How to overcome Big Data’s main stumbling block

The stumbling block for many companies and the reason why organizations fall behind in the planning and pre-planning stages of big data, appears to be confusion on how best to make big data work for the company and pay off competitively.

With all the talk about rapid deployment and breakneck business change, there can be a tendency to assume that businesses are up and running with new technologies as soon as these technologies emerge from proof of concept and enter a mature and commercialized state. However, the realities of where companies are don’t always reflect this.

Take virtualization. It has been on the scene for over a decade-yet recent research by 451 Research shows that only 51 percent of servers in enterprise data centers around the world are virtualized. Other recent survey data collected by DataCore shows that 80 percent of companies are not using cloud storage, although cloud concepts have also been with us for a number of years.

This situation is no different for big data, as reflected in a Big Data Work Study conducted by IBM’s Institute of Business Value. The study revealed that while 33 percent of large enterprises and 28 percent of mid-sized businesses have big data pilot projects under way, 49 percent of large enterprises and 48 percent of mid-sized businesses are still in big data planning stages, and another 18 percent of large enterprises and 38 percent of mid-sized businesses haven’t yet started big data initiatives.

The good news is that the study also showed that of those organizations actively using big data analytics in their businesses, 63 percent said that the use of information and analytics, including big data, is creating a competitive advantage for their organization–up from 37 percent just two years earlier.

The stumbling block for many and the reason why organizations fall behind in the planning and pre-planning stages of big data, appears to be confusion on how best to make big data work for the company and pay off competitively.

Big data projects need to demonstrate value quickly and be tightly linked to bottom line concerns of the business if big data is to cement itself as a long-term business strategy.

In far too many cases when people plan to build out a complete system and architecture before using a single insight or building even one predictive model to accelerate revenue growth. Everyone anticipates the day when Big Data can become a factory spitting out models that finally divulge all manner of secrets, insights, and profits.

So how do you jump start your big data efforts?

Find big data champions in the end business and business cases that are tightly constructed and offer opportunities where analytics can be quickly put to use.

When Yarra Trams of Melbourne Australia wanted to reduce the amount of repair time in the field for train tracks, it placed Internet sensors over physical track and polled signals from these devices into an analytics program that could assess which areas of track had the most wear, and likely would be in need of repair soon. The program reduced mean time to repair (MTTR) for service crews because it was able to preempt problems from occurring in the first place. Worn track could now be repaired or replaced before it ever became a problem-resulting in better service (and higher satisfaction) for consumers.

Define big data use cases that can either build revenue or contribute to the bottom line.

Santam, the largest short-term insurance provider in South Africa, used big data and advanced analytics to collect data about incoming claims, automatically assessing each one against different factors to help identify patterns of fraud to save millions in fraudulent insurance payments.

Focus on customers

There already is a body of mature big data applications that surround the online customer experience. Companies (especially if they are in retail) can take advantage of this if they team with a strong systems integrator or a big data products purveyor with experience in this area.

Walmart and Amazon analyze customer buying and Web browsing patterns for help in predicting sales volumes, managing inventory and determining pricing.