Big Data: Implications for Open Source and Proprietary Tools
April 21, 2012
During a Web cast in the OpenWorld Tokyo this month, Oracle President Mark Hurd zeroed in on the developments his company has made in the area of analytics. The overall theme of the presentation appears in “Oracle’s Mark Hurd Spells Out Analytics Vision”.
Hurd framed his remarks around the perils and promise held in ever-increasing amounts of digital information. “The amount of data on the planet is just huge,” he said. “I have bad news. It’s going to get worse.” He added:
The true question is how to get the right information to the right person at the right time to make the right decision. This is hard.
Come to think of it, all of the other major players in analytics – Microsoft, IBM, and SAP – talk about it in a similar light. The gist is that they’re making Big Data analytics technology available to businesses so that they can delve into both structured and unstructured data to unearth actionable knowledge. That is, minus the risks traditionally associated with it.
Included in the updates that Hurd announced was the upgrade to the Hyperion Enterprise Performance Management (EPM), that is, version 18.104.22.168. This new version has modules for account reconciliation and financial planning, support for Exalytics, and enhanced user experience, among others. Oracle also announced the release of Endeca Information Discovery, which is a system that’s capable of combining both unstructured and structured data sans modeling.
However, Oracle isn’t the only analytics player that is continuously expanding its feature set. SAP recently launched ActiveEmbedded. But several open source analytics players are going strong. Examples of these are Ikanow and Revolution Analytics.
So what does this mean for proprietary solutions?
Enterprises continue to struggle with the amount of data that they have to manage as that amount skyrockets into the petabyte stage. Hence, they also have to upgrade their infrastructure which means bigger costs on top of the license fees of proprietary tools. Open source analytics, aside from being free, allows businesses to create their own custom-fit analytics solution.
However, I believe that that while open-source analytics will eventually be more widely used, proprietary technologies will remain viable and over time, we’ll see a blend of both being used by companies to handle big data.
Lauren Llamanzares, April 24, 2012
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