Selected Readings on Data Collaboratives


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.

Workplace innovation in the public sector


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.

Global governance and ICTs: exploring online governance networks around gender and media


Claudia Padovani and Elena Pavan in the journal “Global Networks“: In this article, we address transformations in global governance brought about by information and communication technologies (ICTs). Focusing on the specific domain of ‘gender-oriented communication governance’, we investigate online interactions among different kinds of actors active in promoting gender equity in and through the media. By tracing and analysing online issue networks, we investigate which actors are capable of influencing the framing of issues and of structuring discursive practices. From the analysis, different forms of power emerge, reflecting diverse modes of engaging in online interactions, where actors can operate as network ‘programmers’, ‘mobilizers’, or ‘switchers’. Our case study suggests that, often, old ways of conceiving actors’ interactions accompany the implementation of new communication tools, while the availability of a pervasive networked infrastructure does not automatically translate into meaningful interactions among all relevant actors in a specific domain….(More)”

The era of development mutants


Guilo Quaggiotto at Nesta: “If you were looking for the cutting edge of the development sector, where would you go these days? You would probably look at startups like Premise who have predicted food trends 25 days faster than national statistics in Brazil, or GiveDirectly who are pushing the boundaries on evidence – from RCTs to new ways of mapping poverty – to fast track the adoption of cash transfers.

Or perhaps you might draw your attention to PetaJakarta who are experimenting with new responses to crises by harnessing human sensor networks. You might be tempted to consider Airbnb’s Disaster Response programme as an indicator of an emerging alternative infrastructure for disaster response (and perhaps raising questions about the political economy of this all).

And could Bitnation’s Refugee Emergency programme in response to the European refugee crisis be the possible precursor of future solutions for transnational issues – among the development sector’s hardest challenges? Are the business models of One Acre Fund, which provides services for smallholder farmers, or Floodtags, which analyses citizen data during floods for water and disaster managers, an indicator of future pathways to scale – that elusive development unicorn?

If you want to look at the future of procuring solutions for the development sector, should you be looking at initiatives like Citymart, which works with municipalities across the world to rethink traditional procurement and unleash the expertise and innovation capabilities of their citizens? By the same token, projects like Pathogen Box, Poverty Stoplight or Patient Innovation point to a brave new world where lead-user innovation and harnessing ‘sticky’ local knowledge becomes the norm, rather than the exception. You would also be forgiven for thinking that social movements across the world are the place to look for signs of future mechanisms for harnessing collective intelligence – Kawal Pamilu’s “citizen experts” self-organising around the Indonesian elections in 2014 is a textbook case study in this department.

The list could go on and on: welcome to the era of development mutants. While established players in the development sector are engrossed in soul-searching and their fitness for purpose is being scrutinised from all quarters, a whole new set of players is emerging, unfettered by legacy and borrowing from a variety of different disciplines. They point to a potentially different future – indeed, many potentially different futures – for the sector…..

But what if we wanted to invert this paradigm? How could we move from denial to fruitful collaboration with the ‘edgeryders’ of the development sector and accelerate its transformation?

Adopting new programming principles

Based on our experience working with development organisations, we believe that partnering with the mutants involves two types of shifts for traditional players: at the programmatic and the operational level. At the programmatic level, our work on the ground led us to articulate the following emerging principles:

  1. Mapping what people have, not what they need: even though approaches like jugaad and positive deviance have been around for a long time, unfortunately the default starting point for many development projects is still mapping needs, not assets. Inverting this paradigm allows for potentially disruptive project design and partnerships to emerge. (Signs of the future: Patient Innovation, Edgeryders, Community Mirror, Premise)

  2. Getting ready for multiple futures: When distributed across an organisation and not limited to a centralised function, the discipline of scanning the horizon for emergent solutions that contradict the dominant paradigm can help move beyond the denial phase and develop new interfaces to collaborate with the mutants. Here the link between analysis (to understand not only what is probable, but also what is possible) and action is critical – otherwise this remains purely an academic exercise. (Signs of the future: OpenCare, Improstuctures, Seeds of Good Anthropocene, Museum of the Future)

