Disrupt & Innovate
Data Collaboratives can transform the way civil society organisations find solutions – Part II
This is the second of two blogs on Data Collaboratives by Stefaan G. Verhulst of The Governance Lab (GovLab) at New York University. Stefaan explains the 5 specific value propositions of Data Collaboratives identified by the Gov Lab. In addition, he tackles the issue of data security. Specifically, how organisations need to professionalise the responsible use of data. To do this, organisations need to embrace the creation of Data Stewardship job roles. (Read Part II here)
At a broad level, data collaboratives offer the possibility of unlocking insights and solutions from vast, untapped stores of private-sector data. But what does this mean in practice? GovLab’s research indicates five specific public value propositions arising from cross-sector data-collaboration. These include:
- Situational Awareness and Response: Private data can help NGOs, humanitarian organisations and others better understand demographic trends, public sentiment, and the geographic distribution of various phenomena:
- One notable instance of this value proposition has been Facebook’s Disaster Maps initiative. Following natural disasters, Facebook shares aggregated location, movement, and self-reported safety data collected through its platform with responding humanitarian organisations, including the International Federation of Red Cross and Red Crescent Societies (IFRC), United Nations International Children’s Emergency Fund (UNICEF), and the World Food Programme (WFP).
Disaster Maps provide another tool in the humanitarian response toolkit to fill any gaps in traditional data sources and to inform more targeted relief efforts from responders on the ground.
- Knowledge Creation and Transfer: Data collaboratives can join widely dispersed datasets, in the process creating a better understanding of possible correlations and causalities as well as what variables make a difference for what type of problem:
- For example: researchers at Data2X, a collaborative platform dedicated to improving “the quality, availability, and use of gender data” has sought to leverage the insights generated by analysing geospatial data, credit card, mobile phone data, and social media posts to pinpoint problems that women and girls in developing countries are facing, such as malnutrition, education, healthcare access, and mental health issues.
- Service Design and Delivery: By definition, data collaboratives increase access to previously inaccessible (i.e. privately held) datasets. These datasets often contain a wealth of information that can enable more accurate modelling of ICSO service delivery:
- For example, the use of Geographic Information Systems (GIS) enabled WorldPop and UNFPAto map human populations that were traditionally unreachable through conventional approaches toward the goal of improving service delivery, resource allocation, urban planning and disaster management by development organisations.
- Prediction and Forecasting: Richer, more complete information from a data collaborative enables new predictive capabilities for ICSOs and others. Thus, allowing them to be more proactive and put in place mechanisms that prevent or at least mitigate crises before they occur:
- For example, the Malaria Elimination Initiative developed DISARM (Disease Surveillance and Risk Monitoring), a platform that uses satellite data and Google Earth data to predict Malaria outbreaks. Mobile phone data has also been used in predicting population displacement during the 2015 earthquake in Nepal, which helped international and domestic humanitarian organisations deliver aid more effectively.
- Impact Assessment and Evaluation: Finally, data collaboratives can aid CSOs in one of the most important (yet often neglected) steps of their value chains: monitoring, evaluation, and improvement. By leveraging data, CSOs can rapidly assess the results of their actions, as to iterate on products and programs when necessary:
- This is what Sport England did, for instance, when it used Twitter data to understand women’s views on exercise to inform its successful #ThisGirlCan campaign aimed at improving women and girl’s health and physical activity.
Professionalising the Responsible Use of Data
These value propositions offer a compelling case for greater use of private data through data collaboratives to solve complex public problems. However, a variety of concerns still exist. Some of these concerns (e.g. fears over privacy) involve public fears, while others (e.g. worries over a potential erosion of competitive advantage) are more internal oriented. Nonetheless, all of these concerns need to be addressed in order to foster greater trust and appreciation of the potential of data collaborative.
That is why there is a need to develop a framework that would guide the responsible use of data. GovLab has looked at these issues in a recent report, The Potential of Social Media Intelligence to Improve People’s Lives: Social Media Data for Good. Responsible data use has many aspects, and there are various degrees of responsibility. At the very least, it means having core (written) principles, and well-defined policies and practices for how data is collected, stored, analysed, shared and used (across the data lifecycle).
