What Can Civic Tech Learn From Social Movements?

Curated on Posted on by Stefaan Verhulst

Stacy Donohue at Omidyar Network: “…In order to spur creative thinking about how the civic tech sector could be accelerated and expanded, we looked to Purpose, a public benefit corporation that works with NGOs, philanthropies, and brands on movement building strategies. We wanted to explore what we might learn from taking the work that Purpose has done mapping the progress of of 21st century social movements and applying its methodology to civic tech.

So why consider viewing civic tech using the lens of 21st century movements? Movements are engines of change in society that enable citizens to create new and better paths to engage with government and to seek recourse on issues that matter to millions of people.  At first glance, civic tech doesn’t appear to be a movement in the purest sense of the term, but on closer inspection, it does share some fundamental characteristics. Like a movement, civic tech is mission driven, is focused on making change that benefits the public, and in most cases enables better public input into decision making.

We believe that better understanding the essential components of movements, and observing the ways in which civic tech does or does not behave like one, can yield insights on how we as a civic tech community can collectively drive the sector forward….

report Engines of Change: What Civic Tech Can Learn From Social Movements….provides a lot of rich insight and detail which we invite everyone to explore.  Meanwhile, we have summarized five key findings:

  1. Grassroots activity is expanding across the US – Activity is no longer centralized around San Francisco and New York; it’s rapidly growing and spreading across the US – in fact, there was an 81% increase in the number of cities hosting civic tech MeetUps from 2013 to 2015, and 45 of 50 states had at least one MeetUp on civic tech in 2015.
  2. Talk is turning to action – We are walking the talk. One way we can see this is that growth in civic tech Twitter discussion is highly correlated with the growth in GitHub contributions to civic tech projects and related Meetup events. Between 2013-2015, over 8,500 people contributed code to GitHub civic tech projects and there were over 76,000 MeetUps for civic tech events. 
  3. There is an engaged core, but it is very small in number – As with most social movements, civic tech has a definite core of highly engaged evangelists, advocates and entrepreneurs that are driving conversations, activity, and events and this is growing. The number of Meetup groups holding multiple events a quarter grew by 136% between 2013 to 2015. And likewise there was a 60% growth in Engaged Tweeters in during this time period.  However, this level of activity is dwarfed by other movements such as climate action.
  4. Civic tech is growing but still lacking scale – There are many positive indications of growth in civic tech; for example, the combination of nonprofit and for-profit funding to the sector increased by almost 120% over the period.  But while growth compares favorably to other movements, again the scale just isn’t there.
  5. Common themes, but no shared vision or identity – Purpose examined the extent to which civic tech exhibits and articulates a shared vision or identity around which members of a movement can rally. What they found is that many fewer people are discussing the same shared set of themes. Two themes – Open Data and Government Transparency – are resonating and gaining traction across the sector and could therefore form the basis of common identity for civic tech.

While each of these insights is important in its own right and requires action to move the sector forward, the main thing that strikes us is the need for a coherent and clearly articulated vision and sense of shared identity for civic tech…

Read the full report: Engines of Change: What Civic Tech Can Learn From Social Movements

Explore the data tool here….(More)”

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)”

Are we too obsessed with data?

Curated on Posted on by Stefaan Verhulst

Lauren Woodman of Nethope:” Data: Everyone’s talking about it, everyone wants more of it….

Still, I’d posit that we’re too obsessed with data. Not just us in the humanitarian space, of course, but everyone. How many likes did that Facebook post get? How many airline miles did I fly last year? How many hours of sleep did I get last week?…

The problem is that data by itself isn’t that helpful: information is.

We need to develop a new obsession, around making sure that data is actionable, that it is relevant in the context in which we work, and on making sure that we’re using the data as effectively as we are collecting it.

In my talk at ICT4D, I referenced the example of 7-Eleven in Japan. In the 1970s, 7-Eleven in Japan became independent from its parent, Southland Corporation. The CEO had to build a viable business in a tough economy. Every month, each store manager would receive reams of data, but it wasn’t effective until the CEO stripped out the noise and provided just four critical data points that had the greatest relevance to drive the local purchasing that each store was empowered to do on their own.

