Posts Tagged Big Data
The Hadoop ecosystem is an eclectic mishmash of start-ups, mid-sized vendors and IT heavyweights with products and services up and down the Big Data stack. Inevitably the ecosystem will consolidate and thin itself out through mergers, acquisitions and – unfortunately for some of these start-ups – bankruptcies.
Consolidation is part of the natural evolution of any given technology market after an initial period of frenzied innovation, and the Big Data market is no exception. I believe we are witnessing the start of this consolidation today. It will take several years to play out, but the first phase of consolidation is manifesting itself in the form of strategic technical partnerships between vendors that play in different segments of the Hadoop market.
IBM’s annual revenue last year dropped below $100 billion for the first time since 2010. The company’s fourth quarter results were particularly weak, coming in 5.5% below expectations. This was due in large part to IBM’s struggling hardware business, with revenue dropping a staggering 27%.
I’ve already laid out my predictions for Big Data in 2014, but I also wanted to let the Wikibon community know how my colleagues and I plan to cover Big Data in the year ahead. We’ve organized our research agenda into three major buckets.
Technology. Clearly the technologies and products that collectively make up Big Data – including Hadoop, NoSQL data stores, analytic databases, data visualization tools and more – are maturing at a rapid pace (much faster, for example, than relational databases did in the 1980s.) Big Data is also applicable across industries, meaning these technologies are inevitably and increasingly intersecting with adjacent technology movements, namely the cloud, mobile computing and social media. As we have for the last several years, Wikibon will devote significant coverage to these developments with an eye on putting technology innovations in context for enterprise Big Data practitioners (both technology practitioners and line-of-business practitioners.)
The benefits of Big Data are often spoken of in the future tense. As in, “Big Data will someday provide enterprises of all types critical insights that allow for increased profitability, improved efficiency and other untold riches.”
Same goes for the technology. Hadoop, some say, will be the foundation of data storage and analytics in the enterprise once it’s proven enterprise-ready.
The reality, however, is that Big Data is today – here and now – delivering on its many promises. We at Wikibon have been documenting Big Data in the Real World for the last two years, including publishing a series of vertical-specific research notes highlighting how enterprises in retail, banking, media, utilities and pharma are leveraging Big Data analytics to drive performance.
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.
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.