Data Minimization Breathes New Life Into Old Hardware

Image of two documents or pieces of data, one smaller and one larger

Posted on Friday, March 18, 2016

As more businesses come up against the challenges of storing and managing Big Data, many are realizing that storing everything is not only unviable, but also unnecessary. Early Big Data adopters have thrown millions of dollars into storage infrastructure so that they can capture every scrap of available data.

But as their datasets have grown, many have come to the realisation that they simply do not need much of the low level data created. Perhaps more importantly still, they have discovered that much of that data will never be used.

Whether they use in-house datacenters or cloud archiving options, there is a cost associated to all of this unnecessary information that they hold.

Data minimization and existing infrastructure

Whether businesses already have Big Data programs in place, or are still working towards deployment, the focus needs to shift towards data minimization. Under this regime, data is prioritized and unnecessary data is discarded.

Discarding data may go against the prevailing wisdom of Big Data advocates, but even household names like Walmart are seriously reconsidering the volumes of information they store. America’s biggest supermarket runs a Big Data program off the back of the previous four weeks data for instance; anything older (barring that which is required for legal and compliance reasons) is discarded.

This data minimization process not only reduces the volumes of data that need to be stored, but also reduces pressure on the CTO to develop an infinitely scalable storage infrastructure. Instead they can look at how best to maximize existing storage systems to provide additional capacity, making use of vacant disk slots and the like.

There is also the option to increase capacity more gradually using post warranty hardware to provide trusted, reliable storage using existing assets. Backed by a third party support and maintenance contract, there is no reason that EoSL hardware cannot be used in a minimized Big Data deployment.

Into the future

The imminent explosion of data created by IoT deployments is only set to increase the capacity demands placed on systems. As valuable as this information is, businesses will need to act now if they are to take a structured approach to managing growth.

Otherwise they will simply adopt the default position of purchasing additional capacity to avoid the inconvenience of slimming down their data assets. An approach that reduces the margin of profitability on their Big Data programs. 

To learn about using existing storage hardware assets more intelligently and using EoSL assets as part of a data minimization strategy, please get in touch.