Accelerating Valuable, Accessible, & Responsible Data

Takeaways from Conversations with Representatives from the Public and Private Sectors

Uma Kalkar
Data Stewards Network
13 min readJan 18


Natalia González Alarcón, Uma Kalkar, Stefaan Verhulst

In November 2022, The GovLab and the Innovation for Citizens Services division of the Inter-American Development Bank (IDB) organized an event on innovative models for data access, collaboration, and policymaking as part of the Data+ Initiative to strengthen the regional data ecosystem. Below, we outline proceedings from the event and share seven takeaways decision-makers should consider when creating data sharing and (re)use initiatives.

Data has become a fundamental resource and a strategic asset for society. However, the potential of data depends on tackling emerging asymmetries between data holders and those who would benefit from the data. Thus, there is a need to accelerate methods and practices for valuable, accessible, and responsible data flow management such as data collaboratives, data trusts, and other approaches.

On November 29, the Inter-American Development Bank (IDB) and The Governance Lab (The GovLab) brought together multisectoral experts to discuss policy, innovation, and practices for data access, collaboration, sharing, use, and (re)use.

The conference hosted two panels, one from a public sector perspective, and one with a private sector outlook. These panels explored existing data operational models to provide practical examples of how the public sector can leverage private sector experiences to learn from data and improve decision-making processes, as well as potential regulatory frameworks that may foster the emergence of such instruments.

Following the panels, IDB department teams from the Infrastructure, Innovation, and Health divisions took part in a hands-on studio session to rapidly develop ways to introduce and improve data collaboration models for better use of data. These sessions were facilitated by some of the panelists to help the departments delve into their data collaboration and innovation aspirations and discuss recommendations for better execution of data strategies.

Panel 1: Emerging Data Policy Models


  • Dominique Duval-Diop, Deputy Chief Data Scientist for the U.S. Office of Science and Technology Policy
  • Peter Fatelnig, Minister Counsellor for Digital Economy Policy for the Delegation of the EU to the U.S.
  • Hyejeong Lim, Senior Manager at the National Information Society Agency of Korea
  • Luisa Medina, Deputy Director for Digital Public Capacities at the Ministry of ICT of Colombia

Panel 2: Innovative Operational Models from a Sectoral Approach


In both panels, we explored numerous types of data flows between supra-national, national, and sub-national government bodies (G); private firms (B); and citizens, academic institutions, and community groups ©. These include G2G (government-to-government), G2B (government-to-business), G2C (government-to-citizen/society), B2G (business-to-government), B2B (business-to-business), B2C (business-to-citizen/society), C2G (citizen/society-to–government), C2B (citizen/society-to-business), and C2C (citizen/society-to-citizen/society) strategies to use and experiment with data to drive innovative and progressive public policies. Further, we looked at how data collaboratives and data strategies facilitate these data flows to unlock the full potential of data’s benefits.

Below, we present a summary of the main conversation points highlighted by the panelists. Panel recordings are available in English (Panel 1 & Panel 2), Spanish (Panel 1 y Panel 2), and Portuguese (Painel 1 e Painel 2). A full playlist is available here.

From the day’s discussions and interactions, we have drawn seven main takeaways:

Takeaway 1: Government data flows allow for a spectrum of data access and (re)use for internal and external partners.

Many governments around the world have turned their attention toward policy and other instruments seeking to make more of their data resources. However, their priorities vary within a wide range of approaches. In the case of the U.S., first with the Evidence Act in 2018 and then with the Federal Data Strategy in 2019, the focus has been to put data at the center of the decision-making and program design across government agencies, facilitating stronger G2G data flows. This might reveal a more internal approach, as confirmed by Dominique Duval-Diop, Deputy Chief Data Scientist for the U.S. Office of Science and Technology Policy. She noted how the U.S. federal data strategy aims at changing the data culture within agencies and establishing a government-wide vision of how agencies should consistently manage and use their data. Here, it is clear that building internal data literacy and capacity, as well as increasing their use of equitable data, are of particular importance for data (re)use initiatives to enable rigorous assessment of government programs and yield consistently informed, fair, and just treatment of data.