Those points – what sold the day before, what sold the same day a year ago, what sold the last time the weather was the same, and what other stores sold the day before – were transformative. Within a year, 7-Eleven had turned a corner, and for 30 years, remained the most profitable retailer in Japan. It wasn’t about the Big Data; it was figuring out what data was relevant, actionable and empowered local managers to make nimble decisions.

For our sector to get there, we need to do the front-end work that transforms our data into information that we can use. That, after all, is where the magic happens.

A few examples provide more clarity as to why this is so critical.

We know that adaptive decision-making requires access to real-time data. By knowing what is happening in real-time, or near-real-time, we can adjust our approaches and interventions to be most impactful. But to do so, our data has to be accessible to those that are empowered to make decisions. To achieve that, we have to make investments in training, infrastructure, and capacity-building at the organizational level.  But in the nonprofit sector, such investments are rarely supported by donors and beyond the limited unrestricted funding available to most most organizations. As a result, the sector has, so far, been able to take only limited steps towards effective data usage, hampering our ability to transform the massive amounts of data we have into useful information.

Another big question about data, and particularly in the humanitarian space, is whether it should be open, closed or somewhere in between. Privacy is certainly paramount, and for types of data, the need for close protection is very clear. For many other data, however, the rules are far less clear. Every country has its own rules about how data can and cannot be used or shared, and more work is needed to provide clarity and predictability so that appropriate data-sharing can evolve.

And perhaps more importantly, we need to think about not just the data, but the use cases.  Most of us would agree, for example, that sharing information during a crisis situation can be hugely beneficial to the people and the communities we serve – but in a world where rules are unclear, that ambiguity limits what we can do with the data we have. Here again, the context in which data will be used is critically important.

Finally, all of in the sector have to realize that the journey to transforming data into information is one we’re on together. We have to be willing to give and take. Having data is great; sharing information is better. Sometimes, we have to co-create that basis to ensure we all benefit….(More)”

Using Innovation and Technology to Improve City Services

Curated on Posted on by Stefaan Verhulst

IBM Center for the Business of Government: “In this report, Professor Greenberg examines a dozen cities across the United States that have award-winning reputations for using innovation and technology to improve the services they provide to their residents. She explores a variety of success factors associated with effective service delivery at the local level, including:

  • The policies, platforms, and applications that cities use for different purposes, such as public engagement, streamlining the issuance of permits, and emergency response
  • How cities can successfully partner with third parties, such as nonprofits, foundations, universities, and private businesses to improve service delivery using technology
  • The types of business cases that can be presented to mayors and city councils to support various changes proposed by innovators in city government

Professor Greenberg identifies a series of trends that drive cities to undertake innovations, such as the increased use of mobile devices by residents. Based on cities’ responses to these trends, she offers a set of findings and specific actions that city officials can act upon to create innovation agendas for their communities. Her report also presents case studies for each of the dozen cities in her review. These cases provide a real-world context, which will allow interested leaders in other cities to see how their own communities might approach similar innovation initiatives.

This report builds on two other IBM Center reports: A Guide for Making Innovation Offices Work, by Rachel Burstein and Alissa Black, and The Persistence of Innovation in Government: A Guide for Public Servants, by Sandford Borins, which examines the use of awards to stimulate innovation in government.

We hope that government leaders who are interested in innovations using technology to improve services will benefit from the governance models and tools described in this report, as they consider how best to leverage innovation and technology initiatives to serve residents more effectively and efficiently….(More)”

Estonia Is Demonstrating How Government Should Work in a Digital World

Curated on Posted on by Stefaan Verhulst

Motherboard: “In May, Manu Sporny became the 10,000th “e-Resident” of Estonia. Sporny, the founder and CEO of a digital payments and identity company located in the United States, has never set foot in Estonia. However, he heard about the country’s e-Residency program and decided it would be an obvious choice for his company’s European headquarters.

People like Sporny are why Estonia launched a digital residency program in December 2014. The program allows anyone in the world to apply for a digital identity, which will let them: establish and run a location independent business online, get easier access to EU markets, open a bank account and conduct e-banking, use international payment service providers, declare taxes, and sign all relevant documents and contracts remotely…..

