Is it possible to aggregate Big Data for intelligence purposes without sacrificing risk mitigation?

I’m not sure if there is an appropriate line to draw between risk mitigation/compliance and giving access to data to the right folks so that intelligence can be derived. The challenge is extracting the purified content out of it—leave out the noise and pick up the signals so that you can do something with them. With all of these different platforms and cloud solutions that folks are adopting, their data footprint ends up all over the map. At some point people want to put it together, make connections across these data sets to derive intelligence for operational efficiencies, new market identifications, optimizations and new service offerings.

Anonymous Author
I’m not sure if there is an appropriate line to draw between risk mitigation/compliance and giving access to data to the right folks so that intelligence can be derived. The challenge is extracting the purified content out of it—leave out the noise and pick up the signals so that you can do something with them. With all of these different platforms and cloud solutions that folks are adopting, their data footprint ends up all over the map. At some point people want to put it together, make connections across these data sets to derive intelligence for operational efficiencies, new market identifications, optimizations and new service offerings.
1 upvotes
Anonymous Author
It's the same problem of keeping an inventory to know what you have and where. We used to talk about the inventory in terms of what data is in what system, but now it's what data is located in our house, and then what data is in 10 or 20 other places?  It's harder to keep track of, so how do we protect that?  Zero trust is a different way to properly authenticate users, and ensure that they are authorized to access certain data. We have to allow access to systems and data they're supposed to and not the data that they aren't.
0 upvotes