Private Data for Public Good: Facilitating Digital Footprint Data Sharing Between Business and Academia

Digital Footprint data—routinely collected through everyday digital interactions such as online purchases, wearable devices, and app use—offer huge opportunities for societal benefit by providing large-scale, detailed insights into every day behaviours. Despite this potential, accessing these data remains challenging due to their proprietary and sensitive nature. This study draws on empirical evidence from a collaborative workshop, which brought together 50 multi-sector participants, including researchers, industry partners, government officials. Through facilitated discussions, participants examined the challenges and benefits of industry-academia partnerships in Digital Footprints research. Four key themes emerged: data access and entry to partnership, governance and infrastructure, incentives and value, and legitimacy and trust. By combining perspectives from both academics and industry, this work provides novel empirical insights into how multi-sector partnerships can maximise the value of Digital Footprint data for public good. Our key recommendations include the need for trusted intermediaries and internal company champions to facilitate collaboration; shared technical infrastructure, such as Trusted Research Environments and common protocols; emphasising societal benefit as a shared incentive alongside reputational gains for commercial partners; and recognising public trust as foundational to the long-term sustainability of Digital Footprints collaborations across stakeholders.

Authors

Publication Details

Published
2026-10-28
DOI
https://doi.org/10.31235/osf.io/63zj5_v1
Primary Topic
University-Industry-Government Innovation Models
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Private Data for Public Good: Facilitating Digital Footprint Data Sharing Between Business and Academia

Michelle A. Morris, Romana Burgess, Anya Skatova, John Stuart et al.
2 links
University-Industry-Government Innovation Models
preprint

Private Data for Public Good: Facilitating Digital Footprint Data Sharing Between Business and Academia

Michelle A. Morris, Romana Burgess, Anya Skatova, John Stuart, Anna Cupani, Laura Fryer
preprint en

Abstract

Digital Footprint data—routinely collected through everyday digital interactions such as online purchases, wearable devices, and app use—offer huge opportunities for societal benefit by providing large-scale, detailed insights into every day behaviours. Despite this potential, accessing these data remains challenging due to their proprietary and sensitive nature. This study draws on empirical evidence from a collaborative workshop, which brought together 50 multi-sector participants, including researchers, industry partners, government officials. Through facilitated discussions, participants examined the challenges and benefits of industry-academia partnerships in Digital Footprints research. Four key themes emerged: data access and entry to partnership, governance and infrastructure, incentives and value, and legitimacy and trust. By combining perspectives from both academics and industry, this work provides novel empirical insights into how multi-sector partnerships can maximise the value of Digital Footprint data for public good. Our key recommendations include the need for trusted intermediaries and internal company champions to facilitate collaboration; shared technical infrastructure, such as Trusted Research Environments and common protocols; emphasising societal benefit as a shared incentive alongside reputational gains for commercial partners; and recognising public trust as foundational to the long-term sustainability of Digital Footprints collaborations across stakeholders.

University-Industry-Government Innovation Models
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon