Abhi Mehta & Michelle Chambers Part 1 | Hadoop Summit 2012
Channel: SiliconANGLE theCUBE
Date: 2012-06-13

The Cube - Hadoop Summit 2012 - Abhi Mehta, Treseta & Michelle Chambers, IBM Netezza, Part 1 with John Furrier
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."
But I understand that change is hard, even more so inside sometimes risk-averse IT departments. But we're at a crossroads. Big Data isn't just a passing fad or a marginally better way to do business intelligence. It's a completely new paradigm that requires a major shift in thinking. In other words, "You've got to take some additional risk," as Chambers puts it, to achieve Big Data success.
That means, she said, "You've got to infuse your applications, you've got to infuse your data with net new information if you want to have additional insights." That means you've got to invest in new infrastructure technologies such as Hadoop and other platforms to form a new foundation for Big Data Analytics. And you've got to take a chance on new end-user tools that can turn all that Big Data into easy-to-understand insights.

The good news is you don't have to rip-and-replace your entire existing infrastructure and tools set. In fact, I would strongly advise against that. What you're using now for business intelligence and data warehousing is likely (hopefully) there for a reason, that being because they are delivering value to the business. And in fact, many Big Data technologies can actually help you derive more value from existing databases and tools.

When it comes to Big Data, start small. Identify a specific business problem that you want to solve, one that if fixed would result in tangible benefits to the business. Talk to peers that have already jumped headfirst into Big Data to learn from their experiences. Educate yourself. Read everything and anything Big Data related you can get your hands on.
And then you have to take a risk. Evaluate and invest in some new Big Data technologies and services that will allow you to tackle your chosen business problem and attack it with abandon. And don't be scared. Change is hard, but the rewards are Big. Big Data Big.
IBM Stepping Up Its Game
On a side note, I want to give credit to IBM for stepping up its Big Data game. A few months back I criticized Big Blue for not providing enough leadership in the Big Data space. Specifically, I said the company needed to talk less about its myriad analytics and database products and talk more about their vision of what Big Data could be for the enterprise and how we're all going to get there. And that's precisely what it's done, Michele Chambers being but one good example. Let's hope IBM keeps it up.