  3. Running multiple parallel experiments: According to Dave Snowden, in order to intervene in a complex system “you need multiple parallel experiments and they should be based on different and competing theories/hypotheses”. Unfortunately, many development projects are still based on linear narratives and assumptions such as “if only we run an awareness raising campaign citizens will change their behaviour”. Turning linear narratives into hypotheses to be tested (without becoming religious on a specific approach) opens up the possibility to explore the solution landscape and collaborate with non-obvious partners that bring new approaches to the table. (Signs of the future: Chukua Hakua, GiveDirectly, Finnish PM’s Office of Experiments, Ideas42, Cognitive Edge)

  4. Embracing obliquity: A deep, granular understanding of local assets and dynamics along with system mapping (see point 5 below) and pairing behavioural experts with development practitioners can help identify entry points for exploring new types of intervention based on obliquity principles. Mutants are often faster in adopting this approach and partnering with them is a way to bypass organisational inertia and explore nonlinear interventions. (Signs of the future: Sardex, social prescriptions, forensic architecture)

  5. From projects to systems: development organisations genuinely interested in developing new partnerships need to make the shift from the project logic to system investments. This involves, among other things, shifting the focus from providing solutions to helping every actor in the system to develop a higher level of consciousness about the issues they are facing and to take better decisions over time. It also entails partnering with mutants to explore entirely new financial mechanisms. (Signs of the future: Lankelly Chase, Indonesia waste banks, Dark Matter Labs)

Adopting new interfaces for working with the mutants

Harvard Business School professor Carliss Baldwin argued that most bureaucracies these days have a ‘non-contractible’ problem: they don’t know where smart people are, or how to evaluate how good they are. Most importantly, most smart people don’t want to work for them because they find them either too callous, unrewarding or slow (or a combination of all of these)….(More)”

Selected Readings on Data and Humanitarian Response


By Prianka Srinivasan and Stefaan G. Verhulst *

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 and humanitarian response was originally published in 2016.

Data, when used well in a trusted manner, allows humanitarian organizations to innovate how to respond to emergency events, including better coordination of post-disaster relief efforts, the ability to harness local knowledge to create more targeted relief strategies, and tools to predict and monitor disasters in real time. Consequently, in recent years both multinational groups and community-based advocates have begun to integrate data collection and evaluation strategies into their humanitarian operations, to better and more quickly respond to emergencies. However, this movement poses a number of challenges. Compared to the private sector, humanitarian organizations are often less equipped to successfully analyze and manage big data, which pose a number of risks related to the security of victims’ data. Furthermore, complex power dynamics which exist within humanitarian spaces may be further exacerbated through the introduction of new technologies and big data collection mechanisms. In the below we share:

  • Selected Reading List (summaries and hyperlinks)
  • Annotated Selected Reading List
  • Additional Readings

Selected Reading List  (summaries in alphabetical order)

Data and Humanitarian Response

Risks of Using Big Data in Humanitarian Context

Annotated Selected Reading List (in alphabetical order)

Karlsrud, John. “Peacekeeping 4.0: Harnessing the Potential of Big Data, Social Media, and Cyber Technologies.” Cyberspace and International Relations, 2013. http://bit.ly/235Qb3e

  • This chapter from the book “Cyberspace and International Relations” suggests that advances in big data give humanitarian organizations unprecedented opportunities to prevent and mitigate natural disasters and humanitarian crises. However, the sheer amount of unstructured data necessitates effective “data mining” strategies for multinational organizations to make the most use of this data.
  • By profiling some civil-society organizations who use big data in their peacekeeping efforts, Karlsrud suggests that these community-focused initiatives are leading the movement toward analyzing and using big data in countries vulnerable to crisis.
  • The chapter concludes by offering ten recommendations to UN peacekeeping forces to best realize the potential of big data and new technology in supporting their operations.

Mancini, Fancesco. “New Technology and the prevention of Violence and Conflict.” International Peace Institute, 2013. http://bit.ly/1ltLfNV

  • This report from the International Peace Institute looks at five case studies to assess how information and communications technologies (ICTs) can help prevent humanitarian conflicts and violence. Their findings suggest that context has a significant impact on the ability for these ICTs for conflict prevention, and any strategies must take into account the specific contingencies of the region to be successful.
  • The report suggests seven lessons gleaned from the five case studies:
    • New technologies are just one in a variety of tools to combat violence. Consequently, organizations must investigate a variety of complementary strategies to prevent conflicts, and not simply rely on ICTs.
    • Not every community or social group will have the same relationship to technology, and their ability to adopt new technologies are similarly influenced by their context. Therefore, a detailed needs assessment must take place before any new technologies are implemented.
    • New technologies may be co-opted by violent groups seeking to maintain conflict in the region. Consequently, humanitarian groups must be sensitive to existing political actors and be aware of possible negative consequences these new technologies may spark.
    • Local input is integral to support conflict prevention measures, and there exists need for collaboration and awareness-raising with communities to ensure new technologies are sustainable and effective.
    • Information shared between civil-society has more potential to develop early-warning systems. This horizontal distribution of information can also allow communities to maintain the accountability of local leaders.

