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:
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.
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.
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.
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.
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 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:
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.
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.
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.