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.
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.
Setting Up a Privacy-Protected Data Lake
A large bank wanted to broaden access to its data lake without compromising data privacy, preserving the data’s analytical value, and at reasonable infrastructure costs. Current approaches to de-identify data did not fulfill the compliance requirements and business needs, which had led to several bank projects being stopped. The issue with techniques, like masking, tokenization, and aggregation, was that they did not sufficiently protect the data without overly degrading data quality.
Making Data Available to Third Parties
A telecom company wanted to work with third-party consulting firms, but could not give them access to their data because of user privacy concerns and regulatory constraints. The data needed to be de-identified, and privacy risk assessed, to ensure the organization was using the data in an ethical and responsible manner. Furthermore, they wanted to link the analysis back to the individual user once they got the results back from the third-party consulting firm.
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.
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.
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