and building strong data management systems.

Taylor, L., & Schroeder, R. “Is bigger better? The emergence of big data as a tool for international development policy.” GeoJournal, 80(4). 2015. 503-518. http://bit.ly/1RZgSy4.

  • This journal article describes how privately held data – namely “digital traces” of consumer activity – “are becoming seen by policymakers and researchers as a potential solution to the lack of reliable statistical data on lower-income countries.
  • They focus especially on three categories of data collaborative use cases:
    • Mobile data as a predictive tool for issues such as human mobility and economic activity;
    • Use of mobile data to inform humanitarian response to crises; and
    • Use of born-digital web data as a tool for predicting economic trends, and the implications these have for LMICs.
  • They note, however, that a number of challenges and drawbacks exist for these types of use cases, including:
    • Access to private data sources often must be negotiated or bought, “which potentially means substituting negotiations with corporations for those with national statistical offices;”
    • The meaning of such data is not always simple or stable, and local knowledge is needed to understand how people are using the technologies in question
    • Bias in proprietary data can be hard to understand and quantify;
    • Lack of privacy frameworks; and
    • Power asymmetries, wherein “LMIC citizens are unwittingly placed in a panopticon staffed by international researchers, with no way out and no legal recourse.”

van Panhuis, Willem G., Proma Paul, Claudia Emerson, John Grefenstette, Richard Wilder, Abraham J. Herbst, David Heymann, and Donald S. Burke. “A systematic review of barriers to data sharing in public health.” BMC public health 14, no. 1 (2014): 1144. Available from: http://bit.ly/1JOBruO

  • The authors of this report provide a “systematic literature of potential barriers to public health data sharing.” These twenty potential barriers are classified in six categories: “technical, motivational, economic, political, legal and ethical.” In this taxonomy, “the first three categories are deeply rooted in well-known challenges of health information systems for which structural solutions have yet to be found; the last three have solutions that lie in an international dialogue aimed at generating consensus on policies and instruments for data sharing.”
  • The authors suggest the need for a “systematic framework of barriers to data sharing in public health” in order to accelerate access and use of data for public good.

Verhulst, Stefaan and Sangokoya, David. “Mapping the Next Frontier of Open Data: Corporate Data Sharing.” In: Gasser, Urs and Zittrain, Jonathan and Faris, Robert and Heacock Jones, Rebekah, “Internet Monitor 2014: Reflections on the Digital World: Platforms, Policy, Privacy, and Public Discourse (December 15, 2014).” Berkman Center Research Publication No. 2014-17. http://bit.ly/1GC12a2

  • This essay describe a taxonomy of current corporate data sharing practices for public good: research partnerships; prizes and challenges; trusted intermediaries; application programming interfaces (APIs); intelligence products; and corporate data cooperatives or pooling.
  • Examples of data collaboratives include: Yelp Dataset Challenge, the Digital Ecologies Research Partnerhsip, BBVA Innova Challenge, Telecom Italia’s Big Data Challenge, NIH’s Accelerating Medicines Partnership and the White House’s Climate Data Partnerships.
  • The authors highlight important questions to consider towards a more comprehensive mapping of these activities.

Verhulst, Stefaan and Sangokoya, David, 2015. “Data Collaboratives: Exchanging Data to Improve People’s Lives.” Medium. Available from: http://bit.ly/1JOBDdy

  • The essay refers to data collaboratives as a new form of collaboration involving participants from different sectors exchanging data to help solve public problems. These forms of collaborations can improve people’s lives through data-driven decision-making; information exchange and coordination; and shared standards and frameworks for multi-actor, multi-sector participation.
  • The essay cites four activities that are critical to accelerating data collaboratives: documenting value and measuring impact; matching public demand and corporate supply of data in a trusted way; training and convening data providers and users; experimenting and scaling existing initiatives.
  • Examples of data collaboratives include NIH’s Precision Medicine Initiative; the Mobile Data, Environmental Extremes and Population (MDEEP) Project; and Twitter-MIT’s Laboratory for Social Machines.

Verhulst, Stefaan, Susha, Iryna, Kostura, Alexander. “Data Collaboratives: matching Supply of (Corporate) Data to Solve Public Problems.” Medium. February 24, 2016. http://bit.ly/1ZEp2Sr.

