Sep 17, 2013

Big Data: As Likely to Confuse As to Enlighten

Author: Alexandra Stanculescu

With the question “What is Big Data?” still lingering in the air, the market is booming, being predicted to reach more than $50 billion by 2017. However, more than 55 % of big data projects are doomed to fail.

What exactly is pushing Big Data forward?

The turning point we’re experiencing is connected to a shift in perspective: all eyes are now pointed to the concept of predictive organizations. This means the industry is taking a step further from the importance of the raw size of data, and a step towards the innovative ways of leveraging it. We’ve reached some kind of a peak, upon realizing that we must unlock the value of the increasing volume of data piling-up every day. And since we’re living in the Cloud era, there’s no need for companies to build their own hardware any more.

Than what Is so Challenging about Big Data?

Big Data is a buzz word that causes confusion when it comes to an actual, practical plan. Basically, you need to adjust your expectations: you need to learn how to successfully fail, when it comes to big data projects.

As Ron Bodkin highlights in his “The big data Wild West: The good, the bad and the ugly” article, many enterprises are only exploring Big Data from a cost containment or storage scalability point of view. They may be looking at “agile analytics” to help processes and work with data. Many times, they are missing opportunities to improve their business and provide better service to customers and the chance to develop new products based on data, rather than just intuition. They are reaching Big Data plateaus — achieving the ability to store data, without extracting additional value.

What shouldn’t ever be overlooked with Big Data Projects?

Speed. Speed is the key to success in the early stages of Big Data implementation. The faster you can complete projects and build organizational expertise in using data in this new way, the sooner you can create value and move to a more sophisticated stage of adoption.

Test & Learn. Companies have to create many small “failures” by developing hypotheses and examining them against the data. This allows enterprises to develop a truly coherent strategic approach grounded in data.

Insight. Businesses can develop new products from their insights, creating new revenue streams and even transforming their culture to be data-driven. They must approach scalability and cost containment, develop agile analytics and insights, and optimize their business with automated predictive analytics at scale. This is when big data capabilities gain the power of transforming your business.

Have you thought about implementing such a project in your company? Do you think the potential benefits outweigh the risks? We’d like to hear your opinion, so please feel free to share your ideas with us in a comment below, or join us for a more private e-chat.

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