Lots of data does not necessarily equate to “Big Data." To my way of thinking, the single most important capability to implement in any large scale data platform that is going to support sophisticated analytics is the ability to quickly construct, high quality random samples.[read more]
Without the right information, an organization cannot make the right decisions or take the right actions. Yet I see organizations today focusing on investments in big data because they believe it can effortlessly bring analysts insights. That premise is incorrect.[read more]
Platfora has gained a lot of buzz in the Big Data analytics market primarily through word of mouth. Late last year the company took the covers off of some impressive and potentially disruptive technology that takes aim at the broad BI and business analytics ecosystem.[read more]
SnapLogic, a provider of data integration in the cloud, last week announced Big Data-as-a-Service to address businesses’ needs to integrate and process data across Hadoop big data environments. I look forward to seeing SnapLogic’s 2013 technology advancements.[read more]
Many organizations are on “Big Data Holy Grail” quests, looking to deliver game-changing business analytics, only to find themselves in a “boil-the-ocean” Big Data project that “has no real value.” They shouldn't be rushing into hasty Hadoop implementations.[read more]
Big data involves interplay between different data management approaches and business intelligence and operational systems. Consider big data integration as part of your business case and project, because it is essential to gaining the most value from your big data investments.[read more]
Just few weeks ago, the term "Big Data" was the darling of every investor. But then, at Gartner’s Business Intelligence Summit in Barcelona, things changed. Svetlana Sicular, a Gartner analyst, exposed what many had feared: Big Data has officially landed in Gartner’s “Trough of Disillusionment.”[read more]
By 2015, 65% of packaged analytic applications with advanced analytics will come embedded with Hadoop. Organizations realize the strength that Hadoop-powered analysis brings to big data programs, particularly for analyzing poorly structured data, text, behavior analysis and time-based queries.[read more]
In my article, “Data Integration Roadmap to Support Big Data and Analytics,” I detailed a five step process to transition traditional ETL infrastructure to support the future demands on data integration services. It is always helpful if we have an insight into the end state for any journey.[read more]
Many so-called Big Data projects have more to do with more traditional data types, i.e. relationally structured, but bigger or requiring faster access. And in these instances, the need is for Big Analytics, rather than Big Data. The value comes from what you do with it, not how big it happens to be.[read more]
As part of the Energy Department’s SunShot Initiative, the Department today announced seven data-driven projects to unearth new opportunities for reducing costs and accelerating solar energy deployment in the United States.[read more]
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