  • This piece articulates a set of key lessons learned during a session at the International Data Responsibility Conference focused on identifying emerging practices, opportunities and challenges confronting data collaboratives.
  • The authors list a number of privately held data sources that could create positive public impacts if made more accessible in a collaborative manner, including:
    • Data for early warning systems to help mitigate the effects of natural disasters;
    • Data to help understand human behavior as it relates to nutrition and livelihoods in developing countries;
    • Data to monitor compliance with weapons treaties;
    • Data to more accurately measure progress related to the UN Sustainable Development Goals.
  • To the end of identifying and expanding on emerging practice in the space, the authors describe a number of current data collaborative experiments, including:
    • Trusted Intermediaries: Statistics Netherlands partnered with Vodafone to analyze mobile call data records in order to better understand mobility patterns and inform urban planning.
    • Prizes and Challenges: Orange Telecom, which has been a leader in this type of Data Collaboration, provided several examples of the company’s initiatives, such as the use of call data records to track the spread of malaria as well as their experience with Challenge 4 Development.
    • Research partnerships: The Data for Climate Action project is an ongoing large-scale initiative incentivizing companies to share their data to help researchers answer particular scientific questions related to climate change and adaptation.
    • Sharing intelligence products: JPMorgan Chase shares macro economic insights they gained leveraging their data through the newly established JPMorgan Chase Institute.
  • In order to capitalize on the opportunities provided by data collaboratives, a number of needs were identified:
    • A responsible data framework;
    • Increased insight into different business models that may facilitate the sharing of data;
    • Capacity to tap into the potential value of data;
    • Transparent stock of available data supply; and
    • Mapping emerging practices and models of sharing.

Vogel, N., Theisen, C., Leidig, J. P., Scripps, J., Graham, D. H., & Wolffe, G. “Mining mobile datasets to enable the fine-grained stochastic simulation of Ebola diffusion.” Paper presented at the Procedia Computer Science. 2015. http://bit.ly/1TZDroF.

  • The paper presents a research study conducted on the basis of the mobile calls records shared with researchers in the framework of the Data for Development Challenge by the mobile operator Orange.
  • The study discusses the data analysis approach in relation to developing a situation of Ebola diffusion built around “the interactions of multi-scale models, including viral loads (at the cellular level), disease progression (at the individual person level), disease propagation (at the workplace and family level), societal changes in migration and travel movements (at the population level), and mitigating interventions (at the abstract government policy level).”
  • The authors argue that the use of their population, mobility, and simulation models provide more accurate simulation details in comparison to high-level analytical predictions and that the D4D mobile datasets provide high-resolution information useful for modeling developing regions and hard to reach locations.

Welle Donker, F., van Loenen, B., & Bregt, A. K. “Open Data and Beyond.” ISPRS International Journal of Geo-Information, 5(4). 2016. http://bit.ly/22YtugY.

  • This research has developed a monitoring framework to assess the effects of open (private) data using a case study of a Dutch energy network administrator Liander.
  • Focusing on the potential impacts of open private energy data – beyond ‘smart disclosure’ where citizens are given information only about their own energy usage – the authors identify three attainable strategic goals:
    • Continuously optimize performance on services, security of supply, and costs;
    • Improve management of energy flows and insight into energy consumption;
    • Help customers save energy and switch over to renewable energy sources.
  • The authors propose a seven-step framework for assessing the impacts of Liander data, in particular, and open private data more generally:
    • Develop a performance framework to describe what the program is about, description of the organization’s mission and strategic goals;
    • Identify the most important elements, or key performance areas which are most critical to understanding and assessing your program’s success;
    • Select the most appropriate performance measures;
    • Determine the gaps between what information you need and what is available;
    • Develop and implement a measurement strategy to address the gaps;
    • Develop a performance report which highlights what you have accomplished and what you have learned;
    • Learn from your experiences and refine your approach as required.
  • While the authors note that the true impacts of this open private data will likely not come into view in the short term, they argue that, “Liander has successfully demonstrated that private energy companies can release open data, and has successfully championed the other Dutch network administrators to follow suit.”

