Big Data Analytics 2014

It’s nearly impossible to write a 2014 forecast on e-commerce without mentioning “big data”. While the term might be overused, its importance to e-commerce cannot be overstated.

Big Data and Analytics will evolve beyond segmentation for email lists. E-commerce merchants will collect and analyze data to discern shopping patterns that have predictive value and to understand consumer experiences in digital and physical contexts. Their users benefit from the digitally enhanced experience, and the data they create also provides usage insights that inform product design and e-commerce strategy.

The underlying tools for the management of this data will only grow smarter, faster and more affordable as companies catch up with an overabundance of data. Big Data Analytics should emerge in 2014 as not just a buzzy trend, but a core business practice that e-commerce firms use to understand their business and their customers in fundamentally new ways.

Big Data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. This data is generated by any activity like page visit, login, transaction a customer does when he/she comes across your business. This data is gone already beyond our imaginations and the rate at which it’s growing will be huge challenge to handle.

Taming Big Data:

Big Data includes data sets whose size and type make them impractical to process and analyze traditional database technologies. The amount of data being generated is almost impossible to handle, therefore we need tools those can condense the data and intelligently present only what is relevant and contextual.

If we look at the numbers at which data is being generated, 2.7 Zetabytes of data exist in the digital universe today. Facebook stores, accesses, and analyzes 30+ Petabytes of user generated data. The rapid growth of unstructured data like YouTube users uploading 48 hours of new video every minute, roughly 175 million tweets every day on Twitter etc. may lead us to many questions about managing the big data and analyzing it.

According to the reports, Poor data across businesses and the government costs the U.S. economy $3.1 trillion dollars a year. Survey also reveals that poor data management or lack of data quality cited as main reason for overrunning project costs.

Key Facts about Big Data:

Despite industry hype, most organizations are yet to develop, implement or execute a Big Data strategy. The survey found that 12 percent of organizations are currently implementing or executing a big data strategy while 40% of them are still considering / exploring Big Data.

 

Fig 1 (Source – sas.com): Which of the following best describes your organization’s stage in using external big data to help make business decisions?

Capturing and Storing Is Only the Beginning

It’s true that big data has great potential for creating a more detailed model of the business, such as tracing a customer’s path through a store and analyzing post-sale sentiments expressed in social media to create better offers. But to date, there have been lots of talk about how it can be stored and captured and very little about the practical ways businesses can exploit it.

But storing and capturing big data won’t make it valuable. Additional tools are needed to explore and analyze it. As it turns out, the simplicity of Hadoop stops with capturing data. The real promise of big data is that it contains information that isn’t part of the typical well-structured view of the business world. It’s all about “not knowing what you don’t know.”

Making Big Data Relevant for Business Orders:

Measuring consumer sentiment, optimizing supply chains, detecting fraud – Big Data is powerful. But to harness that power, organizations must hire data scientists, craft complex algorithms, and make massive investments in infrastructure and software. That leaves business leaders and the IT professionals supporting them wondering: Is it possible to make Big Data useful for business users?

Data’s value can be unleashed for business users by condensing it and intelligently presenting only what is relevant and contextual to the problem at hand. For example, an executive might be interested in summary data across the company’s product lines, while a manager of a specific product or geography might need more detail, but only for the areas that he or she oversees.

More analytics, fewer Gut feelings:

Ecommerce companies will grow increasingly, focusing on data and willing to apply analytics-derived insights to key business operations. Intuitive decision-making will diminish somewhat as companies infuse analytics into everything that customer touch. Data analytics will be the driver for capturing more customers, upselling to existing customers and retaining them for the long term.

The need for automated tools will become increasingly critical.

It seems that the more data we have, the more we want“. But as data volumes increase, the need for pattern matching, simulation, and predictive analytics technologies become more crucial. Engines that can automatically sift through the growing mass of data, identify issues or opportunities, and even take automated action to capitalize on those findings will be a necessity.

An often overlooked benefit is an increase in the volume of analysis. After the process is automated, employees find out how easy it is to analyze the data. So they start doing it more frequently. Where before it was only done when absolutely necessary (if at all), now it becomes a routine part of their Jobs.

Offer Personalization and Customization:

Challenge your business to finally begin offering personalization and customization to both onsite and marketing creative. Work to apply analytics & segment marketing campaigns so that they address customers by name and with relevant products and offers that are based on an individual’s or group of shoppers’ stated preferences or on-site behavior.

Taking on this challenge means that the retailer’s marketing department will need to collect meaningful information about what interest shoppers and organize separate, custom campaigns around those interests.

Personalization and customization could be a significant competitive advantage in 2014.

Summary

Big data made substantial progress in 2013, especially with respect to business stakeholder engagement in the topic. But we have yet to drive widespread adoption of big data, especially from a business transformation perspective. 2014 seems like the right time to make that happen.

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