International Consortium for Data Collaboratives
Public-Private Data Partnerships for International Development
The International Monetary Fund, the Inter-American Development Bank, and the World Bank are forming an International Consortium for Data Collaboratives. The Consortium fosters efficient and responsible data partnerships for public good.
Consortium members are working together to build a platform that improves the security, efficiency, and effectiveness of data partnerships for public good, by focusing on the following features:
Template data sharing framework agreements and joint agreements between multiple organizations save time and resources.
Transparent web-based system for soliciting project ideas from staff improves efficiency and efficacy of data partnerships.
Working groups comprised of data engineers, data scientists, sector domain experts, legal counsel, communications specialists, procurement specialists, and others across the member organizations make a complete platform possible.
Secure IT architecture and processes for ingesting, storing, and accessing data as well as for coding collaboration, create economies of scale amongst Consortium members and facilitate responsible data use.
A formal data partnership management system ensures the value proposition for data partners is met.
Managed, accessible repositories for derived data products and algorithms broaden the impact of Data Collaboratives.
How to engage
The Consortium is open to donors and entities engaged in international development work. Members have access to the Data Collaboratives data sharing platform and IT infrastructure and are invited to participate in workshops, exchanges, and training activities.
The Consortium helps partners unlock public good opportunities from their data in a secure, responsible manner. Data partnerships can be leveraged to open markets in emerging economies, receive new data methods and algorithms, and increase staff skills through collaboration and secondment opportunities.
Provision of public infrastructure and services is heavily dependent on data – higher quality, timely data translates into more effective project design, implementation, and evaluation. Traditional methods for public sector data collection can be inefficient – for example, in the transport sector, travel times are often estimated using stopwatches, and common origin-destination pairs are derived through decennial household surveys.
Increasingly, the private sector is generating data that could be re-purposed to complement traditional public-sector data collection methods – for example, using fleet GPS data to estimate speeds or mobile operator data to determine travel patterns.
Rather than expend public resources collecting data that is already collected, the public sector should seek the comparative advantage of the private sector. In so doing, entirely new public good use cases could be discovered and implemented.
There is a persistent market failure in the international development community between the supply of private sector data and the demand for its use.
On the supply side, companies engaged in a range of businesses – e.g., ridesharing, social media, financial transactions, retail, shipping, mobile operators – generate data that can be repurposed for public good. However, since that “public good” may be in a sector outside the firm’s expertise, identifying an efficient public use would require intervention of a third party.
On the demand side, companies are approached daily with data requests from academics, NGOs, governments, and international organizations. For mid-size and small companies especially, the burden of responding to this disaggregated demand can be overwhelming – processing requests, understanding needs, signing bilateral legal agreements, assigning technical staff to respond to questions, ensuring data security – all of which would be outside the scope of the company’s core business objectives.
In practice, many companies are amenable to sharing data for public good, so long as: (a) the sharing would not compromise the company’s competitive advantage or the privacy of its customers; and (b) sharing would require no time or resources on their part.
Since international organizations have similar aims and government counterparts, they are coordinating data sharing agreements through a new International Consortium for Data Collaboratives.
how it works
Consortium negotiates data sharing agreement with data partner.
Framework agreement signed. All future data requests from Consortium members are made using a template, which is linked to the framework agreement and does not require additional signature.
Project and research proposals are solicited from Consortium staff through open process on the Data Collaboratives web platform. Partner may opt to review proposals.
Data securely and responsibly managed though Data Collaboratives IT architecture and formal data management procedures.
Partner kept updated on research and products through own branded web portal.
Derived data products and algorithms shared with data partner and, where appropriate, with the public and third parties.
Short Term Impact
Since the World Bank launched the beta Data Collaboratives platform in April 2018, eleven development projects are now under implementation
By learning through real project implementation, the Consortium aims to transform proprietary datasets into sustainably generated, sharable insights for improving public sector services and infrastructure in emerging economies
Building Urban Resilience by Mapping Formal/Informal Transit for Freetown, Sierra Leone.
The team is receiving pro-bono data collection and processing and support from Where Is My Transport, for creating the first complete map of the Freetown transit system. This is in support of an on-going technical assistance program on urban climate resilience.
Closing the Data Gap in Bicycle Planning and Evaluation Efforts: Toolkit for Mexico City.
The team has received dockless bikeshare trip data from Mobike, which they are integrating with multiple data sources to improve planning cycling and pedestrian infrastructure.
Data Fusion for Outbreak Prediction
The team is using the Google Health Trends API, combined with data from Twitter and news APIs (Data Collaboratives covers the Google partnership) to improve prediction of outbreaks in fragile states.
Identifying Crash Hotspots in Nairobi
The team is using the Waze API to analyse user-reported crash data in Nairobi and comparing these data to official records as part of a road safety investment program.
Informing Emissions Mitigation and Livlihoods Enhancement Work in Fiji with Remote Sensing
The team is using Digital Globe data on Fiji from 2006 to 2016 to assess land use change and forestation levels.