World Economic Forum, 2015. “Data-driven development: pathways for progress.” Geneva: World Economic Forum. http://bit.ly/1JOBS8u

  • This report captures an overview of the existing data deficit and the value and impact of big data for sustainable development.
  • The authors of the report focus on four main priorities towards a sustainable data revolution: commercial incentives and trusted agreements with public- and private-sector actors; the development of shared policy frameworks, legal protections and impact assessments; capacity building activities at the institutional, community, local and individual level; and lastly, recognizing individuals as both produces and consumers of data.

Society’s biggest problems need more than a nudge

Curated on Posted on by Stefaan Verhulst

 at the Conversation: “So-called “nudge units” are popping up in governments all around the world.

The best-known examples include the U.K.’s Behavioural Insights Team, created in 2010, and the White House-based Social and Behavioral Sciences Team, introduced by the Obama administration in 2014. Their mission is to leverage findings from behavioral science so that people’s decisions can be nudged in the direction of their best intentions without curtailing their ability to make choices that don’t align with their priorities.

Overall, these – and other – governments have made important strides when it comes to using behavioral science to nudge their constituents into better choices.

Yet, the same governments have done little to improve their own decision-making processes. Consider big missteps like the Flint water crisis. How could officials in Michigan decide to place an essential service – safe water – and almost 100,000 people at risk in order to save US$100 per day for three months? No defensible decision-making process should have allowed this call to be made.

When it comes to many of the big decisions faced by governments – and the private sector – behavioral science has more to offer than simple nudges.

Behavioral scientists who study decision-making processes could also help policy-makers understand why things went wrong in Flint, and how to get their arms around a wide array of society’s biggest problems – from energy transitions to how to best approach the refugee crisis in Syria.

When nudges are enough

The idea of nudging people in the direction of decisions that are in their own best interest has been around for a while. But it was popularized in 2008 with the publication of the bestseller “Nudge“ by Richard Thaler of the University of Chicago and Cass Sunstein of Harvard.

A common nudge goes something like this: if we want to eat better but are having a hard time doing it, choice architects can reengineer the environment in which we make our food choices so that healthier options are intuitively easier to select, without making it unrealistically difficult to eat junk food if that’s what we’d rather do. So, for example, we can shelve healthy foods at eye level in supermarkets, with less-healthy options relegated to the shelves nearer to the floor….

Sometimes a nudge isn’t enough

Nudges work for a wide array of choices, from ones we face every day to those that we face infrequently. Likewise, nudges are particularly well-suited to decisions that are complex with lots of different alternatives to choose from. And, they are advocated in situations where the outcomes of our decisions are delayed far enough into the future that they feel uncertain or abstract. This describes many of the big decisions policy-makers face, so it makes sense to think the solution must be more nudge units.

But herein lies the rub. For every context where a nudge seems like a realistic option, there’s at least another context where the application of passive decision support would be either be impossible – or, worse, a mistake.

Take, for example, the question of energy transitions. These transitions are often characterized by the move from infrastructure based on fossil fuels to renewables to address all manner of risks, including those from climate change. These are decisions that society makes infrequently. They are complex. And, the outcomes – which are based on our ability to meet conflicting economic, social and environmental objectives – will be delayed.

But, absent regulation that would place severe restrictions on the kinds of options we could choose from – and which, incidentally, would violate the freedom-of-choice tenet of choice architecture – there’s no way to put renewable infrastructure options at proverbial eye level for state or federal decision-makers, or their stakeholders.

Simply put, a nudge for a decision like this would be impossible. In these cases, decisions have to be made the old-fashioned way: with a heavy lift instead of a nudge.

Complex policy decisions like this require what we call active decision support….(More)”

Insights On Collective Problem-Solving: Complexity, Categorization And Lessons From Academia

Curated on Posted on by Stefaan Verhulst

Part 3 of an interview series by Henry Farrell for the MacArthur Research Network on Opening Governance: “…Complexity theorists have devoted enormous energy and attention to thinking about how complex problems, in which different factors interact in ways that are hard to predict, can best be solved. One key challenge is categorizing problems, so as to understand which approaches are best suited to addressing them.

Scott Page is the Leonid Hurwicz Collegiate Professor of Complex Systems at the University of Michigan, Ann Arbor, and one of the world’s foremost experts on diversity and problem-solving. I asked him a series of questions about how we might use insights from academic research to think better about how problem solving works.

