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Collecting reams of big data is one thing; doing something useful with all that information is another. But the former without the latter won't win business intelligence, analytics and IT teams any plaudits from corporate executives. As Gartner Inc. analyst Doug Laney put it at the consulting company's 2013 Business Intelligence and Analytics Summit in Grapevine, Texas, successful big data projects depend on companies "recognizing that there are opportunities to really innovate with this information" -- and then taking the required steps to capitalize on those opportunities.
That's where big data analytics applications come in. Finding the business value hidden in hoards of big data can be a tough nut to crack -- but there's a growing body of examples that can help organizations get cracking. And more businesses are getting going. At the 2013 Pacific Northwest BI Summit, a gathering of about 20 IT vendor execs and industry consultants held in Grants Pass, Ore., Claudia Imhoff, president of consultancy Intelligent Solutions Inc., said she was seeing companies "starting to move more to the proactive side than the reactive side" on big data analytics. There's a compelling reason to do so, Imhoff noted: "As much as it's hyped, big data does open doors to things we couldn't do five years ago, or even two years ago."
SearchBusinessAnalytics has published a variety of articles and multimedia items that can help prepare your organization to walk through those doors. We've compiled many of them in a comprehensive guide to big data analytics tools and project management best practices. Separately, in a pair of articles, consultant Lyndsay Wise outlines key factors to consider in planning a big data analytics architecture and offers advice on managing successful projects; meanwhile, consultant Rick Sherman details big data analytics worst practices that you'll want to avoid. Wayne Eckerson, TechTarget's BI and analytics industry analyst, discusses big data analytics basics in one video interview and assesses the potential benefits of using Hadoop systems in another. David Loshin, another consultant, weighs in on the upgraded analytical capabilities of Hadoop 2. In addition, we catalog tips on building analytical models with big data and look at the potential big data punch of in-memory analytics software. That's just a sample of our editorial content on big data analytics applications and projects -- and our doors are always open to you.