In contrast, the European Commission representative, Peter Fatelnig, Minister Counsellor for Digital Economy Policy for the Delegation of the European Union, highlighted how the European Union (E.U.) is pushing to unlock privately-held data and increase G2B and B2G data sharing for emergency and crisis situations and beyond. He explained that a main avenue for this effort is the E.U. Data Act proposal, with which the EU seeks to create a minimum legal framework for accessing and using business data fairly among all actors across member countries, evidencing a more external approach.

In the case of South Korea and Colombia, the approach seems more balanced. For example, in Colombia, the National Data Infrastructure Plan was created to facilitate access and exchange of data more effectively, not only within the public sector but also between the different stakeholders of the data ecosystem. They specifically focus on building G2C and G2B data flows by opening up their strategies. In South Korea, the Open Data Service Council’s master plan requires all national ministries, local governments, and public bodies to submit an open data implementation plan to the Ministry of Interior and Safety every three years to demonstrate sector-specific actions and aspirations.

Takeaway 2: Regulatory frameworks may act as an important enabler to use and innovate with data.

There is a well-known and longstanding debate between innovation and regulation. Despite their tensions, they work best together to reduce uncertainty about government oversight and delineate avenues for entrepreneurial endeavors. Indeed, Peter Fatelnig noted that although Europe has spent a lot of time working on regulatory mechanisms, the E.U. realized an innovation approach always needs to go hand in hand with these policies to foster better G2B interactions. He stressed the need for a minimum legal framework for data sharing to encourage different actors to use shared data and expand projects beyond their company walls. For instance, he mentioned how the E.U. created a regulatory approach on top of the GDPR to harness European health data during the COVID-19 pandemic to incentivize companies to develop tools and means for using this data to address the health crisis.

Colombia has a similar approach to the E.U. According to Luisa Medina, Deputy Director for Digital Public Capacities at the Ministry of ICT, the country’s transparency laws, including the National Data Exploitation Policy (CONPES 3920) and economic reactivation policy (CONPES 4023), have been instrumental for new data exchange models, including a data commons project for the agricultural sector.

Ultimately, governments need to consider how to walk the line between fostering innovation and protecting data rights. Informed and balanced regulation helps signal government priorities and foster innovative investments in data-driven work while also demonstrating oversight commitment to codifying and upholding data protection and privacy tenets.

Takeaway 3: Leadership and access to expertise are key to ensuring the sustainability of data strategies.

Leadership has a critical role in unlocking boundaries among stakeholders and triggering innovation. Attracting data talent and promoting data stewardship is key for national data strategies to ensure that both the systems and skillsets needed for data interoperability can withstand turnover in leadership or political administrations and foster a culture of data collaboration within governments.

Dominique gave the example of how the US federal government has taken the lead to place data at the center of policymaking and program decision-making, specifically by finding the right talent to shepherd these goals. One key way that the U.S. government is responding to the suite of new norms and tools that are popular within the private and civil society sectors is by creating data science and stewardship positions in government to support cutting-edge data-driven work. For instance, in addition to the Office of Science Technology Policy and the Chief Data Scientist position, the 2018 Evidence Act established the Federal Chief Data Officer Council to support systemic collaboration among the Chief Data Officers present across government agencies. Further, she mentioned the U.S. Digital Corps Program and the Presidential Innovation Fellows Program as ways by which the federal government attracts new ‘GovTech’ and ‘CivicTech’ talent and strengthens G2C relationships.

Luisa added how Colombia has similarly acquired data talent. Under the leadership of the Ministry of ICT (MinTIC), Colombia created a National Data Coordinator position and has plans to establish a National Data Agency. In parallel, the MinTIC has established Chief Information Officers in each national agency who implement open data initiatives at the regional level. In this way, MinTIC proactively builds capacities across national and sub-national entities for G2G flows, guaranteeing sustainability.