Henry: One of the key issues of collective problem-solving is what you call the ‘problem of problems’ – the question of identifying which problems we need to solve. This is often politically controversial – e.g., it may be hard to get agreement that global warming, or inequality, or long prison sentences are a problem. How do we best go about identifying problems, given that people may disagree?

Scott: In a recent big think paper on the potential of diversity for collective problem solving in Scientific American, Katherine Phillips writes that group members must feel validated, that they must share a commitment to the group, and they must have a common goal if they are going to contribute. This implies that you won’t succeed in getting people to collaborate by setting an agenda from on high and then seeking to attract diverse people to further that agenda.

One way of starting to tackle the problem of problems is to steal a rule of thumb from Getting to Yes, by getting to think people about their broad interests rather than the position that they’re starting from. People often agree on their fundamental desires but disagree on how they can be achieved. For example, nearly everyone wants less crime, but they may disagree over whether they think the solution to crime involves tackling poverty or imposing longer prison sentences. If you can get them to focus on their common interest in solving crime rather than their disagreements, you’re more likely to get them to collaborate usefully.

Segregation amplifies the problem of problems. We live in towns and neighborhoods segregated by race, income, ideology, and human capital. Democrats live near Democrats and Republicans near Republicans. Consensus requires integration. We must work across ideologies. Relatedly, opportunity requires more than access. Many people grow up not knowing any engineers, dentists, doctors, lawyers, and statisticians. This isolation narrows the set of careers they consider and it reduces the diversity of many professions. We cannot imagine lives we do not know.

Henry: Once you get past the problem of problems, you still need to identify which kind of problem you are dealing with. You identify three standard types of problems: solution problems, selection problems and optimization problems. What – very briefly – are the key differences between these kinds of problems?

Scott: I’m constantly pondering the potential set of categories in which collective intelligence can emerge. I’m teaching a course on collective intelligence this semester and the undergraduates and I developed an acronym SCARCE PIGS to describe the different types of domains. Here’s the brief summary:

  • Predict: when individuals combine information, models, or measurements to estimate a future event, guess an answer, or classify an event. Examples might involve betting markets, or combined efforts to guess a quantity, such as Francis Galton’s example of people at a fair trying to guess the weight of a steer.
  • Identify: when individuals have local, partial, or possibly erroneous knowledge and collectively can find an object. Here, an example is DARPA’s Red Balloon project.
  • Solve: when individuals apply and possibly combine higher order cognitive processes and analytic tools for the purpose of finding or improving a solution to a task. Innocentive and similar organizations provide examples of this.
  • Generate: when individuals apply diverse representations, heuristics, and knowledge to produce something new. An everyday example is creating a new building.
  • Coordinate: when individuals adopt similar actions, behaviors, beliefs, or mental frameworks by learning through local interactions. Ordinary social conventions such as people greeting each other are good examples.
  • Cooperate: when individuals take actions, not necessarily in their self interest, that collectively produce a desirable outcome. Here, think of managing common pool resources (e.g. fishing boats not overfishing an area that they collectively control).
  • Arrange: when individuals manipulate items in a physical or virtual environment for their own purposes resulting in an organization of that environment. As an example, imagine a student co-op which keeps twenty types of hot sauce in its pantry. If each student puts whichever hot sauce she uses in the front of the pantry, then on average, the hot sauces will be arranged according to popularity, with the most favored hot sauces in the front and the least favored lost in the back.
  • Respond: when individuals react to external or internal stimuli creating collective responses that maintains system level functioning. For example, when yellow jackets attack a predator to maintain the colony, they are displaying this kind of problem solving.
  • Emerge: when individual parts create a whole that has categorically distinct and new functionalities. The most obvious example of this is the human brain….(More)”

Workplace innovation in the public sector

Curated on Posted on by Stefaan Verhulst

Eurofound: “Innovative organisational practices in the workplace, which aim to make best use of human capital, are traditionally associated with the private sector. The nature of the public sector activities makes it more difficult to identify these types of internal innovation in publicly funded organisations.

It is widely thought that public sector organisations are neither dynamic nor creative and are typified by a high degree of inertia. Yet the necessity of innovation ought not to be dismissed. The public sector represents a quarter of total EU employment, and it is of critical importance as a provider and regulator of services. Improving how it performs has a knock-on effect not only for private sector growth but also for citizens’ satisfaction. Ultimately, this improves governance itself.

So how can innovative organisation practices help in dealing with the challenges faced by the public sector? Eurofound, as part of a project on workplace innovation in European companies, carried out case studies of both private and public sector organisations. The findings show a number of interesting practices and processes used.

Employee participation

The case studies from the public sector, some of which are described below, demonstrate the central role of employee participation in the implementation of workplace innovation and its impacts on organisation and employees. They indicate that innovative practices have resulted in enhanced organisational performance and quality of working life.

It is widely thought that changes in the public sector are initiated as a response to government policies. This is often true, but workplace innovation may also be introduced as a result of well-designed initiatives driven by external pressures (such as the need for a more competitive public service) or internal pressures (such as a need to update the skills map to better serve the public).

Case study findings

The state-owned Lithuanian energy company Lietuvos Energijos Gamyba (140 KB PDF) encourages employee participation by providing a structured framework for all employees to propose improvements. This has required a change in managerial approach and has spread a sense of ownership horizontally and vertically in the company. The Polish public transport company Jarosław City Transport (191 KB PDF), when faced with serious financial stability challenges, as well as implementing operational changes, set up ways for employees’ voices to be heard, which enabled a contributory dialogue and strengthened partnerships. Consultation, development of mutual trust, and common involvement ensured an effective combination of top-down and bottom-up initiatives.

The Lithuanian Post, AB Lietuvos Pastas (136 KB PDF) experienced a major organisation transformation in 2010 to improve efficiency and quality of service. Through a programme of ‘Loyalty day’ monthly visits, both top and middle management of the central administration visit any part of the company and work with colleagues in other units. Under budgetary pressure to ‘earn their money’, the Danish Vej and Park Bornholm (142 KB PDF) construction services in roads, parks and forests had to find innovative solutions to deal with a merger and privatisation. Their intervention had the characteristics of workplace partnership with a new set of organisational values set from the bottom up. Self-managing teams are essential for the operation of the company.

The world of education has provided new structures that provide better outcomes for students. The South West University of Bulgaria (214 KB PDF) also operates small self-managing teams responsible for employee scheduling. Weekly round-tables encourage participation in collectively finding solutions, creating a more effective environment in which to respond to the competitive demands of education provision.

In Poland, an initiative by the Pomeranian Library (185 KB PDF) improved employee–management dialogue and communication through increased participation. The initiative is a response to the new frameworks for open access to knowledge for users, with the library mirroring the user experience through its own work practices.

Through new dialogue, government advisory bodies have also developed employee-led improvement. Breaking away from a traditional hierarchy is considered important in achieving a more flexible work organisation. Under considerable pressure, the top-heavy management of the British Geological Survey (89 KB PDF) now operates a flexible matrix that promotes innovative and entrepreneurial ways of working. And in Germany, Niersverband (138 KB PDF), a publicly owned water-management company innovated through training, learning, reflection partnerships and workplace partnerships. New occupational profiles were developed to meet external demands. Based on dialogue concerning workplace experiences and competences, employees acquired new qualifications that allowed the company to be more competitive.

In the Funen Village Museum in Odense, Denmark, (143 KB PDF) innovation came about at the request of staff looking for more flexibility in how they work. Formerly most of their work was maintenance tasks, but now they can now engage more with visitors. Control of schedules has moved to the team rather than being the responsibility of a single manager. As a result, museum employees are now hosts as well as craftspeople. They no longer feel ‘forgotten’ and are happier in their work….(More)”

The report Workplace innovation in European companies provides a full analysis of the case studies.

The 51 case studies and the  list of companies (PDF 119 KB) the case studies are based on are available for download.

