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The Chemistry and Trust of Big Data Sharing

I love chemistry! These days, I use chemistry to explain what secure data sharing is – and isn’t. Oddly enough, there is a bit of confusion which I can explain by example:


Our first client was a global industrial manufacturer – let’s just say they are BIG in Aviation. Their corporate objective is creating a single global, networked, digital supply chain to shave billions in inventory expense. They needed to analyze a ton of data to figure out how best to run those operations. The vast majority of those transactional data sets lived in locked silos because of highly restricted or classified data elements they contain.


To answer those complex resource allocation questions that digital supply chain transformation involves, my client needed to break down those silos for the purpose of co-mingling, fusing and then analyzing these many datasets. No other option would address their needs, but their existing big data investments had no facility to protect data once co-mingled and fused.


Here is the chemistry lesson to explain it what it takes to make big data shareable.   Data sharing is like making an alloy. Alloys are made by fusing different elements to form a new solution with properties different from the elements of which it is comprised. The important word here is different. You have to co-mingle, fuse and analyze different data sets to solve big problems. Full stop.


My client’s supply chain experts know exactly which problems they need to solve. Any one data set alone is not enough – or they would not have needed help. They used our technology to create an array of new data “alloys” needed to solve their problem.


Some companies say data sharing can be accomplished by simply aggregating away or obfuscating the highly restricted or classified data elements. Nope, not so. To make an alloy you need to use the raw elements, and transform them into something new.


Why do they suggest aggregation or obfuscation? Lack of trust.


Why would third parties trust you with their highly restricted or classified data, so you can do this alloy process between their data and yours? They shouldn’t.


That is why we applied the Zero-Trust paradigm of network security to data sharing. Zero-Trust data security is the basis of our architecture that makes shared data sets self-governing so they cannot be misused. Secure data sharing means a) making data useful (making alloys) while presenting misuse (enforcing Zero-Trust).


Oh, there is more. Very often, new problems arise which require new “data alloys”. The secure sharing mechanism needs to be sufficiently agile and adaptable that any alloy can be created while preventing misuse. Yep, we solved that!


Click here to read how our approach worked for our client’s digital supply chain transformation efforts, and why aggregated, depersonalized or masked data sets do not solve real problems.

Randy Friedman

Randy Friedman is Founder/CEO of 4Dini Software

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