Archive for category Big Data
Our friends at Forbes.com have put together a fantastic new infographic leveraging data from Wikibon’s Big Data Vendor Revenue and Market Forecast, 2012 – 2017 report. It provides a compelling view of the Big Data universe and illustrates the real revenue vendors are deriving from Big Data. They range from the mega-planets (if you’ll go with me on this analogy) IBM, HP and EMC to the smaller but powerful emerging planets like Hortonworks, 10gen and DataStax.
Ok, not the greatest analogy but still a great infographic:
Then-CEO Sam Palmisano launched IBM’s Smarter Planet initiative five years ago during a speech at the Council on Foreign Relations. IBM would focus its energies, Palmisano said, on helping governments and companies understand and analyze the voluminous data streaming off connected devices and industrial equipment to improve operational efficiencies and deliver better services to citizens and customers.
Since then, IBM has largely had the Industrial Internet, as the concept of has come to be called, to itself. The company’s Smarter Planet division has played a key role in making IBM the biggest Big Data company on the planet and was a lone bright spot in IBM’s otherwise disappointing Q1 2013 results.
Recently, IBM announced a $1 billion initiative intended to improve the overall flash storage market and integrate flash storage in the company’s line of enterprise technology equipment, including servers, storage, and other products. The company feels that flash-based storage is an a tipping point in the marketplace and is poised to become much more widely used, thanks to the incredible performance gains offered by the technology. Further, as is the case with any technology, as it approaches a critical mass point, the overall costs of the technology begin to drop and this is certainly happening with flash storage. There are also other significant cost benefits to flash-based storage, such as reduced power consumption. At scale, such power savings can be real and significant.
I’d like to explore the topic of how system and storage architectures are changing and the impact this will have on application delivery and organizational productivity.
Allow me to put forth the following premise:
Today’s enterprise IT infrastructure limits application value.
What does that mean? To answer this, let’s first explore the notion of value. The value IT brings to an organization flows directly from the application to the business and is measured in terms of the productivity of the organization. Infrastructure in-and-of itself delivers no direct value; however the applications, which run on infrastructure directly affect business value. Value comes in many forms but at the highest level it’s about increasing revenue and/or cutting costs; and ultimately delivering bottom line profits.
The first iteration of HP’s new line of low-power servers, known as Moonshot, begins shipping this week. HP plans to release numerous cartridges for the chassis over the next several quarters, each optimized for specific workloads. The first cartridge for the HP Moonshot 1500 chassis utilizes Intel’s Atom chip and is aimed at web hosting; HP promises future versions of the server optimized for Big Data.
Oracle reported disappointing earnings yesterday, with profits flat and revenue down 1%. The company blamed the results on a poor sales execution strategy, which no doubt did have a negative impact on Q3 earnings. But the long-term threat to Oracle’s database business isn’t sales strategy but open source Big Data.
Mobile devices play a dual role in the context of Big Data. Mobile devices – namely traditional mobile phones, smartphones and tablets – are both sources of Big Data and delivery mechanisms for Big Data.
Mobile as Source of Big Data
When talking about Big Data, the conversation tends to focus on Data Science and analytics. That is, the stories about Big Data that hit the front pages of the mainstream press and the hallway conversations taking place at events like Strata are mostly about all the cool new ways to use data to greater effect.
But Big Data Analytics doesn’t take place in a vacuum. It takes place in the enterprise. And any time you mix data and the enterprise, you can’t afford to ignore data management best practices. It may not be as sexy as predictive analytics, but failure to apply fundamental data management best practices to Big Data projects can lead not just to failed projects, but to potential legal consequences as well.
Tomorrow marks the kickoff of Strata Conference 2013. This year, SiliconANGLE Wikibon is expanding its coverage from two days to three full days of live broadcast from the show floor. Tune into theCUBE at SiliconANGLE.tv all week to catch it all, and log on to strataconf.com/live between 8:45 am and 10:00 am PST Wednesday and Thursday to watch the live keynotes.
We start things off Tuesday morning when we welcome Edd Dumbill, Co-Chair of the Strata Conference, to theCUBE. Edd and hosts Dave Vellante and John Furrier will preview the upcoming action and layout the themes we’ll be covering.
A Massachusetts company called Prelert released a new application yesterday that combines machine learning and predictive analytics to detect and report anomalous behavior emanating from IT infrastructure. If that sounds a lot like what Splunk does, you’re right.