Regulatory Transformations: An Introduction

Curated on Posted on by Stefaan Verhulst

Chapter by Bettina Lange and Fiona Haines in the book Regulatory Transformations: “Regulation is no longer the prerogative of either states or markets. Increasingly citizens in association with businesses catalyse regulation which marks the rise of a social sphere in regulation. Around the world, in San Francisco, Melbourne, Munich and Mexico City, citizens have sought to transform how and to what end economic transactions are conducted. For instance, ‘carrot mob’ initiatives use positive economic incentives, not provided by a state legal system, but by a collective of civil society actors in order to change business behaviour. In contrast to ‘negative’ consumer boycotts, ‘carrotmob’ events use ‘buycotts’. They harness competition between businesses as the lever for changing how and for what purpose business transactions are conducted. Through new social media ‘carrotmobs’ mobilize groups of citizens to purchase goods at a particular time in a specific shop. The business that promises to spend the greatest percentage of its takings on, for instance, environmental improvements, such as switching to a supplier of renewable energy, will be selected for an organized shopping spree and financially benefit from the extra income it receives from the ‘carrot mob’ event.’Carrot mob’ campaigns chime with other fundamental challenges to conventional economic activity, such as the shared use of consumer goods through citizens collective consumption which questions traditional conceptions of private property….(More; Other Chapters)”

 

From Stalemate to Solutions

Curated on Posted on by Stefaan Verhulst

Karen Abrams Gerber & Andrea Jacobs  at Stanford Social Innovation Review: “….We waste time asking, “How can we change the way people think?” when we should be asking, “How do we change the way we do things?”

Changing how we do things isn’t just about reworking laws, policies, and systems; it means rethinking the very act of problem-solving. We believe there are five basic tenets to successful collaboration:

  1. Engaging unlikely bedfellows
  2. Creating a resonant vision
  3. Cultivating relationships
  4. Communicating across worldviews
  5. Committing to ongoing learning

Over the past two years, we’ve researched an organization that embodies all of these: Convergence Center for Policy Resolution “convenes people and groups with conflicting views to build trust, identify solutions, and form alliances for action on critical national issues.” Its projects include reimagining K-12 education, addressing economic mobility and poverty, reforming the federal budget process, financing long-term care, and improving the dietary choices and wellness of Americans.

The organization’s unique approach to collaboration enables adversaries to work together and develop breakthrough solutions. It starts with targeting and framing an issue, and then enrolling a wide spectrum of stakeholders. Over an extended period of time, these stakeholders attend a series of expertly facilitated meetings to explore the issue and identify solutions, and finally take joint action….

Foundational to Convergence’s success is the principle of engaging unlikely bedfellows. Stakeholder diversity helps eliminate the “echo chamber” effect (also described by Witter and Mikulsky) created when like-minded groups talk only with one another. The organization vets potential stakeholders to determine their capacity for working with the tensions and complexities of diverse perspectives and their willingness to participate in an emergent process, believing that each ideological camp holds a crucial piece of the puzzle and that the tension of differing views actually creates better solutions.

Convergence exemplifies the power of creating a resonant vision in its approach to tackling big social issues. Framing the issue in a way that galvanizes all stakeholders takes tremendous time, energy, and skill. For example, when the organization decided to focus on addressing K-12 education in the United States, it engaged in hundreds of interviews to identify the best way to frame the project. While everyone agreed the system did not serve the needs of many students, they had difficulty finding consensus about how to move forward. One stakeholder commented that the current system was based on a 19th-century factory model that could never meet the needs of 21st-century students. This comment sparked a new narrative that excited stakeholders across the ideological spectrum: “reimagining education for the 21st century!”

It’s important to note that Convergence focuses on framing the problem, not formulating the solution(s). Rather, it believes the solution emerges through the process of authentic collaboration. This differs significantly from an advocacy-based approach, in which a group agrees on a solution and then mobilizes as much support for that solution as possible. As a result, solutions created through Convergence’s collaborative approach are better able to weather the resistance that all change efforts face, because some of that resistance is built into the process.

Change takes time, and so does cultivating relationships. In an article last year, Jane Wei-Skillern, David Ehrlichman, and David Sawyer wrote, “The single most important factor behind all successful collaborations is trust-based relationships among participants.”…..

Change is complex and certainly not linear. Convergence’s approach “lives” this complexity and uncertainty. In its own words, the organization is “building the ship while sailing it.” Its success is due in part to actively and simultaneously engaging each of the five tenets of authentic collaboration, and its work demonstrates the powerful possibilities of authentic collaboration at a time when partisan rancor and stalemate feel inevitable. It proves we can change the world—collaboratively—without anyone relinquishing their core values….(More)”

The Open Data Barometer (3rd edition)

Curated on Posted on by Stefaan Verhulst

The Open Data Barometer: “Once the preserve of academics and statisticians, data has become a development cause embraced by everyone from grassroots activists to the UN Secretary-General. There’s now a clear understanding that we need robust data to drive democracy and development — and a lot of it.

