Andrew Brust Revolution or Evolution Dan Kusnetzky Best Argument: Revolution List of Events: Opening Statements Rebuttal Closing Statements The moderator has delivered a final verdict. Opening Statements Don’t be afraid Andrew Brust: Big Data is unmistakably revolutionary. For the first time in the technology world, we’re thinking about how to collect more data and analyze it, instead of how to reduce data and archive what’s left. We’re no longer intimidated by data volumes; now we seek out extra data to help us gain even further insight into our businesses, our governments, and our society. The advent of distributed processing over clusters of commodity servers and disks is a big part of what’s driving this, but so too is the low and falling price of storage. While the technology, and indeed the need, to collect, process and analyze Big Data, has been with us for quite some time, doing so hasn’t … [Read more...] about Big Data: Revolution or evolution?
Machine learning vs big data
For IBM, a world leader in AI, as their Watson project has demonstrated, applying intelligence to storage is a natural. We're facing a data onslaught like never before. We'll be generating more data than we have capacity to store once IoT gets rolling. special feature The Evolution of Enterprise Storage How to plan, manage, and optimize enterprise storage to keep up with the data deluge. Read More Just as any software problem can be solved by adding a layer of indirection, any analytics problem can be solved by adding a layer of intelligence. Of course, we know a lot more about indirection than we do intelligence.Wheat vs chaffIBM researchers are demoing an intelligent storage system that works something like your brain: It's easier to remember something important, like a beautiful sunset over the Grand Canyon, than the last time you waited for a traffic light. In Cognitive Storage for Big Data (paywall), IBM researchers Giovanni Cherubini, Jens Jelitto, and Vinodh … [Read more...] about How smart storage will rescue big data
Video: How to build a corporate culture that's ready to embrace big dataEditor's note: This article was originally published in 2016 and has been updated for 2018. We practitioners of the technological arts have a tendency to use specialized jargon. That's not unusual. Most guilds, priesthoods, and professions have had their own style of communication, either for convenience or to establish a sense of exclusivity. In technology, we also tend to attach very simple buzzwords to very complex topics, and then expect the rest of the world to go along for the ride. Take, for example, the tag team of "cloud" and "big data." The term "cloud" came about because systems engineers used to draw network diagrams of local area networks. Between the diagrams of LANs, we'd draw a cloud-like jumble meant to refer to, pretty much, "the undefined stuff in between." Of course, the Internet became the ultimate undefined stuff in between, and the cloud became The Cloud.TechRepublic: For evidence of big … [Read more...] about Volume, velocity, and variety: Understanding the three V’s of big data
Machine Learning (ML) algorithms are embedded in the fabric of much of the technology we use every day. ML innovations spanning computer vision, deep learning, natural language processing (NLP), and beyond are part of a larger revolution around practical artificial intelligence (AI). Not autonomous robots or sentient beings but an intelligence layer baked into our apps, software, and cloud services that combines AI algorithms and Big Data under the surface. The trend is even more pronounced in business. ML is no longer solely used for specialized research projects undertaken by a team of data scientists. Enterprises now make use of ML to gain actionable business intelligence (BI) and predictive analytics from ever-increasing amounts of data. That's why it's more important than ever to be aware not solely of what ML is but also the most effective strategies in which to use it for tangible value. Ted Dunning, Ph.D., is the Chief Application Architect at enterprise Hadoop vendor MapR, and … [Read more...] about The Business Guide to Machine Learning
We've written a lot about the convergence of cloud infrastructure, Big Data, and artificial intelligence (AI) this year. Throughout the Software-as-a-Service (SaaS) space, we've seen an inextricable link between these three factors in business intelligence (BI) tools, social listening platforms, customer relationship management (CRM) solutions, or really any industry that's leveraging cloud-based data ingestion and analysis—which is pretty much all of them. Across use cases, we've observed a four-step process. Enterprise businesses gather massive amounts of data by using a portfolio of SaaS apps. They then store that data in the cloud by using a data warehouse or data lake, using data governance to keep data compliant and secure. Step three is data science experimentation: throwing everything at the data, from machine learning (ML) algorithms and natural language processing (NLP) to predictive analytics. Step four, ideally, is where that data science yields deeper, data-driven … [Read more...] about Business Tech Predictions: 10 Ways AI, Big Data, and Cloud Will Evolve in 2017