Machine Learning

Big Data Discovery Needs Smart Machines

Every business today is a Big Data business, whether they realize it or not. Businesses have access to a wealth of data originating from disparate sources, proprietary and 3rd party. This offers a unique opportunity to businesses today to mine these rich repositories of data together to gain insights and intelligence that could make profound impact to business growth.

But there are several challenges that make most businesses shy away from leveraging Big Data. The most prominent of them are the technology and usability challenges. Big data technologies, like Hadoop, have complex learning curve even for the IT teams. And these take analysis further away from business users and even analysts, of course with a reason – the power of these systems lies in their ability to write elaborate code and analyze data in an unbridled manner, unlike the restrictive relational database driven data warehouses of the previous generations. Hadoop provides an easy storage for different data, as it requires no fixed schema setup or complex maintenance and can scale-out easily. However it comes with cost as these only offer batch queries making real-time responses to ad-hoc queries difficult. Also because these are not natively relational systems, doing relational analysis across different types of data, which otherwise was possible in traditional data warehouses becomes difficult.

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