As well, Hyejeong Lim, Senior Manager at the National Information Society Agency of Korea, brought up how, in Korea, it is mandatory for all ministries and local government bodies to have at least two Open Data Officers in charge of operations and to provide a variety of training courses for civil servants to promote G2G data interactions and understanding.

These examples demonstrate the importance of human capital for long-lasting and informed data actions and agendas.

Takeaway 4: Institutionalizing processes enable the translation of principles into practice in an inclusive manner.

Principles of transparency, accountability, and equity can only go as far as the mechanisms and processes by which they are operationalized. National data strategies need to design processes for data governance and oversight.

Dominique discussed the Biden-Harris administration’s commitment to advancing racial equity and how federal agencies have worked to implement the recommendations of the Equitable Data Working Group. Federal agencies have worked to reshape how, where, and what data is collected and (re)used to assess agency practices and programs. Additionally, they have identified opportunities at an institutional and structural level to build out disaggregated data on protected characteristics, geographic locations, and government service access, thus contributing to a data landscape that embodies equitable values.

Moreover, Hyejeong gave the example of how Korea’s Open Data Strategy Council, which was created under the Korean Prime Minister’s office and co-chaired by a private sector representative, helped craft the country’s Open Data Law and advise open data actions. This council deliberates, coordinates, and monitors all major open government data policies and plans to make sure these governance tactics build trust and value among the public and institutions to build G2G, G2B, and G2C relationships. The council works with municipal leadership to implement strategies, coordinating with private and sub-national stakeholders.

From these examples, panelists revealed how assessments of current practices via ethical frameworks and participatory working groups can introduce values and rights across the data lifecycle.

Takeaway 5: Data collaboration is a process, not just a technological solution.

Data collaboration should be methodologically driven around the people and processes they seek to serve in order to manage the gap between an idea’s potential impact and the availability of data actually to answer the question at hand. In other words, data demand needs to drive data supply for useful and purposeful data stocks and flows. Matt Gee, President of BrightHive, and Alex Hutchison, Director of the Data for Children Collaborative, both pointed out the importance of defining the purpose of an initiative and concentrating on the sustainability and replicability of the methods applied to achieve it.

Alex pointed out that once the purpose of a project is defined, it needs to be kept top of mind as suitable approaches and methodological frameworks to facilitate it are established. She described how the Data for Children Collaborative conducts a ‘True North check’ at the start of a data collaboration to make sure that the project has the right people, data, and demand for action. Moreover, she affirmed that the methodology created to work with diverse partners, where the focus rests primarily on skills rather than on solutions, represents innovation and good practice. She shared how the Data for Children Collaborative focuses on specifically understanding each contributor’s motivation prior to having them join the collaborative.

Echoing Takeaway 3, Matt pointed out that “there needs to be a willingness to learn from pre-existing initiatives, adapt current practices to the problem at hand, and avoid the urge to ‘reinvent the wheel.’” Crafting robust, evidence-based processes can help with that. Since there are many pieces that need to be put together and to get right for a data collaborative to work, “from legal contracts to data governance procedures, privacy and usage policies, complex organizational and business dynamics,” he suggested an 80/20 rule — with 80% of a model’s focus relating to processes and 20% focusing on the technology solution — as a viable way to build data collaboration.

This focus on purposeful and impactful data collaboration aligns with the rise of the Third Wave of Open Data, a movement that looks to open data in a purposeful and impactful way by considering the demand-side (i.e. user need) as well as the supply-side (i.e. available data), demonstrating the need to anchor the narrative of a data collaboration project’s impact to the data sharing and (re)use initiative.

Takeaway 6: There is a lot to learn from experimentation with new operational and governance models, such as data commons and data trusts.

