6 big data privacy practices every company should adopt in 2018
Issues surrounding data privacy are as legally unresolved today as they were two years ago, but the recent Equifax breach now puts a clear focus on them that strikes fear into the hearts of CIOs.
The Equifax data that was breached was not big data. However, big data is a major privacy concern for IT because so much of it is coming into enterprise data repositories from so many sources; and it comes in many shapes and sizes.
After Equifax, CIOs can rest assured that their CEOs and boards will be following their work in data privacy closely—and big data is one of the areas they’ll be most concerned about.
What operational steps can IT take to assure at a grass root level that sound data privacy practices are employed for their big data?
Many cloud vendors can provide the levels of privacy and security that you want for your big data—but you have to demand and be willing to pay for it. Never assume that by default your cloud vendor will automatically apply best practices. Your staff should carefully evaluate the privacy protections that each of your big data cloud vendors offers and determine whether these data protection levels meet your own internal governance standards. If a cloud vendor’s data privacy practices don’t meet your own governance standards, pass on the vendor. Also ask your external IT auditors to review all cloud-based vendor data protection and security practices as part of the IT audits that the auditors perform for your company. Vendor data protection and security levels should minimally be checked on an annual basis.
Most public cloud vendors offer private cloud services, too. Placing your data in a private cloud is more expensive than being a multi-tenant customer in a public cloud, but the private cloud deployment better separates your organization’s data from that of others. Cloud-wise, it is the next best thing to keeping your data on premises.
You can the protect the data privacy of your customers and still perform critical trends analysis. One way that this anonymization can be accomplished is by encrypting data elements that personally identify someone.
