Big Data Analytics
The first steps toward achieving a lasting competitive edge with Big Data analytics.
How would it change your business if you - or your sales team - could just "rent" Big Data?
It's not as far off as it sounds. The New York Times' blog Bits recently claimed Amazon is researching business models for companies that make and sell pattern-finding algorithms for Big Data sets. And GigaOm recently reported on five low-profile startups - including one with a SaaS-based offering - that could change how Big Data is accessed and for whom it's available.
That Amazon would consider offering what is, essentially, Big Data as SaaS is not so far-fetched when you consider that it already owns the raw processing power and data stores. In fact, a number of companies use Amazon to run their Hadoop stores, including eHarmony.
Hardy points out IBM, Oracle and even Apple are well positioned to offer similar Big Data capabilities, if they wanted. And The Register adds that there's no reason a company couldn't start a similar solution using Amazon's own servers.
Right now, if you want to get into Big Data, you can set up your own cluster using Hadoop at a cost of $4,000 per node, or you can rent space from cloud computing providers such as Amazon. Data stores are "sold separately," with many companies using Big Data to process their own internal data.
There are also a number of startups that could change how Big Data is used. So far, the discussion has been focused on the infrastructure - how Hadoop stores and processes data. But that conversation is shifting, according to GigaOm, as startups address how you can actually use that data "without hiring a team of Stanford Ph.D.s."
BloomReach is one of the companies GigaOm identifies, writing it is a SaaS-based product that takes a "very targeted, very hands-free approach to big data for its customers."
Another company, Continuuity, plans to make it easier to build applications that leverage cloud computing and Big Data technologies.
So it soon may be that we'll spend less time talking about Hadoop and MapReduce, but more time talking about the business value of Big Data services.