One of the most essential components of a functioning digital society is a secure digital identity. The state and the private sector need to know who is accessing these online services. Likewise, users need to feel secure that their identity is protected.

Estonia found the solution to this problem. In 2002, we started issuing residents a mandatory ID-card with a chip that empowers them to categorically identify themselves and verify legal transactions and documents through a digital signature. A digital signature has been legally equivalent to a handwritten one throughout the European Union—not just in Estonia—since 1999.

With this new digital identity system, the state could serve not only areas with a low population, but also the entire Estonian diaspora. Estonians anywhere in the world could maintain a connection to their homeland via e-services, contribute to the legislative process, and even participate in elections. Once the government realized that it could scale this service worldwide, it seemed logical to offer its e-services to those without physical residency in Estonia. This meant the Estonian country suddenly had value as a service in addition to a place to live.

What does “Country as a Service” mean?

With the rise of a global internet, we’ve seen more skilled workers and businesspeople offering their services across nations, regardless of their physical location. A survey by Intuit estimates that this number will reach 40 percent in the US alone by 2020.

These entrepreneurs and skilled artisans are ultimately looking for the simplest way to create and maintain a legal, global identity as an outlet for their global offerings.

They look to other countries, not because they are looking for a tax haven, but because they have been prevented from incorporating and maintaining a business, due to barriers from their own government.

The most important thing for these entrepreneurs is that the creation and upkeep of the company is easy and hassle-free. It is also important that, despite being incorporated in a different nation, they remain honest taxpayers within their country of physical residence.

This is exactly what Estonia offers—a location-independent, hassle-free and fully-digital economic and financial environment where entrepreneurs can run their own company globally….

When an e-Resident establishes a company, it means that the company will likely start using the services offered by other Estonian companies (like creating a bank account, partnering with a payment service provider, seeking assistance from accountants, auditors and lawyers). As more clients are created for Estonian companies, their growth potential increases, along with the growth potential of the Estonian economy.

Eventually, there will be more residents outside borders than inside them

If states fail to redesign and simplify the machinery of bureaucracy and make it location-independent, there will be an opportunity for countries that can offer such services across borders.

Estonia has learned that it’s incredibly important in a small state to serve primarily small and micro businesses. In order to sustain a nation on this, we must automate and digitize processes to scale. Estonia’s model, for instance, is location-independent, making it simple to scale successfully. We hope to acquire at least 10 million digital residents (e-Residents) in a way that is mutually beneficial by the nation-states where these people are tax residents….(More)”

Democracy in Decline: Rebuilding its Future

Curated on Posted on by Stefaan Verhulst

Book by Philip Kotler: “An examination by the ‘father of modern marketing’ into how well  a long cherished product (democracy) is satisfying the needs of its consumers (citizens), bringing conversation and solutions on how we can all do our bit to bring about positive change.

At a time where voting systems are flawed, fewer vote, major corporations fund campaigns and political parties battle it out, democracies are being seriously challenged and with that the prospects of a better world for all.

Philip Kotler identifies 14 shortcomings of today’s democracy and proposes potential remedies whilst encouraging readers to join the conversation, exercise their free speech and get on top of the issues that affect their lives regardless of nationality or political persuasion.

An accompanying website (www.democracyindecline.com) invites those interested to help find and publish thoughtful articles that aid our understanding of what is happening and what can be done to improve democracies around the world….(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.

Refugees and the Technology of Exile

Curated on Posted on by Stefaan Verhulst

David Lepeska in Wilson Quaterly: “While working for a Turkish tech firm, Akil learned how to program for mobile phones, and decided to make a smartphone app to help Syrians get all the information they need to build new lives in Turkey. In early 2014, he and a friend launched Gherbtna, named for an Arabic word referring to the loneliness of foreign exile….

