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Big Data – What is it?

Big data, as the name suggests, represents data that is big. It is the term that is used to define data that is known to have exceeded the processing capacity of the traditionally used database systems. The fundamental characteristics of this data include its big size, fast moving speed and structure that cannot be fit in database architectures. The only way you can use this data is by using an alternative and more efficient processing mechanism. However, recent past has presented some viable approaches for processing of big data. As a result, big data has become a buzzword. These approaches tame the variability, velocity and volume of this data. It has become possible to unleash the power of big data and making the same available to the less well-resourced establishments with the use of open source software and cloud architectures. This data can be used by organizations in two ways. Companies can use this data for analytics and use it to get better insights to their business. Secondly, it gives these companies a valued trait of agility. The term ‘big data’ is just as nebulous as the term ‘cloud’. It includes a diverse set, which entails data from satellite imagery, web page content, server logs and data from social networks, in addition to data from several other sources.

In the recent times, Big Data has been put to different uses. Superstar Lady Gaga has created her very own social network called ‘’ from the data of millions of fans who follow her on Facebook and Twitter. Many big healthcare organisations today are adopting advances data analytics which are helping them in offering personalised 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 behind 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 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 include if a in-house or cloud solution must be used. There are three solutions available. These include 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.

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