Your business customers will likely not want their data linked to their identities, or in some cases, will not want any data revealed, even anonymously. In extreme cases, your existing customer contracts may prohibit aggregated (inter-customer) analytics of any kind. If so, this will go against the trend of data analytics, which is starting to inform and enhance business performance everywhere.
We have anticipated and programmed various rules for dealing with data anonymity, from the moderate to the severe. In a severe case, no peer data of any kind can be mentioned, only the standing of the target customer.
In moderate cases, the rule can be this: peer operating and performance data can be revealed, but only as single data points and not in combination, and only anonymously. However, anonymity can be overriden by a specified rule. For example, anonymity is overriden if the target customer and the peer have the same owner.
To show how this works, we took the very same hospitals-application setup that serves as a public showcase for our benchmarking engine, and (1) specified moderate anonymization, and (2) overrode anonymity if both the target and the peer hospitals are government-owned or belong to the same hospital network. Enter a hospital to see how moderate anonymization works.
To illustrate strict confidentiality, we set up a separate hospitals application in which nothing is revealed about any peer unless the same override rules as above apply, namely, both target and peer are government-owned or belong to the same hospital network. Enter a hospital to see how strict confidentiality works.