About one-tenth of the 2.7 million Syrians in Turkey live in refugee camps. The rest fend for themselves, mostly in big cities. Now that they look set to stay in Turkey for some time, their need to settle and build stable, secure lives is much more acute. This may explain why downloads of Gherbtna more than doubled in the past six months. “We started this project to help people, and when we have reached all Syrian refugees, to help them find jobs, housing, whatever they need to build a new life in Turkey, then we have achieved our goal,” said Akil. “Our ultimate dream for Gherbtna is to reach all refugees around the world, and help them.”

Humanity is currently facing its greatest refugee crisis since World War II, with more than 60 million people forced from their homes. Much has been written about their use of technology — how Google Maps, WhatsApp, Facebook, and other tools have proven invaluable to the displaced and desperate. But helping refugees find their way, connect with family, or read the latest updates about route closings is one thing. Enabling them to grasp minute legal details, find worthwhile jobs and housing, enroll their children in school, and register for visas and benefits when they don’t understand the local tongue is another.

Due to its interpretation of the 1951 Geneva Convention on refugees, Ankara does not categorize Syrians in Turkey as refugees, nor does it accord them the pursuant rights and advantages. Instead, it has given them the unusual legal status of temporary guests, which means that they cannot apply for asylum and that Turkey can send them back to their countries of origin whenever it likes. What’s more, the laws and processes that apply to Syrians have been less than transparent and have changed several times. Despite all this — or perhaps because of it — government outreach has been minimal. Turkey has spent some $10 billion on refugees, and it distributes Arabic-language brochures at refugee camps and in areas with many Syrian residents. Yet it has created no Arabic-language website, app, or other online tool to communicate the relevant laws, permits, and legal changes to Syrians and other refugees.

Independent apps targeting these hurdles have begun to proliferate. Gherbtna’s main competitor in Turkey is the recently launched Alfanus (“Lantern” in Arabic), which its Syrian creators call an “Arab’s Guide to Turkey.” Last year, Souktel, a Palestinian mobile solutions firm, partnered with the international arm of the American Bar Association to launch a text-message service that provides legal information to Arabic speakers in Turkey. Norway is running a competition to develop a game-based learning app to educate Syrian refugee children. German programmers created Germany Says Welcome and the similar Welcome App Dresden. And Akil’s tech firm, Namaa Solutions, recently launched Tarjemly Live, a live translation app for English, Arabic, and Turkish.

But the extent to which these technologies have succeeded — have actually helped Syrians adjust and build new lives in Turkey, in particular — is in doubt. Take Gherbtna. The app has nine tools, including Video, Laws, Alerts, Find a Job, and “Ask me.” It offers restaurant and job listings; advice on getting a residence permit, opening a bank account, or launching a business; and much more. Like Souktel, Gherbtna has partnered with the American Bar Association to provide translations of Turkish laws. The app has been downloaded about 50,000 times, or by about 5 percent of Syrians in Turkey. (It is safe to assume, however, that a sizable percentage of refugees do not have smartphones.) Yet among two dozen Gherbtna users recently interviewed in Gaziantep and Istanbul — two Turkish cities with the most dense concentration of Syrians — most found it lacking. Many appreciate Gherbtna’s one-stop-shop appeal, but find little cause to keep using it. ”…(More)”

Searching for Someone: From the “Small World Experiment” to the “Red Balloon Challenge,” and beyond

Curated on Posted on by Stefaan Verhulst

Essay by Manuel Cebrian, Iyad Rahwan, Victoriano Izquierdo, Alex Rutherford, Esteban Moro and Alex (Sandy) Pentland: “Our ability to search social networks for people and information is fundamental to our success. We use our personal connections to look for new job opportunities, to seek advice about what products to buy, to match with romantic partners, to find a good physician, to identify business partners, and so on.

Despite living in a world populated by seven billion people, we are able to navigate our contacts efficiently, only needing a handful of personal introductions before finding the answer to our question, or the person we are seeking. How does this come to be? In folk culture, the answer to this question is that we live in a “small world.” The catch-phrase was coined in 1929 by the visionary author Frigyes Karinthy in his Chain-Links essay, where these ideas are put forward for the first time.

