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Summary:

“Anticipation denotes intelligence.” Zubin Dowlaty, VP and head of innovation and development of analytics-outsourcing firm Mu Sigma, said at Structure:Data that’s what companies need to be striving for and in this era of big data, the barriers to achieving that have fallen away.

Zubin Dowlaty of Mu Sigma at Structure:Data 2012

Zubin Dowlaty was watching the movie the Fifth Element when he saw the future of real-time intelligence laid out in a pithy quote: “Anticipation denotes intelligence.” Dowlaty, vice president and head of innovation and development of analytics-outsourcing firm Mu Sigma, said that’s what companies need to be striving for and in this era of big data, the barriers to achieving that have fallen away.

Zubin Dowlaty of Mu Sigma at Structure:Data 2012

(c) 2012 Pinar Ozger. pinar@pinarozger.com

Speaking at GigaOM’s Structure: Data conference, Dowlaty gave an overview of how companies can stitch together their own real-time intelligent systems that can handle a growing stream of big data. He said companies first need to get into the right mindset and that means worrying more about consumption of analytics and less about the models and technology used.

He said the bigger goal is to help companies create predictive intelligent systems that can handle real-time data and shrink the amount of time it takes to act on events. He said by observing the companies that are successfully building these systems, often high-frequency traders, the necessary components come down to: messaging-oriented middleware; an advanced analytics engine; a business process modeling system; and a rules engine. Assembling these parts is becoming easier now, but companies still need to make the move.

“The biggest mistake is not doing anything,” Dowlaty told me after his talk. “Real time is hard but it’s not that hard. Big data is removing some of the excuses from a computational perspective. The technology exists, we just need to stitch it together and we’re off to the races.”

He said if pursued, this kind of intelligent systems can be applied to fraud detection, exception reporting, offer optimization, quantitive trading and sensor fusion. With the expected onslaught of sensor data coming from smart devices, there’s going be even more data to handle. But if you can anticipate what’s coming next, you’re on your way to becoming intelligent.

“It’s about man plus machine, heuristic and algorithmic coming together to operate analytics on a much quick scale,” he said.

Watch the livestream of Structure:Data here.

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  1. That’s great, but I don’t understand the connection to The Fifth Element quote at all.

  2. Real-time sounds nice, but minus some time sensitive activities like financials, the majority of business decisionmaking processes will never be ready to utilize data that quickly. Not to say this isn’t a good goal but considering the costs to implement/maintain, too much of a solution looking for a problem.

    1. dicelikethunder K Friday, March 23, 2012

      Huge oppertunity in fraud prevention, marketing / sales, retention, customer experience, and CRM.

      Real time is more then about fast trading. It’s about self adapting models reacting to customer actions and transactional data.

  3. Ryan, I agree with the growing need for companies to acquire intelligent systems to analyze their data. The new machine learning solution from the HPCC Systems platform is worth checking out. Built on top of linear algebra distributed data framework, it supports both supervised and unsupervised learning algorithms including Linear/Logistic Regression, Naïve Bayes and Kmeans to assist with predictive analytics tasks. For more info visit http://hpccsystems.com

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