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Match in-memory processing speed to big data analytics business need

Michael Minelli, co-author of a book on big data analytics, says in-memory analytics tools must be tied to business processes to pay off for users.

Michael Minelli is vice president of sales and global alliances for the information services division of MasterCard Advisors, the professional services and data analytics arm of MasterCard International. He's also one of the three co-authors of Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses, a book that aims to explain the big data phenomenon to both IT and business readers.

Minelli, who worked at software vendors Revolution Analytics and SAS Institute Inc. before joining MasterCard, has been involved in big data projects at organizations such as Time Inc., Cablevision, Foxwoods, Major League Baseball, Standard & Poor's and Sony. In an interview with, Minelli discussed big data analytics applications and the role that in-memory analytics technology can play in them. One of his pieces of advices: The faster performance supported by in-memory processing won't provide the hoped-for business benefits unless the analytical results are fed into real decision-making processes. Excerpts from the interview follow.

What's the key message in your book?

Michael Minelli: The book's main theme is that big data analytics are a game changer for the industry, whether you're in IT or the business. Big data is going to make a big impact, and this is going to continue over time. The message is to think about how to do things differently: "If we can do things faster, then what does that mean for the business?" Big data allows us to innovate and make decisions quickly while transforming the way we do business intelligence. People have been talking about building one version of the truth for a while -- that's the whole genesis of the data warehouse movement. The name of the game was who could build the most valuable repository to make better decisions. What's changed is that it's not all about what happens in your world, but what happens in other companies and even in other industries. It's about going from having an insular mindset around data to having an abundance mentality for leveraging data.

Can you give a couple of real-world examples of the opportunities for taking advantage of big data analytics?

Minelli: Online up-selling and cross-selling on the fly before a customer's attention fades. Mining blogs and customer service notes to perform customer sentiment analysis, good and bad. Providing secure e-commerce transactions with built-in anti-fraud controls. Deploying marketing automation that delivers real ROI by enabling actionable insights into customer buying habits. That's how competitive organizations are using big data analytics today.

What specific role can in-memory analytics technology play in turning big data into a competitive advantage?

Minelli: It's all about pure speed -- taking advantage of the hardware and RAM capabilities that have become cheaper to create queries on the fly. Doing so removes some of the barriers between IT and the business so that there's more agility for people to do on-the-fly business intelligence and predictive analytics and to move beyond traditional sampling techniques if they don't have to wait 24 to 48 hours for results.

Everyone talks about faster access to data leading to better insights, but the other critical part is connecting that insight to an actual operation.

When is in-memory processing for big data analytics not the right fit or more trouble than it's worth?

Minelli: It's the notion of fit to purpose. The main thrust is, do you really need the additional speed and do you have the types of users and processes in place so that if empowered with this information, they could really do something with it to impact the business? Everyone talks about faster access to data leading to better insights, but the other critical part is connecting that insight to an actual operation. So it's not just viewing a report, but actually using that report to make a decision on the fly to trigger an event like addressing a fraudulent transaction or initiating a cross-sell opportunity. Having faster analytics is great, but [the results] have to be able to make their way into the decision process.

So how can organizations get to the level where they truly can take advantage of in-memory analytics in combination with big data?

Minelli: For starters, IT should work with the analysts to assess the low-hanging fruit where there are some productivity gains [to be had]. A good example is reducing a data mining process from 24 hours to a matter of minutes so that a data scientist can be more responsive to the rapid changes in today's businesses. The next step would be for the business to develop some use cases where speed can make a difference and then give it a try. The technology part isn't a no-brainer, but it's not quantum physics, either. From my perspective, the major challenge is finding and hiring the right talent and then managing that talent to achieve specific business objectives in a reasonable time frame.

Beth Stackpole is a freelance writer who has been covering the intersection of technology and business for more than 25 years.

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