---
title: "Data collaboratives"
chunk: 1/2
source: "https://en.wikipedia.org/wiki/Data_collaboratives"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:48:56.315156+00:00"
instance: "kb-cron"
---

Data collaboratives (sometimes called "corporate data philanthropy") are a form of collaboration in which participants from different sectors—including private companies, research institutions, and government agencies—can exchange data and data expertise to help solve public problems.

== Types ==
Data collaboratives can take many forms. They can be organized as:

Public Interfaces: Private firms publish select data assets to be public for use by external parties. Firms typically present this information as Application Programming Interfaces (APIs) or data platforms.
Trusted Intermediary: Private sector firms share data with partners from public, civil society actors, and academia. Data can be brokered by third parties, who provide valuable data under fixed terms and time limits to non-private organizations. It can also be run through third-party analytics, which shares data with data stewards to run analysis and share those findings with external actors, providing the outcomes of the data without exposing the sensitive information.
Data Pooling: Multi-sectoral stakeholders join "data pools" to share data resources. Public data pools allow partners to openly access and independently use the data, while private data pools limit access and contribution to the information.
Research and Analysis Partnership: Organizations share data and "proprietary data assets" with public and academic institutions to analyze and advance a public objective. Through these data transfers and data fellowships, access to and terms for use of data are highly controlled.
Prizes and Challenges: Organizations make data available to qualified applicants through competition for innovative use or platform design to add value to the firm. Open innovation competitions, like LinkedIn's Economic Graph Challenge, allow for open and broader use of data by many independent users, while selective innovation challenges give limited data access, narrowing the scope of its application to a specific situation.  Oftentimes, competition members are bound to data responsibility guidelines.
Intelligence Generation: Companies use data to build shareable tools and release them for public use. Although no formal, direct cross-sector sharing occurs, it lays the foundation for knowledge transfer and a culture of open, data-driven analysis.

== Reasons for data collaboratives ==
The big data boom has demonstrated the power of data to inform and design public projects in an accountable and iterative manner. However, unequal access to certain data across sectors limits the ability of groups to find, access, or be made aware of valuable information, hindering social innovation. Data collaboratives create networks that bridge access and knowledge gaps by bringing different sectors together to share data to address social challenges.
The GovLab argues data collaboratives wherein a private sector data holder shares data with other groups tend to be motivated by a desire for:

Reciprocity: Sharing data with others can guide mutually beneficial business decisions.
Research and Insights: Sharing data can spark new and innovative approaches to issues.
Reputation and Public Relations: Sharing data, especially to advance public issues, can bolster the image and reputability of a firm, attracting new socially-conscious clients, talent, and followers.
Revenue Generation: Corporate data can be sold to data collaboratives, generating novel revenue streams.
Regulatory Compliance: Data collaboratives can help corporations advance transparency and trust by establishing and following data sharing protocols.
Responsibility and Corporate Philanthropy: Data collaboratives allow businesses to drive meaningful corporate social responsibility programs.
Data collaboratives can help respond to service delivery and emergency preparedness and disaster response problems. Robert Kirkpatrick, Director of UN Global Pulse noted that "the lack of innovation [in these sectors have] resulted in a failure to protect the public from what turns out to be preventable harms."

== Incentives for private sector participation ==
According to The GovLab, data collaboratives can provide five main benefits for public problems:

Situational awareness and response: recent, robust, and quality data from private or public sectors can help governments and civil society better mobilize in crisis and emergency situations. For instance, the Mobile Data, Environmental Extremes, and Population Project (MDEEP) is a collaboration between international organizations and telecommunications companies in Bangladesh to build "large-scale population displacement models to understand population movement related to natural disasters."
Public service design and delivery: Access to previously inaccessible datasets can enable more accurate modelling of public service design and guide service delivery in a targeted, evidence-based manner. For example, collaborative use of datasets by governments, international organizations, aid groups, and private telecommunications carriers during the 2014 Ebola outbreak helped track and trace the virus.
Knowledge creation and transfer: Utilizing a larger number of and more diverse datasets can fill knowledge gaps to better respond to the problem at hand. The All of Us Research Program, created by the Obama administration in 2015, allows participants to share their health data to a secure system, which is then aggregated and anonymized for researchers to study and advance medical science.
Prediction and forecasting: Data from the past allows for informed prediction in the future, allowing groups to identify problems and respond more quickly. Leveraging search engine query data, researchers identified search terms, times, demographics that correlated with suicidal ideation across Indian youth.
Impact assessment and evaluation: Access to additional datasets can help organizations monitor and evaluate the effectiveness of policies and iteratively adapt programs for better service delivery. For example, the US Food and Drug Administration's Sentinel Initiative used anonymized patient information sourced through the TriNetX Live USA Network to assess how many adults hospitalized for COVID-19 experienced or succumbed to thrombosis-related complications.

== Examples ==
From 2017 to 2019, the percentage of companies entering data-related partnerships rose from 21% to 40%. A growing share of business competitors are also deciding to connect their data—jumping from 7% to 17%. In a 2019 report, the World Economic Forum and McKinsey estimated that connecting data across institutional and geographic boundaries could create roughly $3 trillion annually in economic value by 2020.
The following is an illustrative (but not exhaustive) list of some data collaboratives: