Synthetic data is a form of collaboration in which businesses can share information with each other to analyze it without sharing real customer or patient information. An obvious downfall of collaborating by sharing synthetic data is that businesses are sharing generic data sets and not real data; however, synthetic data is acceptable when real data is unnecessary.
For example, synthetic data may be used by a credit card aggregator to determine macro trends from the data because not every bank collaborates with them and not every credit card provider will offer data. In those situations, synthetic data would be acceptable to glean industry macro-trends from the data.
However, if a company wanted to determine if a customer deserves a particular credit limit or understand how a small part of the population’s microtransactions yield a certain insight, they would need real data.
Another problem with sharing synthetic data is that outlying data is often omitted, making the data set inaccurate or can later be identified through spear-phishing or cross-correlation.
TripleBlind is far superior to sharing synthetic data because businesses can fully analyze real data in order to understand real trends. TripleBlind’s solution allows for data collaboration without jeopardizing privacy or compliance. Data shared through TripleBlind’s solution remains de-identified, private and can only be used for its intended purpose.
As shown in the above chart, collaboration via synthetic data has a negligible impact in most categories where accuracy and compliance are necessary. On the contrary, TripleBlind’s solution fulfills criteria across the board, making it a superior way to share data.
To learn more about how TripleBlind compares to other competitors and methods of data collaborations, follow us on LinkedIn and Twitter to be notified when we post the next installation in our Competitor Blog Series.
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