Let me put it this way: Planet Earth has never been as tiny as it is now. It shrunk — relatively speaking of course — due to the quickening pulse of both physical and verbal communication. We never talked about the fact that anyone on Earth, at my or anyone’s will, can now learn in just a few minutes what I think or do, and what I want or what I would like to do. Now we live in fairyland. The only slightly disappointing thing about this land is that it is smaller than the real world has ever been. — Frigyes Karinthy, Chain-Links, 1929

Then, it was just a dystopian idea reflecting the anxiety of living in an increasingly more connected world. But there was no empirical evidence that this was actually the case, and it took almost 30 years to find any.

Six Degrees of Separation

In 1967, legendary psychologist Stanley Milgram conducted a ground-breaking experiment to test this “small world” hypothesis. He started with random individuals in the U.S. midwest, and asked them to send packages to people in Boston, Massachusetts, whose address was not given. They must contribute to this “search” only by sending the package to individuals known on a first-name basis. Milgram expected that successful searches (if any!) would require hundreds of individuals along the chain from the initial sender to the final recipient.

Surprisingly, however, Milgram found that the average path length was somewhere between five point five and six individuals, which made social search look astonishingly efficient. Although the experiment raised some methodological criticisms, its findings were profound. However, what it did not answer is why social networks have such short paths in the first place. The answer was not obvious. In fact, there were reasons to suspect that short paths were just a myth: social networks are very cliquish. Your friends’ friends are likely to also be your friends, and thus most social paths are short and circular. This “cliquishness” suggests that our search through the social network can easily get “trapped” within our close social community, making social search highly inefficient.

Architectures for Social Search

Again, it took a long time — more than 40 years — before this riddle was solved. In a 1998 seminal paper in Nature, Duncan Watts & Steven Strogatzcame up with an elegant mathematical model to explain the existence of these short paths. They started from a social network that is very cliquish, i.e., most of your friends are also friends of one another. In this model, the world is “large” since the social distance among individuals is very long. However, if we take only a tiny fraction of these connections (say one out of every hundred links), and rewire them to random individuals in the network, that same world suddenly becomes “small.” These random connections allow individuals to jump to faraway communities very quickly — using them as social network highways — thus reducing average path length in a dramatic fashion.

While this theoretical insight suggests that social networks are searchable due to the existence of short paths, it does not yet say much about the “procedure” that people use to find these paths. There is no reason, a priori, that we should know how to find these short chains, especially since there are many chains, and no individuals have knowledge of the network structure beyond their immediate communities. People do not know how the friends of their friends are connected among themselves, and therefore it is not obvious that they would have a good way of navigating their social network while searching.

Soon after Watts and Strogatz came up with this model at Cornell University, a computer scientist across campus, Jon Kleinberg, set out to investigate whether such “small world” networks are searchable. In a landmark Nature article, “Navigation in a Small World,” published in 200o, he showed that social search is easy without global knowledge of the network, but only for a very specific value of the probability of long-range connectivity (i.e., the probability that we know somebody far removed from us, socially, in the social network). With the advent of a publicly available social media dataset such as LiveJournal, David Liben-Nowell and colleagues showed that real-world social networks do indeed have these particular long-range ties. It appears the social architecture of the world we inhabit is remarkably fine-tuned for searchability….

The Tragedy of the Crowdsourcers

Some recent efforts have been made to try and disincentivize sabotage. If verification is also rewarded along the recruitment tree, then the individuals who recruited the saboteurs would have a clear incentive to verify, halt, and punish the saboteurs. This theoretical solution is yet to be tested in practice, and it is conjectured that a coalition of saboteurs, where saboteurs recruit other saboteurs pretending to “vet” them, would make recursive verification futile.

If we are to believe in theory, theory does not shed a promising light on reducing sabotage in social search. We recently proposed the “Crowdsourcing Dilemma.” In it, we perform a game-theoretic analysis of the fundamental tradeoff between the potential for increased productivity of social search and the possibility of being set back by malicious behavior, including misinformation. Our results show that, in competitive scenarios, such as those with multiple social searches competing for the same information, malicious behavior is the norm, not an anomaly — a result contrary to conventional wisdom. Even worse: counterintuitively, making sabotage more costly does not deter saboteurs, but leads all the competing teams to a less desirable outcome, with more aggression, and less efficient collective search for talent.

