The Cube - Hadoop Summit 2012 - Andre Dragomir, with Jeff Kelly and Abhi Mehta
The definition of insanity, so said Albert Einstein, is doing the same thing over and over again and expecting different results. That applies to life, love and, yes, Big Data Analytics.
Think about it. If you put a Toyota Camry engine in a Ferrari and expected it to perform like a Ferrari should, you'd be sorely disappointed. You'd be insane, however, if you tried the car again and again, day after day, expecting one day to hit zero-to-sixty in six seconds.
But that's just what Michele Chambers sees customers attempting in pursuit of Big Data Analytics. Chambers, GM and VP of IBM's analytics solutions division, said many of her customers buy into the promise of Big Data and predictive analytics, but want to achieve it with the same old business intelligence and database tools they're comfortable with.
"Typically what [customers] do is they go back and lean on what they already know," said Chambers in an appearance on theCUBE at Hadoop Summit 2012 (video at bottom of post.) "They want to use their existing infrastructure, they want to use their existing data, they want to use their existing tools. They don't want to do anything different. And I say if you don't do anything different, you're not going to get any different results."
New Approaches, Technologies and Tools Required
Chambers is exactly right, on all three fronts, and here's why:
Infrastructure. New methods of processing and storing large, multi-structured data sets are emerging precisely because traditional relational technology cannot do the job in a time- or cost-effective way. Hadoop, for example, allows you to store and process Big Data at scale in a reasonable timeframe on cheap commodity boxes running open source software. Now try doing that with Oracle. I'll check back with you $3 million and six months from now.
Data. Big Data is about enriching your existing internal transactional data with additional data from diverse sources, some of those sources from outside of your enterprise. That could mean social media data from Twitter or Facebook, public sector data from the National Weather Service or Department of Education, or market data from Bloomberg or Dow Jones. If you're not mashing up data, you're probably not doing Big Data Analytics.
Tools. Because they must operate on new, larger, more diverse data volumes on top of parallel computing infrastructure, most traditional business intelligence tools aren't going to cut it either. What you need are modern data visualization and analytic platforms that allow users to easily manipulate and visualize Big Data. To be fair, a handful of existing BI vendors like Tableau and Microstrategy are working hard to allow their products to better integrate with Big Data. But by and large, that old reporting tool you've been using for the last decade or so isn't going to be enough to deliver actionable insights from Big Data.
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Andre Dragomir | Hadoop Summit 2012
The Cube - Hadoop Summit 2012 - Andre Dragomir, with Jeff Kelly and Abhi Mehta
The definition of insanity, so said Albert Einstein, is doing the same thing over and over again and expecting different results. That applies to life, love and, yes, Big Data Analytics.
Think about it. If you put a Toyota Camry engine in a Ferrari and expected it to perform like a Ferrari should, you'd be sorely disappointed. You'd be insane, however, if you tried the car again and again, day after day, expecting one day to hit zero-to-sixty in six seconds.
But that's just what Michele Chambers sees customers attempting in pursuit of Big Data Analytics. Chambers, GM and VP of IBM's analytics solutions division, said many of her customers buy into the promise of Big Data and predictive analytics, but want to achieve it with the same old business intelligence and database tools they're comfortable with.
"Typically what [customers] do is they go back and lean on what they already know," said Chambers in an appearance on theCUBE at Hadoop Summit 2012 (video at bottom of post.) "They want to use their existing infrastructure, they want to use their existing data, they want to use their existing tools. They don't want to do anything different. And I say if you don't do anything different, you're not going to get any different results."
New Approaches, Technologies and Tools Required
Chambers is exactly right, on all three fronts, and here's why:
Infrastructure. New methods of processing and storing large, multi-structured data sets are emerging precisely because traditional relational technology cannot do the job in a time- or cost-effective way. Hadoop, for example, allows you to store and process Big Data at scale in a reasonable timeframe on cheap commodity boxes running open source software. Now try doing that with Oracle. I'll check back with you $3 million and six months from now.
Data. Big Data is about enriching your existing internal transactional data with additional data from diverse sources, some of those sources from outside of your enterprise. That could mean social media data from Twitter or Facebook, public sector data from the National Weather Service or Department of Education, or market data from Bloomberg or Dow Jones. If you're not mashing up data, you're probably not doing Big Data Analytics.
Tools. Because they must operate on new, larger, more diverse data volumes on top of parallel computing infrastructure, most traditional business intelligence tools aren't going to cut it either. What you need are modern data visualization and analytic platforms that allow users to easily manipulate and visualize Big Data. To be fair, a handful of existing BI vendors like Tableau and Microstrategy are working hard to allow their products to better integrate with Big Data. But by and large, that old reporting tool you've been using for the last decade or so isn't going to be enough to deliver actionable insights from Big Data.
