Stephen Wolfram is doing it again.  I’m a big fan of Wolfram (you can read some of my other posts here, here, and here…), and am always intrigued by what he comes up with.  A couple of days ago, Wolfram launched his latest contribution to data science and computational understanding – Wolfram|Alpha Pro. 

Here’s an overview of what the new Pro version of Wolfram|Alpha can provide:

With Wolfram|Alpha Pro, you can compute with your own data. Just input numeric or tabular data right in your browser, and Pro will automatically analyze it—effortlessly handling not just pure numbers, but also dates, places, strings, and more.

Upload 60+ types of data, sound, text, and other files to Wolfram|Alpha Pro for automatic analysis and computation. CSV, XLS, TXT, WAV, 3DS, HDF, GXL, XML…

Zoom in to see the details of any output—rendering it at a larger size and higher resolution.

Perform longer computations as a Wolfram|Alpha Pro subscriber by requesting extra time on the Wolfram|Alpha compute servers when you need it.

Licenses of prototying and analysis software go for several thousand dollars (Matlab, IDL, even Mathematica) - student versions can be had for a few hundred dollars, but you can’t leverage data science for business purposes on student licenses.

Wolfram|Alpha Pro lets anyone with a computer, an internet connection, and a small budget to leverage the power of data science.  Right now, you can get a free trial subscription, and from there, the costs are $4.99/month.  This price is introductory, but it could be sedutive enough to attract a lot of users (I’ve already signed up – all you need for the free trial is an e-mail address…)

One option that I find really interesting is Wolfram’s creation of the Computable Document Format (CDF), which interactivity lets you get dynamic versions of existing Wolfram|Alpha output as well as access to new content using interactive controls, 3D rotation, and animation.  It’s like having Wolfram|Alpha is embedded in the document.

I had attended a Wolfram Science Conference back in 2006 and saw the potential for such a document format back then.  There were a number of presenters who later wrote up their work into a paper, published by the journal Complex Systems.  Since many of the presentations utilized a real interactivity with the data, I could see where much of the insight would be lost when people tried to write things down and limit their visualizations to simple, static graphs and figures.

I remember contacting Jean Buck at Wolfram Research, and recommending such a format.  Who knows whether that had any impact, but I’m certainly glad to see that this is finally becoming a reality.  I actually got the opportunity to meet Wolfram at the conference (he even signed a copy of his Cellular Automata and Complexity for me… – Jean was kind enough to arrange that for me – thanks, Jean!)

If you’re interested in data science and have a spare $5 this month, try out Wolfram|Alpha Pro!

Based upon their Magic Quadrant analysis of data integration tools, Gartner rates Informatica Corp. and IBM as the top software vendors in the space.

Gartner uses a Magic Quadrant to rate companies as leaders, challengers, niche players and visionaries based on several criteria including “completeness of vision” and “ability to execute.”  From Gartner’s website:

• Leaders execute well against their current vision and are well positioned for tomorrow.
• Visionaries understand where the market is going or have a vision for changing market rules, but do not yet execute well.
• Niche Players focus successfully on a small segment, or are unfocused and do not out-innovate or outperform others.
• Challengers execute well today or may dominate a large segment, but do not demonstrate an understanding of market direction.

A post by Mark Brunelli, Senior News Editor, at SeniorDataManagement has a more detailed analysis of the Gartner report.  Here’s what Brunelli wrote, detailing some of the thoughts of Ted Friedman, a Gartner vice president and information management analyst and co-author of the report:

“You’re hearing a lot about big data and analytics around big data,” Friedman said. “To do that kind of stuff you’ve got to collect the data that you want to analyze and put it somewhere. [That] in effect is a job for data integration tools.”

It does seems that the main focus right now in this space is on data handling and data management.  A lot of work is being done by companies to create data visualization tools to gain insight from the data, but as the problems get much harder, better analytics approaches will need to be brought to bear.  The real key over the next few years will be on the smart analysis of all this data, turning the data into reliable actionable information.

On the Wall Street Journal website today, Ben Rooney posts an interview with Hortonworks CEO Eric Baldeschwieler, co-creator of Hadoop.  For all those in the big data space, the Hadoop project develops open-source software for reliable, scalable, distributed computing, and Hortonworks is focused on accelerating the development and adoption of Hadoop.

In Rooney’s interview, Baldeschwieler describes the problem Hadoop is designed to solve:

At its base, it is just a way to take bulk data and storage in a way that is cheap and replicated and can pull up data very, very fast.

Hadoop is at one level much simpler than other databases. It has two primary components; a storage layer that lets you combine the local disks of a whole bunch of commodity computers, cheap computers. It lets you combine that into a shared file system, a way to store data without worrying which computer it is on. What that means is you can use cheap computers. That lets you strip a lot of cost out of the hardware layer.

The thing that people don’t appreciate when you drop a lower price point is that it is not about saving money, it is about being able to do an order of magnitude more on the same budget. That is revolutionary. You can score five to 10 times more data and you can process it in ways that you can’t imagine. A lot of the innovation it opens up is just the speed of innovation. You get to an answer faster, you move into production faster, you make revenue faster.

You can read Rooney’s WSJ interview here…

Looking ahead, there are a number of interesting upcoming conferences within the “big data” and “data science” fields, so I thought I’d list a few in this post.

First, Strata is probably one of the biggest and fastest growing conferences within the field, and they’ll be holding their next conference in Santa Clara, CA, starting on February 28, 2012.  Featured speakers include danah boyd from Microsoft Research, Hal Varian, Chief Economist for Google, and Pete Warden, CTO of Jetpac.  They hold online conferences as well – here’s more info on the December 7 event from a previous post…

Next, Predictive Analytics World hosts a number of conferences around the world and for differing industries.  Their next conference is scheduled for November 30 in London, UK, in conjunction with the Marketing Optimization Summit and Conversion Conference as part of Data Driven Business Week.  PAW held a recent conference in New York this past October and will be hosting another one in San Francisco in March of 2012.

Also, The Big Data Summit team announced today session details for the upcoming technology event, November 8-10, 2011 in Miami, Florida.  The Summit is for C-level executives who are involved in data storage, data management and data analysis to gather and discuss how companies can effectively manage, protect and leverage the growing amounts of data in the enterprise.