Last year, the world agreed the Sustainable Development Goals (SDGs) — seventeen global commitments that set an ambitious agenda to end poverty, fight inequality and tackle climate change by 2030. Recognising that good data is essential to the success of the SDGs, the Global Partnership for Sustainable Development Data and the International Open Data Charter were launched as the SDGs were unveiled. These alliances mean the “data revolution” now has over 100 champions willing to fight for it. Meanwhile, Africa adopted the African Data Consensus — a roadmap to improving data standards and availability in a region that has notoriously struggled to capture even basic information such as birth registration.

But while much has been made of the need for bigger and better data to power the SDGs, this year’s Barometer follows the lead set by the International Open Data Charter by focusing on how much of this data will be openly available to the public.

Open data is essential to building accountable and effective institutions, and to ensuring public access to information — both goals of SDG 16. It is also essential for meaningful monitoring of progress on all 169 SDG targets. Yet the promise and possibilities offered by opening up data to journalists, human rights defenders, parliamentarians, and citizens at large go far beyond even these….

At a glance, here are this year’s key findings on the state of open data around the world:

    • Open data is entering the mainstream.The majority of the countries in the survey (55%) now have an open data initiative in place and a national data catalogue providing access to datasets available for re-use. Moreover, new open data initiatives are getting underway or are promised for the near future in a number of countries, including Ecuador, Jamaica, St. Lucia, Nepal, Thailand, Botswana, Ethiopia, Nigeria, Rwanda and Uganda. Demand is high: civil society and the tech community are using government data in 93% of countries surveyed, even in countries where that data is not yet fully open.
    • Despite this, there’s been little to no progress on the number of truly open datasets around the world.Even with the rapid spread of open government data plans and policies, too much critical data remains locked in government filing cabinets. For example, only two countries publish acceptable detailed open public spending data. Of all 1,380 government datasets surveyed, almost 90% are still closed — roughly the same as in the last edition of the Open Data Barometer (when only 130 out of 1,290 datasets, or 10%, were open). What is more, much of the approximately 10% of data that meets the open definition is of poor quality, making it difficult for potential data users to access, process and work with it effectively.
    • “Open-washing” is jeopardising progress. Many governments have advertised their open data policies as a way to burnish their democratic and transparent credentials. But open data, while extremely important, is just one component of a responsive and accountable government. Open data initiatives cannot be effective if not supported by a culture of openness where citizens are encouraged to ask questions and engage, and supported by a legal framework. Disturbingly, in this edition we saw a backslide on freedom of information, transparency, accountability, and privacy indicators in some countries. Until all these factors are in place, open data cannot be a true SDG accelerator.
    • Implementation and resourcing are the weakest links.Progress on the Barometer’s implementation and impact indicators has stalled or even gone into reverse in some cases. Open data can result in net savings for the public purse, but getting individual ministries to allocate the budget and staff needed to publish their data is often an uphill battle, and investment in building user capacity (both inside and outside of government) is scarce. Open data is not yet entrenched in law or policy, and the legal frameworks supporting most open data initiatives are weak. This is a symptom of the tendency of governments to view open data as a fad or experiment with little to no long-term strategy behind its implementation. This results in haphazard implementation, weak demand and limited impact.
    • The gap between data haves and have-nots needs urgent attention.Twenty-six of the top 30 countries in the ranking are high-income countries. Half of open datasets in our study are found in just the top 10 OECD countries, while almost none are in African countries. As the UN pointed out last year, such gaps could create “a whole new inequality frontier” if allowed to persist. Open data champions in several developing countries have launched fledgling initiatives, but too often those good open data intentions are not adequately resourced, resulting in weak momentum and limited success.
    • Governments at the top of the Barometer are being challenged by a new generation of open data adopters. Traditional open data stalwarts such as the USA and UK have seen their rate of progress on open data slow, signalling that new political will and momentum may be needed as more difficult elements of open data are tackled. Fortunately, a new generation of open data adopters, including France, Canada, Mexico, Uruguay, South Korea and the Philippines, are starting to challenge the ranking leaders and are adopting a leadership attitude in their respective regions. The International Open Data Charter could be an important vehicle to sustain and increase momentum in challenger countries, while also stimulating renewed energy in traditional open data leaders….(More)”

