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The brave new world of Big Data Analytics

Posted by Rahul Jain (View Profile | View All Posts) | October 31, 2014 9:17 AM

We all have heard about Big Data and much more about the hype surrounding it. Is it worth the excitement? Can Big Data live-up to its expectations or is it just another technology fad?

Every day 2.5 exabytes (2.5 billion GBs) of data is generated, and 90% of current data has been generated as recently as in last two years. [1] Data is not just growing, but it's exploding exponentially. We have witnessed data explosion trend over the past few years. The trend is certain to continue as the world embraces digitalization further. Digitalization has allowed organizations to capture/store more data and details, which was previously either infeasible or expensive.

Big Data is not just about storing all social media activities, clickstreams, public web, transaction and application data, machine logs, etc., but it has more to do with insights generation using collated data.

Big Data Analytics at work

We will discuss three examples which will help to understand the potential of this field.

During FIFA 2014 World Cup, Microsoft's Bing prediction engine predicted winner of each match even before the match started! The prediction engine was so successful that it was able to point out winner for each elimination match right till the finale, that's an impeccable record of 15 matches in a row! The prediction algorithm used various types of data fields like winning/losing margins, offensive and defensive stats, team composition, player position, game timing, playing condition like weather, and venue distance from playing country. If these exclusive insights would have been made available to one particular team, their FIFA journey would have been far more focused and successful. In near future, don't be surprised to see brands partnering up with Bing (or any other for that matter) to predict the winning team in order to make their sponsorship decisions.

Let's take a look at another example, this one is from the media and entertainment industry. Netflix started off as a content distribution company, soon they realized the value of their existing dataset in terms of customer taste. Netflix went on investing $100m into production of a TV show 'House of Cards' without even considering the pilot episode (Pilot episode is used to evaluate the performance of a new TV show in order to get go ahead with investment for full production). Netflix knew the show will be a hit even before the production started! In line with Netflix expectations, the show was well received by subscribers. About 10% subscribers began streaming the show on Day 1, and many of them ended up binge-watching. Thanks to Big Data analytics capabilities of Netflix that analysed billion hours of viewing patterns along with reviews and feedback of its 50 million subscriber base. The same technique is now used to invest in other TV shows/documentary productions. These shows being exclusive to Netflix, are fuelling up Netflix subscriptions, and also, they are making their presence felt at various award functions such as Emmy/Golden Globe Award/Oscar.

Let's take insurance sector now. If it's one of those days you think you need your car insurance more than ever may be due to bad weather or just trusting your intuitions, big data analytics can come to your rescue. Understanding the need of a customer segment, the vehicle insurance companies have come up exciting innovation of urge based insurance policies like 'Pay as you drive' and 'Pay how you drive'. The insurer will quote a premium considering various factors like driving location, traffic and weather condition, time of drive and data from on-board diagnostics. Such products are sometimes more economical than traditional products. These products are sure to gain further traction, once the cars become digitally connected.

The way forward

In above examples, we have seen organizations making bold move in order to leverage their big data capabilities. These are early days for big data, especially for big data analytics (BDA), and we have seen promise of what technology can help us achieve.  BDA in near future is sure to have a significant impact on business model of many companies. Also, we are certain to see innovative products and new revenue streams solely powered by analytics.

For any business or organization to invest in big data technology, they need to understand that BDA is not a crystal ball, at times it has its own limitation. Further, for any successful implementation of BDA project requires right blend of technology, machine learning expertise along with strong business acumen.



Thanks for the post

Big Data,Data about Data
Big data concept is very helpful to us.. But the drawback is,performance is getting slow and need a bulk of memory to store vast information.
We'll be eliminate this drawback by following some special mechanisms

Thanks for such in interesting post! The potential of Big Data is in its ability to solve business problems and provide new business opportunities. So to get the most from your Big Data investments, focus on the questions you’d love to answer for your business. This simple shift can transform your perspective, changing big data from a technological problem to a business solution.

It is an interesting post and the use cases are new to me. Thanks for making this post. The term Big Data has become synonymous with open source tools and massively parallel processing and draws the line with traditional tools. Banks and telecom giants however have been using analytics over big datasets for the last 15 years but on proprietary products. Where Big Data, in its current context, really draws the line is in the processing of unstructured content which was severely constrained earlier. Now users can hope at the prospect of mining their vast library of documents like Clinical Studies. The current trend of use cases is however geared towards cannibalising and the market is yet to see a move drive towards new opportunities.

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Finally new world analytics systems need to make the real time data accessible, comprehensible & actionable to business users with sandbox style adhoc query and data visualization tools.

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