Got Data? Let’s Party, Let’s Mingle!

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Got Data? Let’s Party, Let’s Mingle!

Most folks who work in Big Data will tell you – there is lots of partying, but very little mingling.

 

Everyone wants to mingle, but they just don’t know how. Easy to explain why:

 

Where is the Party? First-Party data refers means “your internal data”, the data generated by your commercial, industrial or operational activity. This is detailed data from your ERP, CRM, Health Records Systems, sensors, web logs, smart devices, etc. You generate loads of valuable data sets, and most of it has sensitive and restricted elements which need protecting.  Second-Party data is someone else’s first party data. Just like yours, granular and sensitive detail, but it provides you with greater reach because it’s not limited to just your operation. Third-Party data is either generic (available for everyone), depersonalized or aggregated to hide sensitive but valuable detail First- or Second-Party data contain. Useful, but not strategic.

 

Why Mingle? Most data scientists say 1 + 1 = 3 when it comes to data mingling of First and Second-Party data because doing so solves problems that First-Party data alone is insufficient to address and Third-Party data is not useful in addressing. For a data scientist, co-mingling, fusing and analyzing many data sets that have not been first depersonalized or aggregated is vital to address complex operational problems.

 

Just Imagine:  The myriad ways in which mingling Ford’s smart car, Visa’s credit card transaction data and Verizon’s smart phone usage data can be applied to solve pressing business issues, identify risk, and opportunity?  The new value generating opportunities that arise by mingling Verizon’s smart phone usage data with Netflix and Comcast’s audience activity data?

 

Mingling is Complex: Mingling First- and Second-Party data requires an entirely new way of protecting and governing sensitive and restricted data. Big Data Lakes and EDWs are only designed to protect First-Party data. Would you expect Verizon to trust Ford, Visa, Comcast and Netflix to protect all the sensitive and restricted data generated by its smart phones? Heck. No!

 

Each party will insist they define and impose the rules by which their data can be used and by whom, while at the same time allowing it to be mingled, merged, fused and analyzed at the granular level that addresses problems. Heyo, that’s a very complex technical challenge.

 

Mingling is Valuable: Would Verizon, Ford, Visa, Comcast and Netflix all love to mingle their data to create value if they could bypass trust and ensure their data was self-governed as they require? Heck. Yeah! (Spoiler alert: Secure Big Data mingling is the problem we solve with SecureQuery)

 

Mingling is Vital: Experts have been talking about this for years. They call it the Networked Economy and Autonomous Digital Ecosystems –a shared digital ecosystem of event driven, api-enabled, programmable lightweight, distributed autonomous agents (services) with imbued purpose via embedded rules and business logic. Nice, but how will all this happen without secure mingling of First-Party and Second-Party data?

 

Third-Party data firms can’t help either: If you were an executive at Ford, Verizon or Visa, would you trust some third-party data aggregator or broker (hello Equifax) with your data security required for mingling? (Heck. No!)

 

Beyond security, the value generating power of mingling First- and Second-Party data lies in your ability to determine for yourself —not through Third-Party interpretation—what information is relevant to your needs. When data is opaque or anonymized, or analyzed with a black-box algorithm, then who exactly is determining its relevancy to your problem?

 

Secure Big Data mingling is the problem we solve at SecureQuery.co

Randy Friedman

Randy Friedman is Founder/CEO of 4Dini Software

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