Under the lead of private initiatives, there have been multiple experiments related to innovative data collaboration models. Many of these have opened opportunities for the public sector to adopt lessons from corporate practices. Matt emphasized that building on prior practices and experiences accelerates the design and implementation of new data collaboration models since they provide some evidence that it has been successfully done before. For example, the Data Trust for the State of Virginia has served as a model to show that when different local agencies share their data under a controlled instance, they can better identify their targeted population and improve overall service allocation.

In the case of PLACE, a data collaboration model that seeks to solve inefficiencies of high-resolution mapping, Peter Rabley, Managing Partner of PLACE, explained how his organization created a trusted intermediary (a data trust) between the public and private providers to facilitate G2B and B2G data flows. The data trust structure established a membership model that created a ‘club good,’ or what economists define as something that is excludable but non-rivalrous. He stated that this model can be replicated in other sectors if the technical and legal conditions are in place because the technical aspect of the trust is always based on the legal jurisdiction in which the model will be deployed.

Similarly, Colombia is implementing a data commons pilot project in the agricultural sector. Luisa mentioned how this forthcoming data commons project establishes a governance model for data exchange between stakeholders with specific roles and responsibilities. It responds to the need of the different actors (public and private) to exchange data related to market, financial, and climatic risk indicators to deepen the integral management of agricultural risks. This project has demonstrated that sharing data and reusing information in a G2B and B2G manner generates public value by addressing a common problem, and that this methodology will serve as a benchmark for other Colombian sectoral initiatives.

Takeaway 7: New methods are needed to establish a social license and govern data in the public interest.

Most of today’s data discussions focus on protecting individuals’ privacy and how the state needs to protect citizens’ data from (mostly private sector) exploitation. Yet these debates fail to consider the agency of data subjects. Mara Balestrini, Data Lead Specialist at the IDB Lab, stated that nowadays, “people are more educated and more connected” around data and data issues, presenting “ample opportunities for mass collaboration and the creation of enormous impact based on purpose and altruism.” As The GovLab previously explored in its Data Assembly, wherein we held mini-publics to understand public opinion over C2G data (re)use by the government to address COVID-19, people are willing to contribute their data for the common good.

However, Mara noted that an individual’s bargaining power is nonexistent when it comes to sharing data with private companies in a C2G or C2B manner. To this end, Peter added that individual and government data is not valuable without a platform that can wrangle and aggregate that information to generate value. Further, Mara suggested some type of shared governance scheme to increase collective bargaining power and build a consumer-to-corporation data sharing pipeline.

Thus, not only is public awareness about data innovation important, but their active engagement in creating and monitoring these processes can improve acceptance of data sharing and (re)use.

Drawing from these takeaways, The GovLab and the IDB will be releasing a report on harnessing data flows for innovation and collaboration between the public and private sectors. Follow @TheGovLab on Twitter and subscribe to the Data Stewards Newsletter and Medium to be the first to learn more.


About The Governance Lab

The Governance Lab’s mission is to improve people’s lives by changing the way we govern. Our goal at The GovLab is to strengthen the ability of institutions — including but not limited to governments — and people to work more openly, collaboratively, effectively, and legitimately to make better decisions and solve public problems. We believe that increased availability and use of data, new ways to leverage the capacity, intelligence, and expertise of people in the problem-solving process, combined with new advances in technology and science, can transform governance. We approach each challenge and opportunity in an interdisciplinary, collaborative way, irrespective of the problem, sector, geography, and level of government. For more information, visit

About the Inter-American Development Bank

The IDB is the main source of multilateral financing and expertise for sustainable economic, social, and institutional development in Latin America and the Caribbean. The IDB Group is the leading source of development finance for Latin America and the Caribbean. The group comprises the IDB, which has worked with governments for 60 years; IDB Invest, which serves the private sector; and IDB Lab, which tests innovative ways to enable more inclusive growth.