How can organizations successfully convert big data into real-world decisions?

The word wide web is turning into a colossal heap of data that is being stored at hundreds and thousands of datacenters across the world. According to a recent research made by Data Science Central, the size of data on the internet is expected to double in every two years. Such amount of data is not only hard to be stored but it is also posing a challenge for organizations and businesses to use this data for making useful decisions.

Ever since the idea of big data started making headlines in the cyber fraternity, organization have been trying to understand and make good use of this phenomenon. This is why businesses are pouring millions of dollars into the research while have already come up with tools to make it easier for organizations to make decisions based on the results from data sets.

Wikipedia defines big data as the term for datasets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy.

There is no hard and fast rule to call any information big data, however, any kind of information that needs new tools and techniques to be processed could be big data.

The processing of such data is to be done by groundbreaking technologies compared to the old hardware that the industry has been using for decades.

There is currently an on-going debate among the stakeholders on how useful it could be investing in this industry. The good sign is that a number of organizations have already been able to turn the idea of big data into lucrative businesses. Although there is a lot more to be done both in terms of research and practical development, this new arena in the digital landscape cannot be ignored.

Universities and independent think tanks have termed big data as the next big thing. It has been the buzzword among technical groups for quite some time now. There is steady development being made in data storage, computation, and visualization which gives a lot of hope for the future of this industry.

Another reason why the industry is attracting huge investments from corporations is that the field is quite new and by investing time, money and manpower in the right place at the right time, companies can take lead and set an example for others to follow.

One of the most important question about big data is how it can be analyzed given its huge size and complexity that an ordinary analytical software cannot manage? The most common way to analyze big data is by using a method called MapReduce. The process includes processing data sets in a parallel model. The process itself includes two part, Map function and reduce function.

The Map function does all the filtering and sorting of data. It then categorizes each of the processed datasets in a structured form so that it can later be analyzed easily.

The next part of the process is the Reduce function. The Reduce function creates a summary of all the data that was categorized in the previous step.