Meier, Patrick. “Digital humanitarians: how big data is changing the face of humanitarian response.” Crc Press, 2015. http://amzn.to/1RQ4ozc

  • This book traces the emergence of “Digital Humanitarians”—people who harness new digital tools and technologies to support humanitarian action. Meier suggests that this has created a “nervous system” to connect people from disparate parts of the world, revolutionizing the way we respond to humanitarian crises.
  • Meier argues that such technology is reconfiguring the structure of the humanitarian space, where victims are not simply passive recipients of aid but can contribute with other global citizens. This in turn makes us more humane and engaged people.

Robertson, Andrew and Olson, Steve. “Using Data Sharing to Improve Coordination in Peacebuilding.” United States Institute for Peace, 2012. http://bit.ly/235QuLm

  • This report functions as an overview of a roundtable workshop on Technology, Science and Peace Building held at the United States Institute of Peace. The workshop aimed to investigate how data-sharing techniques can be developed for use in peace building or conflict management.
  • Four main themes emerged from discussions during the workshop:
    • “Data sharing requires working across a technology-culture divide”—Data sharing needs the foundation of a strong relationship, which can depend on sociocultural, rather than technological, factors.
    • “Information sharing requires building and maintaining trust”—These relationships are often built on trust, which can include both technological and social perspectives.
    • “Information sharing requires linking civilian-military policy discussions to technology”—Even when sophisticated data-sharing technologies exist, continuous engagement between different stakeholders is necessary. Therefore, procedures used to maintain civil-military engagement should be broadened to include technology.
    • “Collaboration software needs to be aligned with user needs”—technology providers need to keep in mind the needs of its users, in this case peacebuilders, in order to ensure sustainability.

United Nations Independent Expert Advisory Group on a Data Revolution for Sustainable Development. “A World That Counts, Mobilizing the Data Revolution.” 2014. https://bit.ly/2Cb3lXq

  • This report focuses on the potential benefits and risks data holds for sustainable development. Included in this is a strategic framework for using and managing data for humanitarian purposes. It describes a need for a multinational consensus to be developed to ensure data is shared effectively and efficiently.
  • It suggests that “people who are counted”—i.e., those who are included in data collection processes—have better development outcomes and a better chance for humanitarian response in emergency or conflict situations.

Katie Whipkey and Andrej Verity. “Guidance for Incorporating Big Data into Humanitarian Operations.” Digital Humanitarian Network, 2015. http://bit.ly/1Y2BMkQ

  • This report produced by the Digital Humanitarian Network provides an overview of big data, and how humanitarian organizations can integrate this technology into their humanitarian response. It primarily functions as a guide for organizations, and provides concise, brief outlines of what big data is, and how it can benefit humanitarian groups.
  • The report puts forward four main benefits acquired through the use of big data by humanitarian organizations: 1) the ability to leverage real-time information; 2) the ability to make more informed decisions; 3) the ability to learn new insights; 4) the ability for organizations to be more prepared.
  • It goes on to assess seven challenges big data poses for humanitarian organizations: 1) geography, and the unequal access to technology across regions; 2) the potential for user error when processing data; 3) limited technology; 4) questionable validity of data; 5) underdeveloped policies and ethics relating to data management; 6) limitations relating to staff knowledge.

Risks of Using Big Data in Humanitarian Context
Crawford, Kate, and Megan Finn. “The limits of crisis data: analytical and ethical challenges of using social and mobile data to understand disasters.” GeoJournal 80.4, 2015. http://bit.ly/1X0F7AI

  • Crawford & Finn present a critical analysis of the use of big data in disaster management, taking a more skeptical tone to the data revolution facing humanitarian response.
  • They argue that though social and mobile data analysis can yield important insights and tools in crisis events, it also presents a number of limitations which can lead to oversights being made by researchers or humanitarian response teams.
  • Crawford & Finn explore the ethical concerns the use of big data in disaster events introduces, including issues of power, privacy, and consent.
  • The paper concludes by recommending that critical data studies, such as those presented in the paper, be integrated into crisis event research in order to analyze some of the assumptions which underlie mobile and social data.

