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)”

Open access: All human knowledge is there—so why can’t everybody access it?

Curated on Posted on by Stefaan Verhulst

 at ArsTechnica: “In 1836, Anthony Panizzi, who later became principal librarian of the British Museum, gave evidence before a parliamentary select committee. At that time, he was only first assistant librarian, but even then he had an ambitious vision for what would one day became the British Library. He told the committee:

I want a poor student to have the same means of indulging his learned curiosity, of following his rational pursuits, of consulting the same authorities, of fathoming the most intricate inquiry as the richest man in the kingdom, as far as books go, and I contend that the government is bound to give him the most liberal and unlimited assistance in this respect.

He went some way to achieving that goal of providing general access to human knowledge. In 1856, after 20 years of labour as Keeper of Printed Books, he had helped boost the British Museum’s collection to over half a million books, making it the largest library in the world at the time. But there was a serious problem: to enjoy the benefits of those volumes, visitors needed to go to the British Museum in London.

Imagine, for a moment, if it were possible to provide access not just to those books, but to all knowledge for everyone, everywhere—the ultimate realisation of Panizzi’s dream. In fact, we don’t have to imagine: it is possible today, thanks to the combined technologies of digital texts and the Internet. The former means that we can make as many copies of a work as we want, for vanishingly small cost; the latter provides a way to provide those copies to anyone with an Internet connection. The global rise of low-cost smartphones means that group will soon include even the poorest members of society in every country.

That is to say, we have the technical means to share all knowledge, and yet we are nowhere near providing everyone with the ability to indulge their learned curiosity as Panizzi wanted it.

What’s stopping us? That’s the central question that the “open access” movement has been asking, and trying to answer, for the last two decades. Although tremendous progress has been made, with more knowledge freely available now than ever before, there are signs that open access is at a critical point in its development, which could determine whether it will ever succeed in realising Panizzi’s plan.

Table of Contents

Code and the City

Curated on Posted on by Stefaan Verhulst

Book edited by Rob Kitchin, Sung-Yueh Perng: “Software has become essential to the functioning of cities. It is deeply embedded into the systems and infrastructure of the built environment and is entrenched in the management and governance of urban societies. Software-enabled technologies and services enhance the ways in which we understand and plan cities. It even has an effect on how we manage urban services and utilities.

Code and the City explores the extent and depth of the ways in which software mediates how people work, consume, communication, travel and play. The reach of these systems is set to become even more pervasive through efforts to create smart cities: cities that employ ICTs to underpin and drive their economy and governance. Yet, despite the roll-out of software-enabled systems across all aspects of city life, the relationship between code and the city has barely been explored from a critical social science perspective. This collection of essays seeks to fill that gap, and offers an interdisciplinary examination of the relationship between software and contemporary urbanism.

This book will be of interest to those researching or studying smart cities and urban infrastructure….(More)”.

Digital Government: overcoming the systemic failure of transformation

Curated on Posted on by Stefaan Verhulst

Paul Waller and Vishanth Weerakkody: “This Working Paper contains propositions regarding the use of digital technology to “transform” government that significantly conflict with received wisdom in academia and governments across the world. It counters assertions made in countless political, official and commercial statements and reports produced over past decades….

The “transformation of government” has often been proposed as an objective of e-government; frequently presented as a phase in stage models following the provision online of information and transactions. Yet in literature or official documents there is no established definition of transformation as applied to government. Implicitly or explicitly, it mostly refers to a change in organisational form, signalled by the terms “joining-up” or “integration”, of government. In some work,

In some work, transformation is limited to changing processes or “services”— though “services” is a term unhelpfully applied to a multitude of entities. There is in academic or other literature little evidence of any type of “transformation” achieved beyond a change in an administrative process, nor a robust framework of benefits one might deliver. This begs the questions of what it actually means in reality and why it might be a desired goal.

In essence, what we aim to do in this paper is to develop a structured frame of reference for making sense of how information and communications technologies (ICT), in all their forms, really fit within the world of government and public administration — exactly the challenge set by Professor Christopher Hood in his 2007 paper:

“But we need to have a way of assessing current developments in administrative technologies with those of other eras, such as development of telephones, cars, radios, and fingerprinting in police work in the early part of the twentieth century, or of exact methods of measurement on excise tax collection in the eighteenth century. And if the analysis of the changes such developments bring is to amount to anything more than a breathless tour d’horizon of the latest technological gizmos in public policy (much though governments themselves have a liking for that sort of approach), it needs to be related to some foundational analysis that is, in some way, technology-free and rooted in the nature of government as a social and legal phenomenon.”

After a brief historical review, the paper starts by considering what governments and public administrations actually do: specifically, policy design and implementation through policy instruments. It redefines transformation in terms of changing the policy instrument set chosen to implement policy and sets out broad rationales for how and why ICT can enable this. It proposes a frame of reference of terminology, concepts and objects that enable the examination of not only such transformation, but e-government in general as it has developed over two decades. …(More)”

Connect the corporate dots to see true transparency

Curated on Posted on by Stefaan Verhulst

Gillian Tett at the Financial Times: “…In all this, a crucial point is often forgotten: simply amassing data will not solve the problem of transparency. What is also needed is a way for analysts to track the connections that exist between companies scattered across different national jurisdictions.

There are more than 45,000 companies listed on global stock exchanges and, according to Chris Taggart of OpenCorporates, an independent data company, there are between 250m and 400m unlisted groups. Many of these are listed on national registries but, since registries are extremely fragmented, it is very difficult for shareholders or regulators to form a complete picture of company activity.

It also creates financial stability risks. One reason why it is currently hard to track the scale of Chinese corporate debt, say, is that it is being issued by an opaque web of legal entities. Similarly, regulators struggled to cope with the fallout from the Lehman Brothers collapse in 2008 because the bank was operating almost 3,000 different legal entities around the world.

Is there a solution to this? A good place to start would be for governments to put their corporate registries online. Another crucial step would be for governments and companies to agree on a common standard for labelling legal entities, so that these can be tracked across borders.

Happily, work on that has begun: in 2014, the Global Legal Entity Identifier Foundation was created. It supports the implementation and use of “legal entity identifiers”, a data standard that identifies participants in financial transactions. Groups such as the Data Coalition in Washington DC are lobbying for laws that would force companies to use LEIs….However, this inter-governmental project is moving so slowly that the private sector may be a better bet. In recent years, companies such as Dun & Bradstreet have begun to amass proprietary information about complex corporate webs, and computer nerds are also starting to use the power of big data to join up the corporate dots in a public format.

OpenCorporates is a good example. Over the past five years, a dozen staff there have been painstakingly scraping national corporate registries to create a database designed to show how companies are connected around the world. This is far from complete but data from 100m entities have already been logged. And in the wake of the Panama Papers, more governments are coming on board — data from the Cayman Islands are currently being added and France is likely to collaborate soon.

Sadly, these moves will not deliver real transparency straight away. If you type “MIO” into the search box on the OpenCorporates website, you will not see a map of all of McKinsey’s activities — at least not yet.

The good news, however, is that with every data scrape, or use of an LEI, the picture of global corporate activity is becoming slightly less opaque thanks to the work of a hidden army of geeks. They deserve acclaim and support — even (or especially) from management consultants….(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.

Three Things Great Data Storytellers Do Differently

Curated on Posted on by Stefaan Verhulst

Jake Porway at Stanford Social Innovation Review: “…At DataKind, we use data science and algorithms in the service of humanity, and we believe that communicating about our work using data for social impact is just as important as the work itself. There’s nothing worse than findings gathering dust in an unread report.

We also believe our projects should always start with a question. It’s clear from the questions above and others that the art of data storytelling needs some demystifying. But rather than answering each question individually, I’d like to pose a broader question that can help us get at some of the essentials: What do great data storytellers do differently and what can we learn from them?

1. They answer the most important question: So what?

Knowing how to compel your audience with data is more of an art than a science. Most people still have negative associations with numbers and statistics—unpleasant memories of boring math classes, intimidating technical concepts, or dry accounting. That’s a shame, because the message behind the numbers can be so enriching and enlightening.

The solution? Help your audience understand the “so what,” not the numbers. Ask: Why should someone care about your findings? How does this information impact them? My strong opinion is that most people actually don’t want to look at data. They need to trust that your methods are sound and that you’re reasoning from data, but ultimately they just want to know what it all means for them and what they should do next.

A great example of going straight to the “so what” is this beautiful, interactive visualization by Periscopic about gun deaths. It uses data sparingly but still evokes a very clear anti-gun message….

2. They inspire us to ask more questions.

The best data visualization helps people investigate a topic further, instead of drawing a conclusion for them or persuading them to believe something new.

