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6/27/2012
09:36 AM
Doug Henschen
Doug Henschen
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Big Data Analytics Power Revolution In Decision-Making

InformationWeek virtual event explores enterprise steps toward deriving valuable insights amid unprecedented data volume, velocity, variety, and complexity.

You've heard the highfalutin forecasts about how many zettabytes of information the world will create within the next two years, but just what is your organization supposed to do to take advantage of big data?

Maybe you've gone a level deeper and learned that the big-data challenge is as much about the variety, complexity, and velocity of data now encountered as it is about sheer scale. But what's next? How does big data fit into your enterprise? What platforms should you consider and how do they fit in with your existing enterprise infrastructure and tools? And what about data quality, a nettlesome challenge that persists even within comparatively small relational databases?

On June 28 at 10 a.m. ET, InformationWeek will launch Big Data Analytics: Deriving Business Value From Massive Data Sets, a virtual event packed with Webinars and content aimed at answering these and other important questions.

I'm kicking off the event with an opening keynote that looks at the big data opportunity, platform choices, and how analytics are being employed to make better business decisions. I'll be joined by Ovum analyst Tony Baer, who will explore the data-management and data-quality challenges enterprises are facing as they tap new data sources and manage information at an unprecedented scale.

Join InformationWeek Reports director Art Wittmann and expert Michael Biddick at 12:30 p.m. ET as they discuss big-data hardware and architectural considerations. Biddick authored our recent report on The Big Data Management Challenge. In this Webinar Biddick will examine open-source and commercial platform alternatives, and he'll also discuss on-premises versus cloud deployment options.

There's no better way to start thinking about big data possibilities than to hear about what first movers are doing. With this in mind, I've invited practitioners Opower, 5ivepoints, and General Electric to share the details of their big data deployments. Drew Hylbert, Opower's systems engineer, will explain Opower's use of Hadoop to do comparative analyses of smart meter data for millions of consumer and commercial utility customers.

5ivepoints has developed a mobile political campaign app that lets grassroots supporters gather and make use of information from iPhones and Android devices. Data on voter opinions is captured on these devices and uploaded to a centralized server that uses a NoSQL database to correlate new information with data on more than a 100 million U.S. voters. Analysis illuminates what issues matter to which voters, and the app can then be used to canvass constituents and turn out the right votes on election day. It's a timely case example in an election year, but the app also has commercial application parallels. 5ivepoints' director of analytics, Daniel Weitzenfeld, will explain.

At General Electric, complex and high-volume sensor data has been used for years to monitor and test industrial equipment such as turbines, jet engines, and locomotives. We'll hear from Brian Courtney, general manager, operations data management at GE, who will discuss how big data is being used to predict product performance and maintenance needs and avoid unplanned downtime. He'll also explain how GE is helping other manufacturers with big-data software and data-management capabilities.

The examples you'll hear from Opower, 5ivepoints, and GE are typical of the kind of breakthrough applications that are possible in the era of big data. There's much more to Thursday's event, including a trove of articles, reports and whitepapers, so register now. And don't worry about missing any of the presentations; all of the Webinars will be available on demand soon after the event, so you'll be able to watch and download reports and white papers on your own schedule. It's a must-attend for anybody hoping to get to better, big-data-driven business decisions.

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