In the recent times, Big Data has been put to different uses.

Superstar Lady Gaga has created her very own social network Littlemonster.com from the data of millions of fans who follow her on Facebook and Twitter. Many big healthcare organizations today are adopting advances data analytics which are helping them in offering personalized care to the patients, determining the patient population of a particular type, advancing in medical research and much more. Not just this, even the Central Intelligence Agency of the US is looking for data scientists who can create robust database systems.

There are reasons why Big Data has really taken off the market and why more and more people have stopped considering their bulk external as trash. The reason lies in the characteristics of Big Data, which are stated below:

Volume

The biggest attraction of big data to big data analytics is its ability to process large amounts of data. The integral challenge of this processing to IT structures is volume, which calls for a distributed approach for query and scalable storage.

Velocity

The other important characteristic of big data is the velocity with which data flows into an organization. The pattern followed by this parameter is similar to that of volume.

Variety

Data used in an organization is usually unordered and not ready for any kind of processing. Therefore, the data is diverse in nature and form.

Big Data in Practice

There is a difference between theoretical practices and actual deployment. The tool that should be used depends on the different dimensions of deployment. One of the major decisions that have to be made includes whether an in-house or cloud-solution must be used.

There are three solutions available: cloud-based solutions, appliance-based solutions, and software only solutions.

The decision depends on the assessment of issues like data locality and project requirements. The other facet of big data that plays a significant part in this deployment is the fact that the volume of this data is so large that it is not possible to transport to another location.

Therefore, the priority, in this case, is not the data, but the program that is to be used to transport the data.

Even when the data volume is manageable, it is not usually possible because of issues like data locality. Finally, a major issue arises when dealing with big data is not the data or the infrastructure involved, but it is related to cleaning up data.

Data acquisition and cleaning can be costly. If you consider the relevance and implementation for big data for a real business problem, factors like advertising strategy and measures taken for increasing spend per customer play a crucial part in deciding the kind of implementation required.