Big Data "breaks" e-warehouse: Data management challenges caused by high volume, velocity and variety of unstructured information

WARC, 28 March 2014

PHILADELPHIA: The volume of data being generated is accelerating and is "breaking the electronic data warehouse" a leading industry figure has said.

Gary Survis, COO at Synscort Data Integration and lecturer at Wharton, University of Pennsylvania, noted that 90% of all of data had been created within the last two years and that traditional relational databases were unable to handle the kind of data now available.

But, in an interview with Knowledge@Wharton, he was emphatic that Big Data did not simply mean "a lot of data". His definition encompassed the three Vs – volume, velocity and variety – with data arriving quickly and frequently in an unstructured format which was the source of the stresses on existing systems.

But volume of data could also be translated into new insights. He pointed to the example of the data captured by on-board computers for one jet engine on one plane on one flight and multiplied that by the number of daily flights, then by the number of planes in the air every day.

"Imagine how all of a sudden that you can start to get insights on how to make that plane run more efficiently," he said, with a consequent impact on the bottom line.

It was still early days for Big Data, however, and he expected that as technology advanced "you are going to start seeing it in every part of your life".

Marketers on a panel at ad:tech San Francisco 2014 discussing 'The myth of Big Data' offered an alternative view, ranging from its role in making mcommerce more efficient to a source of stories to engage consumers, Luxury Daily reported.

Joe McCambley, founder and creative director of digital design agency The Wonderfactory, said the goals of Big Data included determining what people had done in the past, what they needed now and what they might want in the future.

While the first was easily achievable, the second two were more problematic, although Big Data could help, for example, with testing permutations of a campaign to improve content.

Data sourced from Knowledge@Wharton; Luxury Daily; additional content by Warc staff

For more on theory and case studies on: http://expertresearchers.blogspot.com