These empirical and theoretical findings have cautionary implications for the future of social search, and crowdsourcing in general. Social search is surprisingly efficient, cheap, easy to implement, and functional across multiple applications. But there are also surprises in the amount of evildoing that the social searchers will stumble upon while recruiting. As we get deeper and deeper into the recruitment tree, we stumble upon that evil force lurking in the dark side of the network.

Evil mutates and regenerates in the crowd in new forms impossible to anticipate by the designers or participants themselves. Crowdsourcing and its enemies will always be engaged in an co-evolutionary arms race.

Talent is there to be searched and recruited. But so are evil and malice. Ultimately, crowdsourcing experts need to figure out how to recruit more of the former, while deterring more of the later. We might be living on a small world, but the cost and fragility of navigating it could harm any potential strategy to leverage the power of social networks….

Being searchable is a way of being closely connected to everyone else, which is conducive to contagion, group-think, and, most crucially, makes it hard for individuals to differentiate from each other. Evolutionarily, for better or worse, our brain makes us mimic others, and whether this copying of others ends up being part of the Wisdom of the Crowds, or the “stupidity of many,” it is highly sensitive to the scenario at hand.

Katabasis, or the myth of the hero that descends to the underworld and comes back stronger, is as old as time and pervasive across ancient cultures. Creative people seem to need to “get lost.” Grigori Perelman, Shinichi Mochizuki, and Bob Dylan all disappeared for a few years to reemerge later as more creative versions of themselves. Others like J. D. Salinger and Bobby Fisher also vanished, and never came back to the public sphere. If others cannot search and find us, we gain some slack, some room to escape from what we are known for by others. Searching for our true creative selves may rest on the difficulty of others finding us….(More)”

Fan Favorites

Curated on Posted on by Stefaan Verhulst

Erin Reilly at Strategy + Business: “…In theory, new technological advances such as big data and machine learning, combined with more direct access to audience sentiment, behaviors, and preferences via social media and over-the-top delivery channels, give the entertainment and media industry unprecedented insight into what the audience actually wants. But as a professional in the television industry put it, “We’re drowning in data and starving for insights.” Just as my data trail didn’t trace an accurate picture of my true interest in soccer, no data set can quantify all that consumers are as humans. At USC’s Annenberg Innovation Lab, our research has led us to an approach that blends data collection with a deep understanding of the social and cultural context in which the data is created. This can be a powerful practice for helping researchers understand the behavior of fans — fans of sports, brands, celebrities, and shows.

A Model for Understanding Fans

Marketers and creatives often see audiences and customers as passive assemblies of listeners or spectators. But we believe it’s more useful to view them as active participants. The best analogy may be fans. Broadly characterized, fans have a continued connection with the property they are passionate about. Some are willing to declare their affinity through engagement, some have an eagerness to learn more about their passion, and some want to connect with others who share their interests. Fans are emotionally linked to the object of their passion, and experience their passion through their own subjective lenses. We all start out as audience members. But sometimes, when the combination of factors aligns in just the right way, we become engaged as fans.

For businesses, the key to building this engagement and solidifying the relationship is understanding the different types of fan motivations in different contexts, and learning how to turn the data gathered about them into actionable insights. Even if Jane Smith and her best friend are fans of the same show, the same team, or the same brand, they’re likely passionate for different reasons. For example, some viewers may watch the ABC melodrama Scandal because they’re fashionistas and can’t wait to see the newest wardrobe of star Kerry Washington; others may do so because they’re obsessed with politics and want to see how the newly introduced Donald Trump–like character will behave. And those differences mean fans will respond in varied ways to different situations and content.
Though traditional demographics may give us basic information about who fans are and where they’re located, current methods of understanding and measuring engagement are missing the answers to two essential questions: (1) Why is a fan motivated? and (2) What triggers the fan’s behavior? Our Innovation Lab research group is developing a new model called Leveraging Engagement, which can be used as a framework when designing media strategy….(More)”