Jacobsen, Katja Lindskov (2010) Making design safe for citizens: A hidden history of humanitarian experimentation. Citizenship Studies 14.1: 89-103. http://bit.ly/1YaRTwG

  • This paper explores the phenomenon of “humanitarian experimentation,” where victims of disaster or conflict are the subjects of experiments to test the application of technologies before they are administered in greater civilian populations.
  • By analyzing the particular use of iris recognition technology during the repatriation of Afghan refugees to Pakistan in 2002 to 2007, Jacobsen suggests that this “humanitarian experimentation” compromises the security of already vulnerable refugees in order to better deliver biometric product to the rest of the world.

Responsible Data Forum. “Responsible Data Reflection Stories: An Overview.” http://bit.ly/1Rszrz1

  • This piece from the Responsible Data forum is primarily a compilation of “war stories” which follow some of the challenges in using big data for social good. By drawing on these crowdsourced cases, the Forum also presents an overview which makes key recommendations to overcome some of the challenges associated with big data in humanitarian organizations.
  • It finds that most of these challenges occur when organizations are ill-equipped to manage data and new technologies, or are unaware about how different groups interact in digital spaces in different ways.

Sandvik, Kristin Bergtora. “The humanitarian cyberspace: shrinking space or an expanding frontier?” Third World Quarterly 37:1, 17-32, 2016. http://bit.ly/1PIiACK

  • This paper analyzes the shift toward more technology-driven humanitarian work, where humanitarian work increasingly takes place online in cyberspace, reshaping the definition and application of aid. This has occurred along with what many suggest is a shrinking of the humanitarian space.
  • Sandvik provides three interpretations of this phenomena:
    • First, traditional threats remain in the humanitarian space, which are both modified and reinforced by technology.
    • Second, new threats are introduced by the increasing use of technology in humanitarianism, and consequently the humanitarian space may be broadening, not shrinking.
    • Finally, if the shrinking humanitarian space theory holds, cyberspace offers one example of this, where the increasing use of digital technology to manage disasters leads to a contraction of space through the proliferation of remote services.

Additional Readings on Data and Humanitarian Response

* Thanks to: Kristen B. Sandvik; Zara Rahman; Jennifer Schulte; Sean McDonald; Paul Currion; Dinorah Cantú-Pedraza and the Responsible Data Listserve for valuable input.

Mapping a flood of new data


Rebecca Lipman at Economist Intelligence Unit Perspectives on “One city tweets to stay dry: From drones to old-fashioned phone calls, data come from many unlikely sources. In a disaster, such as a flood or earthquake, responders will take whatever information they can get to visualise the crisis and best direct their resources. Increasingly, cities prone to natural disasters are learning to better aid their citizens by empowering their local agencies and responders with sophisticated tools to cut through the large volume and velocity of disaster-related data and synthesise actionable information.

Consider the plight of the metro area of Jakarta, Indonesia, home to some 28m people, 13 rivers and 1,100 km of canals. With 40% of the city below sea level (and sinking), and regularly subject to extreme weather events including torrential downpours in monsoon season, Jakarta’s residents face far-too-frequent, life-threatening floods. Despite the unpredictability of flooding conditions, citizens have long taken a passive approach that depended on government entities to manage the response. But the information Jakarta’s responders had on the flooding conditions was patchy at best. So in the last few years, the government began to turn to the local population for help. It helped.

Today, Jakarta’s municipal government is relying on the web-based PetaJakarta.org project and a handful of other crowdsourcing mobile apps such as Qlue and CROP to collect data and respond to floods and other disasters. Through these programmes, crowdsourced, time-sensitive data derived from citizens’ social-media inputs have made it possible for city agencies to more precisely map the locations of rising floods and help the residents at risk. In January 2015, for example, the web-based Peta Jakarta received 5,209 reports on floods via tweets with detailed text and photos. Anytime there’s a flood, Peta Jakarta’s data from the tweets are mapped and updated every minute, and often cross-checked by Jakarta Disaster Management Agency (BPBD) officials through calls with community leaders to assess the information and guide responders.

