Big data has become a buzzword among businesses and executives, and with good reason.  More information is transmitted from various electronic devices daily than previously thought possible. Consumers continually embrace forever evolving technologies making it easier than ever to collect and collaborate this information. 

We generate more data every 2 days than was generated from the big bang until 2003. That data is now being measured in zettabytes.  To put it simply, if your average cup of coffee was a gigabyte, a zettabyte would be the Great Wall of China. Today, more than 2.7 zettabytes exist in the digital world.

But What Exactly is Big Data?

Technically speaking, big data is incomprehensibly large packets of data that can be computed and then analyzed to generate trends and models, specifically targeted towards Clients/Customer’s persona, Market Movement/Response, and Decision Making.

To break it down, Big Data refers to large sets of structured, semi-structured or unstructured data, from either traditional or digital sources. Think of all the information that inundates your business on a daily basis.  However, there are other data procuring sources like Business transactions, machine-to-machine data, sensor technology, databases, video, audio, emails, even social media that assist the business in roaring. The Big Data industry is estimated to grow to about $53.4 billion by 2017.

The volume of data can be so large and complex that conventional data processing methods and applications are just unable to deal with them effectively. Google was processing close to 20,000 terabytes of data in the year 2008.

Big Data Analytics

The Size Doesn’t Matter; It’s How You Use It

With Big data, it’s essential to understand that it’s not the amount of data that is collected, but how it’s analyzed.  You can take data gathered from any source and scrutinize it to find solutions that enable cost and time reductions, enable new product development, and make better business decisions.

According to Bart Baesens, the two most valuable techniques for analyzing big data are Social Network Analysis and Logistic Regression.

  • Logistic regression has been used to classify the data that makes up credit scores and response modeling.
  • Social network analysis is seen as the future of fraud detection and churn prediction. It has a much higher success rate because it analyzes social behaviors as opposed to straight numbers.

So What Benefits Can It Offer My Business?

The use of big data gives your business unlimited potential. A survey by Vetana., a research, and service firm in 2011 shows that Hadoop was being used by more than 54% of organizations in that year.By analyzing the data, businesses can yield more productive results and can counter respond to the upcoming issues as predicted by the big data models.
On implementation of the big data, the following changes can be witnessed in the business.

  • Predictive customer churn.
  • Response modeling generation.
  • Suggestive marketing tactics.
  • Loan Default Prediction.
  • More intense risk analysis.
  • Website customization.
  • Better data management than the conventional way.
  • Minimizes cyber-fraud

Big Data Benefits

As of now, the world has generated about 3.2 zettabytes of data. By 2020, that’s supposed to jump to 40 zettabytes.  Although there are several benefits of collecting the voluminous data but the worst nightmare is the management of the big data.

One of the fundamental problems while managing big data is where to put it i.e. Location.  Currently, digital data storage sites take up a land mass of 6,000 football fields. The more data a company has, the more complex the problem becomes.

Adding to that complexity is what’s referred to as ‘signal in the noise.’  When you’re dealing with such large volumes of unprocessed data, it can be hard to find the true value.

However, the biggest obstacle to big data analytics is the means to actually visualize the information.  Visualization takes data and presents it into relevant charts and reports.  To visualize the outcomes of the data analysis, a business manager/data scientist must have an understanding of the data processing techniques and subsequent models.

And The Big Solutions

Although the amount of data collected seems overwhelming, technology has been able to keep up.  For keeping up with the needs of big data, there are several low-cost options available for storing data, including cloud technology.The universal solution to big data analytics, however, is fully-trained analysts. In 2014 it was estimated that big data analysis was set to generate a total of 6 million technical and support jobs.

For this reason, trained analysts will be the single most valuable solution to the massive amounts of data headed our way.

The Human Intervention in Big Data Analytics

The effectiveness of big data analytics hugely depends on the people analyzing the large sets of data and formulating queries for the same. However, big data tools such as Hadoop helps tackling big data projects with lesser human intervention by decreasing the need for specialized hardware and software skills. In fact, there is an expectation of expansion of the Hadoop market to almost $50 billion by 2020.

Big Data Analytics makes it easy for you to understand what your data is trying to tell you. Not only does it generate absolute value for your business, but it also gives you a competitive edge.