Customers' data remain confidential throughout the entire process. We have made sure that it is impossible to identify customers' data.
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Customer data is anonymized by Pricefx via double-blinding (see below how the process works), an automated process that removes customer identifiers by assigning a random key to the customer data immediately at the point of extraction to disassociate it from the customer, ensuring that it is impossible to trace individual customer data.
Through the double-blinding process, no one outside of Pricefx has visibility to unique customer identifiers, including Pricefx customers. This means, that as a user of Plasma, the client can only see their data against other players in the industry without being able to identify them.
Benchmarks are compiled from aggregated metrics with a number of anonymized entities to ensure that users cannot self-identify certain companies.
This rule also protects the identity of the underlying entities in addition to improving statistical validity. Benchmarks are not shown when sufficient data is unavailable, e.g., in case of more detailed KPI filtering by a user (by industry, region, etc.).
Due to applied aggregation of data, it is impossible to conduct a competitive comparison on standalone items (e.g., product price or even product lines) for users to self-identify certain companies.
PricefxPlasma works with the following data from customers:
Transactions data
Quotes data
Customer data is extracted and anonymized on a monthly basis.
The anonymized data is transformed into standardized metrics that are then loaded into the PricefxPlasma platform, which aggregates and filters the metrics further to create industry-level benchmarks.
The resulting benchmarks are distributed to the customers’ environments as a set of standard Pricefx dashboards and customers can also include this data in their own dashboards, allowing for a direct comparison between their company and the benchmark.
The benchmarks are made available with a three-month delay, fast enough to allow for relevant analysis but sufficiently disconnected from the current market status to avoid any compliance issues.
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Basics of anonymization
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In double blind anonymization, data is first anonymized by removing personally identifiable information such as names, addresses, and specific identifiers. This is the first layer of anonymization, which ensures that the users cannot identify the data subjects. The second layer of anonymization involves masking other identifiers that could potentially reveal the identity of the data subjects. For example, if data collection includes information about the geographic location of data subjects, this information will be masked or generalized to protect their privacy. By using double blind anonymization, benchmark comparisons can be done without compromising the privacy of data subjects. This technique helps to ensure that sensitive information remains protected and that research is done in an ethical manner. |
How exactly it works in Plasma
Plasma does data anonymization in 4 stages to ensure that it is impossible to trace individual customer data.
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