Our interaction with the Internet, specifically on Social Media sites, has become part of our lives nowadays. It has come to a point wherein our day won't be complete if we are not able to check our e-mail, update our Facebook account or status, or tweet an idea or two. Some of us have even become so absorbed with our "online world" that we prefer it over our "actual life."

As we weave our way through the web jungle, we unconsciously leave behind traces of data. Every time we sign up for a certain online service, answer a random survey, put a simple post on Facebook, or search in Google, certain data may already be extracted from us users.

These traces of information we leave behind in the web are automatically accumulated by companies who have their own websites. Take for example, Google. With over a million users, may it be a member or non-member, they amass many traces of information, which eventually mount up to loads of data. Imagine keeping all that in storage.

With all these information accumulated by companies for years emerged a new back office outsourcing service.

Big data is a general term used to describe the voluminous amount of unstructured and semi-structured data that a company creates. When you say big data, we're talking petabytes and exabytes of data, not just any amount. These capacious data would take too much time and money for relational database and analysis.

Unstructured data can amount to 80 percent of an organization’s data. If the volume of unstructured data accumulated in a year is left unmanaged, the organization would have to shoulder a staggering cost for its storage. Also, unmanaged data will cause liability of information if they cannot be located, in the event if compliance audit or a lawsuit.

The process of examining these huge volumes of data in a variety of types is called big data analytics. The method is used to uncover hidden patterns, unknown correlations, and other useful information.

Accumulated information from the big data can be useful in providing competitive advantage over rival organizations, uncovering effective marketing techniques that will yield business benefits.

Data scientists, analyzers, miners, and other types of data experts are enabled to examine the huge amounts of transaction data as well as other data sources that may be left untapped by conventional business intelligence (BI). These other data sources may include Web server logs and Internet click stream data, social media activity reports, mobile-phone call detail records and information captured by sensors. Some people exclusively associate big data and big data analytics with unstructured data of that sort, but consulting firms like Gartner Inc. and Forrester Research Inc. also consider transactions and other structured data to be valid forms of big data.

The execution of this new Back Office Outsourcing Service can be done by using software tools commonly used as advanced analytic disciplines such as predictive analytics and data mining. But using the unstructured data sources for big data analytics may be too big for traditional company databases. To support the big data analytics environment, a few technologies for the service have emerged. The technologies associated with big data analytics include NoSQL, Hadoop, and MapReduce. These technologies form the core of an open source software framework that supports the processing of large data sets across clustered systems.

But being cautious is also important when one starts to adapt to the new trend. A lack of internal analytical skills and the high cost of hiring analytical professionals can be a pitfall for companies getting big data analytics systems.

Big data analytics can provide companies a concrete basis for the decisions for their business. The information gathered from the amassed structured data can aid in the prediction of consumer behavior and sales movement. Also, this can lead to the discovery of new marketing techniques or direct the application of unique strategies, giving the company an edge over its competitors.