A New Dark Age Looms

Curated on Posted on by Stefaan Verhulst

William B. Gail in the New York Times: “Imagine a future in which humanity’s accumulated wisdom about Earth — our vast experience with weather trends, fish spawning and migration patterns, plant pollination and much more — turns increasingly obsolete. As each decade passes, knowledge of Earth’s past becomes progressively less effective as a guide to the future. Civilization enters a dark age in its practical understanding of our planet.

To comprehend how this could occur, picture yourself in our grandchildren’s time, a century hence. Significant global warming has occurred, as scientists predicted. Nature’s longstanding, repeatable patterns — relied on for millenniums by humanity to plan everything from infrastructure to agriculture — are no longer so reliable. Cycles that have been largely unwavering during modern human history are disrupted by substantial changes in temperature and precipitation….

Our foundation of Earth knowledge, largely derived from historically observed patterns, has been central to society’s progress. Early cultures kept track of nature’s ebb and flow, passing improved knowledge about hunting and agriculture to each new generation. Science has accelerated this learning process through advanced observation methods and pattern discovery techniques. These allow us to anticipate the future with a consistency unimaginable to our ancestors.

But as Earth warms, our historical understanding will turn obsolete faster than we can replace it with new knowledge. Some patterns will change significantly; others will be largely unaffected, though it will be difficult to say what will change, by how much, and when.

The list of possible disruptions is long and alarming. We could see changes to the prevalence of crop and human pests, like locust plagues set off by drought conditions; forest fire frequency; the dynamics of the predator-prey food chain; the identification and productivity of reliably arable land, and the predictability of agriculture output.

Historians of the next century will grasp the importance of this decline in our ability to predict the future. They may mark the coming decades of this century as the period during which humanity, despite rapid technological and scientific advances, achieved “peak knowledge” about the planet it occupies. They will note that many decades may pass before society again attains the same level.

One exception to this pattern-based knowledge is the weather, whose underlying physics governs how the atmosphere moves and adjusts. Because we understand the physics, we can replicate the atmosphere with computer models. Monitoring by weather stations and satellites provides the starting point for the models, which compute a forecast for how the weather will evolve. Today, forecast accuracy based on such models is generally good out to a week, sometimes even two.

But farmers need to think a season or more ahead. So do infrastructure planners as they design new energy and water systems. It may be feasible to develop the science and make the observations necessary to forecast weather a month or even a season in advance. We are also coming to understand enough of the physics to make useful global and regional climate projections a decade or more ahead.

The intermediate time period is our big challenge. Without substantial scientific breakthroughs, we will remain reliant on pattern-based methods for time periods between a month and a decade. … Our best knowledge is built on what we have seen in the past, like how fish populations respond to El Niño’s cycle. Climate change will further undermine our already limited ability to make these predictions. Anticipating ocean resources from one year to the next will become harder.

Civilization’s understanding of Earth has expanded enormously in recent decades, making humanity safer and more prosperous. As the patterns that we have come to expect are disrupted by warming temperatures, we will face huge challenges feeding a growing population and prospering within our planet’s finite resources. New developments in science offer our best hope for keeping up, but this is by no means guaranteed….(More)”

Open Data and Beyond

Curated on Posted on by Stefaan Verhulst

Paper by Frederika Welle Donker, Bastiaan van Loenen and Arnold K. Bregt: “In recent years, there has been an increasing trend of releasing public sector information as open data. Governments worldwide see the potential benefits of opening up their data. The potential benefits are more transparency, increased governmental efficiency and effectiveness, and external benefits, including societal and economic benefits. The private sector also recognizes potential benefits of making their datasets available as open data. One such company is Liander, an energy network administrator in the Netherlands. Liander views open data as a contributing factor to energy conservation. However, to date there has been little research done into the actual effects of open data. This research has developed a monitoring framework to assess the effects of open data, and has applied the framework to Liander’s small-scale energy consumption dataset….(More)“