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Big Data: No Longer the Marketers Dilemma

Yves de Montcheuil | Oct 19, 2012 37 views No Comments

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All organizations – no matter their size – always have and always will collect vast amounts of data. Of course, it makes sense to store it; companies need for regulations and audit requirements, and collecting the data hasn’t ever really been the issue. There are storage clouds and inexpensive disk space to hold that big data that have been around forever. But how do companies make sense of this big data, and specifically, how can marketers leverage big data?

If we were all coders, this would be an easy issue to solve, but most marketers can’t write code. Still, today’s marketers are inundated with complex data that has the potential to lead to a better understanding of customer demand, behavior and preferences as well as stronger customer connections, given the technology to manage and interpret the data effectively. There must be tools to use big data effectively.

Leveraging of big data for marketing can be real-time, on the fly behavioral analysis for cross promotions, or batch analysis and segmentation for targeting and nurturing. In the first case – real-time – the goal is to influence consumer behavior at the point of sale or on the site. For years, large organizations such as Amazon or eBay have used recommendation engines to match and recommend products, people and advertisements to users based on analysis of their profile and behavioral data.



In the second case – batch segmentation – massive amounts of data are analyzed, which that was just not possible or practical with traditional systems. Organizations are now able to better identify a target audience and identify the right person for the right offerings. Big data allows marketing teams to evaluate large volumes from new data sources, like click-stream data and call detail records, to increase the accuracy of analysis. Indeed, the more information made available to a marketer the more granular targets can be identified and messaged – but the traditional issue was that too much data killed data. No longer!

So how does a marketer get to this point and go about leveraging? Aside from the simple option of finding a developer to do it, marketers thankfully today have a new generation of tools in the big data ecosystem, and especially around the Hadoop paltform. Similarly to what happened with relational databases 15 years ago, big data integration tools provide a level of abstraction to design these big data tasks – with everything happening through a drag-and-drop user interface.

It’s then up to the big data integration tool to generate the appropriate code to execute these tasks. The marketer doesn’t need to turn into a developer and write Hadoop MapReduce programs – the tool does it for him or her. MapReduce code is generated and executed behind the scenes, isolating the user from the technical complexity of Hadoop.

All the tasks required can be designed graphically, which brings to the marketer the agility that code writing would have denied him. It is very easy to adjust a few parameters and to try again. When learning and refining the processing in real-time, this iterative mode is priceless.

And the icing on the cake: big data quality. Marketers should take big data one step further by ensuring the reliability and trustworthiness of the data. Because no matter how big the data – if the data is bad, it will just be big bad data.

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