For example, the nonprofit DC Action for Children was interested in leveraging publicly available data from government agencies and the US Census, as well as DC Action for Children’s own databases, to help policymakers, parents, and community members understand the conditions influencing child well-being in Washington, DC. We helped create a tool that could bring together data in a multitude of forms, and present it in a way that allowed people to delve into the topic themselves and uncover surprising truths, such as the fact that one out of every three kids in DC lives in a neighborhood without a grocery store….

3. They use rigorous analysis instead of just putting numbers on a page.

Data visualization isn’t an end goal; it’s a process. It’s often the final step in a long manufacturing chain, along which we poke, prod, and mold data to create that pretty graph.

Years ago, the New York City Department of Parks & Recreation (NYC Parks) approached us—armed with data about every single tree in the city, including when it was planted and how it was pruned—and wanted to know: Does pruning trees in one year reduce the number of hazardous tree conditions in the following year? This is one of the first things our volunteer data scientists came up with:

Visualization of NYC Parks’ Department data showing tree density in New York City.

This is a visualization of tree density New York—and it was met with oohs and aahs. It was interactive! You could see where different types of trees lived! It was engaging! But another finding that came out of this work arguably had a greater impact. Brian D’Alessandro, one of our volunteer data scientists, used statistical modeling to help NYC Parks calculate a number: 22 percent. It turns out that if you prune trees in New York, there are 22 percent fewer emergencies on those blocks than on the blocks where you didn’t prune. This number is helping the city become more effective by understanding how to best allocate its resources, and now other urban forestry programs are asking New York how they can do the same thing. There was no sexy visualization, no interactivity—just a rigorous statistical model of the world that’s shaping how cities protect their citizens….(More)”

Your City Needs a Local Data Intermediary Now

Curated on Posted on by Stefaan Verhulst

Matt Lawyue and Kathryn Pettit at Next City: “Imagine if every community nationwide had access to their own data — data on which children are missing too many days of school, which neighborhoods are becoming unaffordable, or where more mothers are getting better access to prenatal care.

This is a reality in some areas, where neighborhood data is analyzed to evaluate community health and to promote development. Cleveland is studying cases of lead poisoning and the impact on school readiness and educational outcomes for children. Detroit is tracking the extent of property blight and abandonment.

But good data doesn’t just happen.

These activities are possible because of local intermediaries, groups that bridge the gap between data and local stakeholders: nonprofits, government agencies, foundations and residents. These groups access data that are often confidential and indecipherable to the public and make them accessible and useful. And with the support of the National Neighborhood Indicators Partnership (NNIP), groups around the country are championing community development at the local level.

Without a local data intermediary in Baltimore, we might know less about what happened there last year and why.

Freddie Gray’s death prompted intense discussion about police brutality and discrimination against African-Americans. But the Baltimore Neighborhood Indicators Alliance (BNIA) helped root this incident and others like it within a particular place, highlighting what can happen when disadvantage is allowed to accumulate over decades.

BNIA, an NNIP member, was formed in 2000 to help community organizations use data shared by government agencies. By the time of Gray’s death, BNIA had 15 years of data across more than 150 indicators that demonstrated clear socioeconomic disadvantages for residents of Gray’s neighborhood, Sandtown-Winchester. The neighborhood had a 34 percent housing vacancy rate and 23 percent unemployment. The neighborhood lacks highway access and is poorly served by public transit, leaving residents cut off from jobs and services.

With BNIA’s help, national and local media outlets, including the New York Times,MSNBC and the Baltimore Sun portrayed a community beset by concentrated poverty, while other Baltimore neighborhoods benefited from economic investment and rising incomes. BNIA data, which is updated yearly, has also been used to develop policy ideas to revitalize the neighborhood, from increasing the use of housing choice vouchers to tackling unemployment.

Local data intermediaries like BNIA harness neighborhood data to make underserved people and unresolved issues visible. They work with government agencies to access raw data (e.g., crime reports, property records, and vital statistics) and facilitate their use to improve quality of life for residents.

But it’s not easy. Uncovering useful, actionable information requires trust, technical expertise, knowledge of the local context and coordination among multiple stakeholders.

This is why the NNIP is vital. NNIP is a peer network of more than two dozen local data intermediaries and the Urban Institute, working to democratize data by building local capacity and planning joint activities. Before NNIP’s founding partners, there were no advanced information systems documenting and tracking neighborhood indicators. Since 1996, NNIP has been a platform for sharing best practices, providing technical assistance, managing cross-site projects and analysis, and expanding the outreach of local data intermediaries to national networks and federal agencies. The partnership continues to grow. In order to foster this capacity in more places, NNIP has just released a guide for local communities to start a data intermediary….(More)”