But in any city Twitter is only one piece of a very large puzzle. …

Even with such life-and-death examples, government agencies remain deeply protective of data because of issues of security, data ownership and citizen privacy. They are also concerned about liability issues if incorrect data lead to an activity that has unsuccessful outcomes. These concerns encumber the combination of crowdsourced data with operational systems of record, and impede the fast progress needed in disaster situations….Download the case study .”

Innovation Prizes in Practice and Theory


Paper by Michael J. Burstein and Fiona Murray: “Innovation prizes in reality are significantly different from innovation prizes in theory. The former are familiar from popular accounts of historical prizes like the Longitude Prize: the government offers a set amount for a solution to a known problem, like £20,000 for a method of calculating longitude at sea. The latter are modeled as compensation to inventors in return for donating their inventions to the public domain. Neither the economic literature nor the policy literature that led to the 2010 America COMPETES Reauthorization Act — which made prizes a prominent tool of government innovation policy — provides a satisfying justification for the use of prizes, nor does either literature address their operation. In this article, we address both of these problems. We use a case study of one canonical, high profile innovation prize — the Progressive Insurance Automotive X Prize — to explain how prizes function as institutional means to achieve exogenously defined innovation policy goals in the face of significant uncertainty and information asymmetries. Focusing on the structure and function of actual innovation prizes as an empirical matter enables us to make three theoretical contributions to the current understanding of prizes. First, we offer a stronger normative justification for prizes grounded in their status as a key institutional arrangement for solving a specified innovation problem. Second, we develop a model of innovation prize governance and then situate that model in the administrative state, as a species of “new governance” or “experimental” regulation. Third, we derive from those analyses a novel framework for choosing among prizes, patents, and grants, one in which the ultimate choice depends on a trade off between the efficacy and scalability of the institutional solution….(More)”

Opening Up Government: Citizen Innovation and New Modes of Collaboration


Chapter by Stefan Etzelstorfer, Thomas Gegenhuber and, Dennis Hilgers in Open Tourism: Open Innovation, Crowdsourcing and Co-Creation Challenging the Tourism Industry: “Companies use crowdsourcing to solve problems by using a widely dispersed and large group of individuals. Crowdsourcing and open innovation are not restricted to businesses. Governments also increasingly rely on open innovation principles to harness the expert knowledge of citizens and use citizens’ contributions to the public value creation process. While a large body of literature has examined the open government paradigm at the national level, we still know relatively little about how open government initiatives play out at the local level. Even less is known about whether open government initiatives may create positive spill overs, for example by having a trickle-down effect onto local tourism sectors. In this article, we present the City of Linz’s open government activities. More specifically, we review how the public administration implemented the interactive mapping and reporting application “Schau auf Linz“ (“Look at Linz“). Through our analysis of this case study, we show what role the local context and prior policies play in implementing open government initiatives on a local level. In addition, we discuss how this initiative, like others, leads to positive spill overs for the tourism sector….(More)”

Smarter State Case Studies


“Just as individuals use only part of their brainpower to solve most problems, governing institutions make far too little use of the skills and experience of those inside and outside of government with scientific credentials, practical skills, and ground-level street smarts. New data-rich tools—what The GovLab calls technologies of expertise—are making it possible to match the supply of citizen and civil servant talent to the demand for it in government to solve problems.

The Smarter State Case Studies examine how public institutions are using technologies of expertise, including:

Talent Bank – Professional, social and knowledge networks
Collaboration – Platforms for group work across silos
Project Platforms – Places for inviting new participants to work on projects
Toolkits – Repositories for shared content

Explore the design and key features of these novel platforms; how they are being implemented; the challenges encountered by both creators and users and the anticipated impact of these new ways of working.
The case studies can be found at http://www.thegovlab.org/smarterstate.html
To share a case study, please contact: maria@thegovlab.org

The Smart City and its Citizens


Paper by Carlo Francesco Capra on “Governance and Citizen Participation in Amsterdam Smart City…Smart cities are associated almost exclusively with modern technology and infrastructure. However, smart cities have the possibility to enhance the involvement and contribution of citizens to urban development. This work explores the role of governance as one of the factors influencing the participation of citizens in smart cities projects. Governance characteristics play a major role in explaining different typologies of citizen participation. Through a focus on Amsterdam Smart City program as a specific case study, this research examines the characteristics of governance that are present in the overall program and within a selected sample of projects, and how they relate to different typologies of citizen participation. The analysis and comprehension of governance characteristics plays a crucial role both for a better understanding and management of citizen participation, especially in complex settings where multiple actors are interacting….(More)”