Data Minimization in the age of Big Data!

In the age of Big Data where every second feeds tonnes of data to the cloud, there is an information overload happening. Do you really require to collect all that data and store it in the hope that you will use it someday? This is where Data Minimization comes into the picture.

As businesses grow, so does the amount of data/information that it collects over the years. Faced with the challenges of storing and managing Big Data, many are realizing that storing everything is not only unviable, but also unnecessary.

Don’t get buried under the Big Data deluge. Get in touch with Sysfore’s cloud specialists and we’ll help manage your information storage through Data Minimization.

Businesses have invested millions of dollars into storage infrastructure so that they can capture every bit of available data. But as their datasets have grown, many have realized that they simply do not need much of the low level data created. More importantly, they have discovered that much of that data will never be used.

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

What is Data Minimization?

Data minimization refers to the practice of limiting the collection of personal information to that which is directly relevant and necessary to accomplish a specified purpose.

Data Minimization - Minimizing Big Data

The deluge of information started as companies and organizations began to understand the power of data. As data becomes more ubiquitous and easy to collect, analysts are faced with a hurricane of potential data points. The impulse was to save all of it – indefinitely.

As the Internet of Things continues to grow, organizations are faced with more ways to collect data, including and especially private, personally identifiable data.

The focus needs to shift towards data minimization, where data is prioritized and unnecessary data is discarded. Instead of a “save everything” approach, data managers are now embracing a data minimization policy, keeping only what’s relevant and necessary. Even Walmart only relies on the previous 4 weeks of data for its day-to-day merchandising strategies.

Underlying Data Minimization benefits

One factor which is becoming increasingly popular among organizations is the cost and time factor involved in hoarding this excessive data indefinitely. All data storage costs money, and no business has an infinite budget to go on collecting and storing data indefinitely.

Another factor is the corporate computer security. Having too much data like personally identifiable data brings big risks. There is the risk of data loss and security breaches. A major leak of sensitive personal information can easily destroy a business or even lead to charges of criminal negligence.

Data Minimization mitigate both these factors significantly. It avoids multiple ways of storing data, thus reduces the cost of storing indefinite chunks of information. The value of the stored data decreases quickly and imagine the loss of a piece of information which is not even compatible now.

The idea of Data Minimization is going strong and it is only a matter of time these are included as standard procedures for mitigating risks.

Sysfore can guide you towards a Data Minimization approach for your businesses. For more information, you contact us at  info@sysfore.com or call us at +91-80-4110-5555.

Understanding AWS Big Data Services available in the Cloud

Amazon Web Services is the cloud platform which allows you to handle the Big Data. Whether it is structured or unstructured data, AWS enables you to collect, store, process, analyze and visualize Big Data on the cloud. Irrespective of the three V’s (Volume, Velocity and Variety) of the Big Data, you can build any application and support any type of workload.

There are many Amazon Web Services that are available for Cloud users to manage and use their Big Data. The most widely used services are Amazon Elastic MapReduce (Amazon EMR), Amazon Kinesis, Amazon S3, Amazon Redshift, and Amazon DynamoDB.

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Data Lake – Is it the future for Big Data?

Data Lake is a new storage concept that is gaining ground in the Cloud. Often the Data Lake term is being used as part of the Big Data solution. In theory, it is where you can store raw data in its native format, usually in Hadoop and Hadoop Distributed File System (HDFS). It can be used and processed to create data sets for other applications and users as and when needed. You don’t worry about the complex (and often expensive) data pipeline needed to simply collect and store diverse data.

The credit for coining the term Data Lake goes to James Dixon, Pentaho Chief Technology Officer. Dixon used the term initially to contrast with “data mart”, which is a smaller repository of interesting attributes extracted from the raw data.

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