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Use Case

Privacy-Protected Data Sharing & Collaboration

Data-driven organizations are continually trying to collaborate with data partners to augment the value of their data assets; however, regulations, IP protection, and lack of trust limit the movement of data.

Our solutions help organizations overcome these challenges by providing a way to generate privacy-protected datasets or by allowing them to train machine learning models without exposing, sharing, or moving data.

Let Data Scientist access high value privacy-protected data

More Data

Access data previously restricted, due to privacy or data residency restrictions, and augment your datasets.

Improved Models

Augment your datasets and use that data to improve your models.

Monetize data assets and models

Let other organizations access your data or models without losing ownership.

 

Example

Unlocking Data Monetization

One of the largest aggregators of credit card transaction data wanted to open up a new revenue stream, by using its data for secondary purposes with its business partners: retailers, banks and advertising companies. They could help their partners achieve better ad conversion rate, improved customer satisfaction, and more timely offerings. However, the use of data for secondary purposes needed to respect user privacy and specific regulations. 

Monetizing Healthcare Data

A large healthcare network, with millions of patients, wanted to share patient data
for Real World Data (RWD) purposes, academic research, training of new staff, and
improving patient outcomes. However, they were restricted from allowing others to
access their data because of privacy regulations.

Optimizing Multi-Site Research Process

A medical research institution wanted to do a cohort analysis for a specific group of
patients in collaboration with other health care networks. However, their process
was cumbersome and constrained by privacy regulations. This process created enourmous overhead and delays reducing the impact of the research.

Solving Data Residency

A Fortune 500 reinsurance company wanted to understand their risk exposure across their
international locations. However, they were unable to co-locate the data due to data
residency regulations. Their options involved having models trained in each
location, rather than across the data, which resulted in inferior results. Alternatively, they could de-identify the data and co-locate it, but that significantly reduced its quality.