Lessons Learned in Data Science

December 14, 2016

Earlier this month at the New Jersey Technology Council’s Annual Tech Day, Rosario Mastrogiacomo, our Director of Architecture and Design, discussed how there is a divide between cybersecurity and the basic tenets of data analysis, especially big data, predictive analysis and data mining.

Today, data analysis is at the core of most jobs, and organizations are seeing the value in making informed decisions by taking data that in the past was ignored and now is being used by taking a deep dive into what is available.  To that effect, open access to data is a basic tenet of data science.

However, the open nature of data science is in direct conflict with the fundamentals behind Data Governance and Security.

“Data Governance and Security is the exact opposite of (providing open access,)” said Mastrogiacomo.  “It’s about locking down access to your data, so that only individuals who should have access get it.”

While open access to data allows companies to make informed decisions, the risk for a data breach is high and many examples exist where customer and employee data is lost, and companies face stiff fines and damage to their reputation.  Add the regulatory requirements that organizations need to comply with and you only add more challenges.

The easy way to solve this problem?  Lock everything down.  But that isn’t realistic.  Data analysis is critical.  Instead, it’s about keeping a watch over what you have.

“It’s about knowing what you have…the challenge is that most organizations don’t know what they have” said Mastrogiacomo.

There must be coordination between security and business use. Risks should be evaluated and a least privilege access model should be in place. It’s important that not all data be available to all people, and equally important that all data is not locked up and unusable.

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