New Age ‘Big Data’ and the Concept of Data Minimization

Being entrepreneurs, we often tend to ignore the humanitarian aspects of data analytics. For most of us- bigger caveats of data are worth looking into as sleeker and compacter resources are often ignored. However, the new gen ideas of Big Data travel way beyond the concept of ‘Bigger is better'.

It all starts with a constrained approach- urging organisations to focus more on less.

The present layout has given out a few evasive predictions- making Data Minimization a necessary yet innovative approach. In the wider realm of data warehousing and analytics- Minimization has always been an ignored commodity. We all started arriving at a conclusion with Amazon's inclination towards the judicious usage of same. While the newer laws are mostly in favour of data protection- Data Minimization is more of a precursor to these. It theorizes personal pieces of information or data as adequate and relevant resources- shaming the overexposure of data. In the truest of terms it means data minimization- an act that holds, collects and uses only the bare minimum- mostly for safety and security reasons.

Defining Data Minimization

Before we delve into the details- we need to assess as why any information is important. Firstly we need to fulfil a definite purpose, using the data warehouse to our benefit. But do we actually need every layer of information to get the job done. The answer is certainly no.

With Data Minimization it becomes easier to constrict the free flow of information. Now when we all have realised the true potential of data- it becomes imperative to store it and fix those leaking data points. The approach towards minimization helps save data and even put them to good use. That said, the IoT has grown beyond its cloak- eating up more data- irrespective of the personal or even private undertones. This is where the concept of Data Minimization comes to fore as most companies are hoping to put a tap on this free fall. They are trying hard to segregate data hoardings into useful sections and junk- making it easier to identify the required clusters. While other resources targeting these ideas might not be cost effective- Data Minimization will surely be economical and most importantly, safe.

This approach is slightly different than the idea of ‘Save Everything'. Only the relevant bits of information are allowed to pass- giving ample importance to the concept of necessity. Walmart sets an example by saving in only a month's data for the purpose of daily merchandising.

Underlining Benefits

Having excessive data at our disposal will give rise to two abnormalities; the first being decrease in value. Second issue will concern safety as it will be hard for us to track every aspect of Big Data and therefore some of it might go awry. This is where Data Minimization comes to play- reducing concerns and even the overall costs.

Coming to costs- Data Minimization shuns the multiple ways of storing data. Business need to shell out a lot when it comes to storing indefinite chunks of information. These added expenses can now be avoided- with immediate effect.

Again with so much of data floating around- safety will always be a concern. How well we save the information- in case of a leak or break-in- the entire framework will be compromised. This is mostly a concern with ‘personally identifiable' data. Imagine how ironical it would be to lose a piece of information that isn't even compatible now.

The ideas of Data Minimization are going strong and it is only a matter of time these are included as standard procedures for mitigating risks. However, this isn't a tedious affair anymore- as individuals and companies are pairing up to achieve a sense of harmony.


Fecha publicación: 16/03/2016
Autor: Richard Smith

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