In addition, it is essential to conduct regular risk assessments that consider the balance between the potential value and dangers inherent at every stage of the data lifecycle. Such risk assessments can help data stakeholders decide when data sharing can be truly beneficial (or what the opportunity cost may be of not sharing the data). Several ICSOs have already started developing such responsible data frameworks such as Oxfam (Responsible Data Policy) and World Vision (Data Protection, Privacy & Security (DPP&S) framework). Increased awareness, further coordination (toward perhaps an ICSO Responsible Data Framework) and translation of these policies into decision trees may be required.
Data Stewardship roles
Yet not only do ICSOs and other private actors lack the frameworks to determine how to responsibly share and use data for the public good, they often lack a well-defined, professionalised concept of “Data Stewardship.” Today, each attempt to establish a cross-sector partnership built on the analysis of data requires significant and time-consuming efforts. ICSOs rarely have personnel tasked with undertaking such efforts and making such decisions.
The process of establishing “Data Collaboratives” and leveraging privately-held data for evidence-based policy making is onerous. Also, it is generally a one-off process and not informed by best practices or any shared knowledge base. Thus it is prone to dissolution when the champions involved move on to other functions.
By establishing “Data Stewardship” as a job function in organisations alongside methods and tools for responsible data-sharing, we can free data sharing for development from its stuck dynamic, and turn it into a regularised, predictable, and de-risked activity. Only then can ICSOs use and share their own data and that of others – including private companies – through data collaboratives to help transform how they achieve their missions while improving people’s lives.
Stefaan Verhulst
Co-Founder & Chief of Research and Development at the GovLab
Stefaan G. Verhulst is Co-Founder and Chief Research and Development Officer of the Governance Laboratory @NYU (GovLab) where he is responsible for building a research foundation on how to transform governance using advances in science and technology. Verhulst’s latest scholarship centers on how technology can improve people’s lives and the creation of more effective and collaborative forms of governance. Specifically, he is interested in the perils and promise of collaborative technologies and how to harness the unprecedented volume of information to advance the public good. Before joining NYU full time, Verhulst spent more than a decade as Chief of Research for the Markle Foundation, where he continues to serve as Senior Advisor. At Markle, an operational foundation based in New York, he was responsible for overseeing strategic research on all the priority areas of the Foundation including, for instance: transforming health care using information and technology, re-engineering government to respond to new national security threats, improving people’s lives in developing countries by connecting them to information networks, developing multi-stakeholder networks to tackle global governance challenges, changing education through information technology et al. Many of Markle’s reports have been translated into legislation and executive orders, and have informed the creation of new organizations and businesses. He is also an Adjunct Professor in the Department of Culture and Communications at New York University, Senior Research Fellow for the Center for Media and Communications Studies at Central European University in Budapest; and an Affiliated Senior Research Fellow at the Center for Global Communications Studies at the University of Pennsylvania’s Annenberg School for Communications. Previously at Oxford University he co-founded and was the Head of the Programme in Comparative Media Law and Policy at the Centre for Socio Legal Studies, and also served as Senior Research Fellow of Wolfson College. He is still an emeritus fellow at Oxford. He also taught several years at the London School of Economics. Verhulst was the UNESCO Chairholder in Communications Law and Policy for the UK, a former lecturer on Communications Law and Policy issues in Belgium, and Founder and Co-Director of the International Media and Info-Comms Policy and Law Studies at the University of Glasgow School of Law. He has served as a consultant to numerous international and national organizations, including the Council of Europe, the European Commission, UNESCO, World Bank, UNDP, USAID, the UK Department for International Development among others. He has been a grant recipient of the Bertelsmann Foundation, the Ford Foundation, the Rockefeller Foundation, and the Markle Foundation. Verhulst has authored and co-authored several books, including: In Search of the Self: Conceptual Approaches to Internet Self Regulation (Routledge, 2001); Convergence in European Communications Regulation (Blackstone, 1999); EC Media Law and Policy (AWL, 1998); Legal Responses to the Changing Media (OUP, 1998); and Broadcasting Reform in India (OUP, 1998) and The Routledge Handbook of Media Law (2013). Latest reports and papers include, for instance, Innovations in Global Governance: Toward a Distributed Internet Governance Ecosystem (2014) and The Open Data Era in Health and Social Care (2014). Verhulst blogs also regularly on a variety of topics. For instance: Data Collaboratives: Exchanging Data to Improve People’s Lives (2015), and Reimagining Cities (2014). Verhulst is also founder and editor of numerous journals including the International Journal of Communications Law and Policy, and the Communications Law in Transition Newsletter. Currently, he is the Curator and Editor of the Govlab Weekly Digest.
