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Public & Health Research

Collaborating for the common good.

Facilitating data collaboration, whether within the public sector, or between public, private and research bodies, provides enhanced insight into the way people live, work and communicate; preparing the foundations for effective, evidence-based public policy.

“The term data collaborative refers to a new form of collaboration, beyond the public-private partnership model, in which participants from different sectors — including private companies, research institutions, and government agencies — can exchange data to help solve public problems. In the coming months and years, data collaboratives will be essential vehicles for harnessing the vast stores of privately held data toward the public good.”
Stefaan G. Verhulst
Co-Founder and Chief Research and Development Officer of the Governance Laboratory @NYU (GovLab)

Quick and easy reporting

InfoSum provides an instant view of the customer overlap once both parties have prepared their data. This immediately quantifies the size of the intersection and joint data fields giving a flavour of the value of the joined data.

Full reports can be created within minutes as predefined report selections and chart formats extract the key information needed to solve research issues. Report interfaces have been built with flexibility and simplicity in mind. Technical data scientists are not needed to explore the data.

Answering diverse research questions


Profile existing users by learning from second or third party datasets. Add demographic, lifestyle or purchasing data for richer profiling.

Commercial partnerships

Understand the profile of the existing customer overlap with commercial partners. This may help with funding, communication or R&D efforts.


Recognise the joint characteristics of the 2 datasets. This could help explain the cause of a particular issue or throw up new ideas on the association.

Nudge strategies

Assess what might incentivise changes in behaviour by looking at the characteristics of those who have changed.

Propensity modelling

Establish the likely predictors of behaviour and future intersection of the two datasets.

Data quality audit

Evaluate the quality of your dataset by comparing it to a third party dataset e.g. electoral roll.

Exploring different segments within the datasets

Understand joint data subject profiles

Explore the overlap between your datasets and the characteristics of those in the intersection.

Correlation to their individuals

Find look-a-like people by targeting those with similar traits of your existing dataset. Use joint users to identify how to find them in the contributor’s dataset.

Correlation to your individuals

Predict which members of your dataset are most likely to become members of the other party’s dataset by looking for those with shared characteristics.

Find unique members

Build a combined picture of the disparate members of the two datasets by excluding overlap of joint users.

Joining sensitive data for the public good

Informing public health research

Collecting tracking data (e.g. wearables technology) and health records to securely bring health and social care data together so it can advise research-led health initiatives.

Improving transport efficiency

Using insights from joined smart travelcard account information and local transport programme participants (e.g. school, day care transport), to nudge groups towards new travel choices such as smart mobility, integrated transport routes or car sharing schemes.

Reducing energy costs

Collating data on energy customers and smart meter usage and processing it alongside local residential information to assist with better policy making, investment and business decisions, as well as fuel the creation of more tailored services for certain communities  — increasing inclusion and meeting the specific aim of reducing fuel poverty.