Data Collaboratives can transform the way civil society organisations find solutions – Part I
This is the first of two blogs on Data Collaboratives by Stefaan G. Verhulst of The Governance Lab. Data Collaboratives are an emerging public-private partnership model, in which participants from different sectors come together to exchange data and pool analytical expertise. Their potential is great, offering new solutions to old problems and making International Civil Society Organisations more effective. (Read Part II here)
The need for innovation is clear: The twenty-first century is shaping up to be one of the most challenging in recent history. From climate change to income inequality to geopolitical upheaval and terrorism: the difficulties confronting international civil society organisations (ICSOs) are unprecedented not only in their variety but also in their complexity. At the same time, today’s practices and tools used by ICSOs seem stale and outdated. Increasingly, it is clear, we need not only new solutions but new methods for arriving at solutions.
Data will likely become more central to meeting these challenges. We live in a quantified era. It is estimated that 90% of the world’s data was generated in just the last two years. We know that this data can help us understand the world in new ways and help us meet the challenges mentioned above. However, we need new data collaboration methods to help us extract the insights from that data.
UNTAPPED DATA POTENTIAL
For all of data’s potential to address public challenges, the truth remains that most data generated today is in fact collected by the private sector – including ICSOs who are often collecting a vast amount of data – such as, for instance, the International Committee of the Red Cross, which generates various (often sensitive) data related to humanitarian activities. This data, typically ensconced in tightly held databases toward maintaining competitive advantage or protecting from harmful intrusion, contains tremendous possible insights and avenues for innovation in how we solve public problems. But because of access restrictions and often limited data science capacity, its vast potential often goes untapped.
DATA COLLABORATIVES AS A SOLUTION
Data Collaboratives offer a way around this limitation. They represent an emerging public-private partnership model, in which participants from different areas — including the private sector, government, and civil society — come together to exchange data and pool analytical expertise.
While still an emerging practice, examples of such partnerships now exist around the world, across sectors and public policy domains. Importantly several ICSOs have started to collaborate with others around their own data and that of the private and public sector. For example:
- Several civil society organisations, academics, and donor agencies are partnering in the Health Data Collaborative to improve the global data infrastructure necessary to make smarter global and local health decisions and to track progress against the Sustainable Development Goals (SDGs).
- Additionally, the UN Office for the Coordination of Humanitarian Affairs (UNOCHA) built Humanitarian Data Exchange (HDX), a platform for sharing humanitarian from and for ICSOs – including Caritas, InterAction and others – donor agencies, national and international bodies, and other humanitarian organisations.
These are a few examples of Data Collaboratives that ICSOs are participating in. Yet, the potential for collaboration goes beyond these examples. Likewise, so do the concerns regarding data protection and privacy.
At The Governance Lab (GovLab) at New York University, we have researched in depth the potential of Data Collaboratives, and have identified five specific public value propositions. We are also clear in the need for organisations in Data Collaboratives to embrace establishing “Data Stewardship” roles to ensure responsible data management.
In the next blog, I will go into greater detail about GovLab’s work and explain how ICSOs could use Data Collaboratives to their benefit more, and how they can manage data responsibly.
Stefaan Verhulst
Co-Founder & Chief of Research and Development at the GovLab
Stefaan G. Verhulst is Co-Founder and Chief Research and Development Officer of the Governance Laboratory @NYU (GovLab) where he is responsible for building a research foundation on how to transform governance using advances in science and technology. Verhulst’s latest scholarship centers on how technology can improve people’s lives and the creation of more effective and collaborative forms of governance. Specifically, he is interested in the perils and promise of collaborative technologies and how to harness the unprecedented volume of information to advance the public good. Before joining NYU full time, Verhulst spent more than a decade as Chief of Research for the Markle Foundation, where he continues to serve as Senior Advisor. At Markle, an operational foundation based in New York, he was responsible for overseeing strategic research on all the priority areas of the Foundation including, for instance: transforming health care using information and technology, re-engineering government to respond to new national security threats, improving people’s lives in developing countries by connecting them to information networks, developing multi-stakeholder networks to tackle global governance challenges, changing education through information technology et al. Many of Markle’s reports have been translated into legislation and executive orders, and have informed the creation of new organizations and businesses. He is also an Adjunct Professor in the Department of Culture and Communications at New York University, Senior Research Fellow for the Center for Media and Communications Studies at Central European University in Budapest; and an Affiliated Senior Research Fellow at the Center for Global Communications Studies at the University of Pennsylvania’s Annenberg School for Communications. Previously at Oxford University he co-founded and was the Head of the Programme in Comparative Media Law and Policy at the Centre for Socio Legal Studies, and also served as Senior Research Fellow of Wolfson College. He is still an emeritus fellow at Oxford. He also taught several years at the London School of Economics. Verhulst was the UNESCO Chairholder in Communications Law and Policy for the UK, a former lecturer on Communications Law and Policy issues in Belgium, and Founder and Co-Director of the International Media and Info-Comms Policy and Law Studies at the University of Glasgow School of Law. He has served as a consultant to numerous international and national organizations, including the Council of Europe, the European Commission, UNESCO, World Bank, UNDP, USAID, the UK Department for International Development among others. He has been a grant recipient of the Bertelsmann Foundation, the Ford Foundation, the Rockefeller Foundation, and the Markle Foundation. Verhulst has authored and co-authored several books, including: In Search of the Self: Conceptual Approaches to Internet Self Regulation (Routledge, 2001); Convergence in European Communications Regulation (Blackstone, 1999); EC Media Law and Policy (AWL, 1998); Legal Responses to the Changing Media (OUP, 1998); and Broadcasting Reform in India (OUP, 1998) and The Routledge Handbook of Media Law (2013). Latest reports and papers include, for instance, Innovations in Global Governance: Toward a Distributed Internet Governance Ecosystem (2014) and The Open Data Era in Health and Social Care (2014). Verhulst blogs also regularly on a variety of topics. For instance: Data Collaboratives: Exchanging Data to Improve People’s Lives (2015), and Reimagining Cities (2014). Verhulst is also founder and editor of numerous journals including the International Journal of Communications Law and Policy, and the Communications Law in Transition Newsletter. Currently, he is the Curator and Editor of the Govlab Weekly Digest.
Disrupt and Innovate in a Data-Driven World
If you do an internet search for ‘data-driven disruption’ you can find articles about almost every industry being disrupted by digitalisation and new applications of data. Banking, transportation, healthcare, retail, and real estate, all have seen the emergence of new business models fundamentally changing how customers use their services. While there are instances of data-driven efforts in the nonprofit sector, they are not as widespread as they can be. Bridgespan Group estimated in 2015 that only 6% of nonprofits use data to drive improvements in their work.
At the same time, the Sustainable Development Goals (SDGs) have set a very ambitious global change agenda and we won’t be able to meet their targets by doing business as usual. To achieve the SDGs requires new ideas across the board: new solutions, new sources of funding, new ways of delivering services and new approaches to collaborating within and across social, public and private sectors.
The private sector already very successfully uses data analytics and machine learning not only to realise efficiency gains but also – even more importantly – to create completely new services and business models. For example, applying machine learning to wind forecasting is expected to reduce uncertainty in wind energy production by more than 45% and will allow utilities to integrate wind more easily with traditional forms of power supply. And entirely new utility start-ups such as Drift use machine learning technologies to provide customers with cheaper wholesale energy prices by more accurately predicting consumption.
In the nonprofit sector, early applications of data analytics and machine learning have mostly focused on improving fundraising and marketing. In a next step, the broader adoption of data analysis techniques and tools has the potential to help nonprofits increase their programmatic impact as well as identify completely new ways of achieving their mission.
- Gain improved intelligence on operating context and needs through expanded use of descriptive analytics techniques. On the program side, teams largely tend to use descriptive analytics – statistical techniques that provide insight into the past and answer: “What has happened?” – on survey data, sometimes complemented by samples from larger raw datasets, e.g. Facebook posts or tweets. In many settings this is the best information available. However, it presents obvious drawbacks: given the expense and time required to conduct surveys we frequently operate based on information that is years old. Also, surveys are often run to confirm or refute certain hypotheses making it challenging to utilise existing survey data to answer new sets of questions. The more we can directly analyse raw data, such as today’s internet searches, the more we will be able to obtain a close to real-time picture of the situation on the ground. Applying data analytics and machine learning to large raw datasets will likely also yield us new and unexpected insights as these techniques and tools allow us to unearth patterns and seek potential explanations for those in contrast to responding to a predefined set of questions.
- Identify those most at risk or most affected by a problem more accurately by using predictive analytics. For example, a County Department of Human Services in Pennsylvania recently implemented a predictive risk model designed to improve screening decision-making in the county’s child welfare system. The model integrates and analyses hundreds of data elements. The resulting score predicts the long-term likelihood of home removal and provides a recommendation on whether a follow-up investigation is warranted. The model has been shown to be effective in preventing the screening-out of at-risk children. It has also lowered the number of investigations with potential disruptive effects on low-risk families. One could imagine similar models being applied to screening cases of domestic violence or abuse of domestic migrant workers.
- Achieve best possible outcomes for individuals through the application of prescriptive analytics. In healthcare, some hospitals are now generating predictions of a patient’s readmission risk at the time of diagnosis. Patients with a higher likelihood of returning to the hospital within a month receive additional care and supports such as home visits. This has reduced the readmission rates and freed up resources that can be used to treat additional patients. There are many possible use cases for prescriptive analytics in the development sector, particularly in health where we have much existing data on what works in light of specific risk factors. Tools that incorporate these models could assist community health workers in triaging cases and prioritising their workload. They could also be applied to people suffering from addictions or people with learning challenges to prescribe individualised treatment and support plans.
As these approaches become more mature and wide-spread in their application their impact will go much beyond making workflows more efficient. They have the potential to fundamentally disrupt how we work and what we define as our core competencies. Today, it may seem challenging to move towards a future where recommending who to support and how could be largely automated. I also don’t want to minimise the challenges in this scenario: the availability of required data and the privacy issues involved.
However, I want to encourage us to actively embrace and shape this future as its potential for positive impact is immense. We need to work together to ensure that the automation involved in these techniques and tools will provide valuable insights that support humans in making thoughtful and effective decisions, free up our valuable and constrained resources and focus them on those parts of our work that truly make a difference in people’s lives.
Claudia Juech
Executive Director
Claudia Juech is the founding Executive Director of the Cloudera Foundation, which will use Cloudera’s expertise in data analytics and machine learning to change people’s lives for the better. Previously, Claudia was an Associate Vice President at the Rockefeller Foundation, leading the organisation’s Strategic Insights division. Working with grantees and partners around the globe, she and her team used data and information to identify large-scale opportunities to address economic inequality and critical challenges in the areas of health, the environment, and in cities. Prior to joining the Rockefeller Foundation in 2007, Claudia was a Vice President at DB Research, Deutsche Bank’s think tank for trends in business, society and the financial markets. She has a degree in Information Science from Cologne University of Applied Sciences and an International MBA from the University of Cologne.
