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.

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…

Here’s just on back-and-forth between Jannot and Wolfram:

Jannot:  A couple years ago at TED, Tim Berners-Lee led the audience in a chant of “More raw data now!” so he’s out there trying to create the data Web. And your project in Wolfram Alpha is not to deliver raw data but to deliver the interface that allows for raw data to turn into meaning.

Wolfram:  Yes, what we’re trying to do—as far as I’m concerned, the thing that’s great about all this data is that it’s possible to answer lots of questions about the world and to make predictions about things that might happen in the world, and so on. But possible how? Possible for an expert to go in and say, “Well, gosh, we have this data, we know what the weather was on such-and-such a date, we know what the economic conditions at such-and-such place were, so now we can go and figure something out from that.” But the thing that I’m interested in is, Can one just walk up to a computer and basically be able to say, “OK, answer me this question.”

This part of the Q&A is particularly interesting, since it highlights a difference of approach in what some want in technology.  Berners-Lee seems to want more “raw data”, while Wolfram is highlighting that the data isn’t really important unless you can turn the data into actionable information.  Wolfram|Alpha does just this – the technology uses Wolfram’s understanding of computation (what he built as part of his wildly successful Mathematica product line) and lets us answer questions. 

It’s an incredibly rich article – one worth reading (1) if you’re interested in data and where its taking us, and (2) if you’re interested in Wolfram and his take on science and technology.  I’m interested in both, so I think it’s very worth highlighting…

Here’s the Popular Science article, and another post to Wolfram|Alpha that highlights the history of computable knowledge (you can even order a poster of the timeline here…).   I’ve had a number of other posts on Wolfram and his scientific approach, which might worth looking into as well…

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.

15 years ago, PBS created a documentary called Triumph of the Nerds:  An Irreverent History of the PC Industry.  It was hosted by Bob Cringely, who at the time wrote a column for InfoWorld about the goings-on of Silicon Valley and now writes a weekely column, I, Cringely.  In it, Cringely humorously and effectively describes how Steve Jobs and Bill Gates created the PC industry as we know it. 

I was always impressed with this documentary and the impact the subjects (especially Jobs) had on our lives through their business and technology pursuits.  I personally think it’s amazing and worth your time watching.  They made another documentary about the history of the Internet:  Nerds 2.0.1 - this second documentary was done in 1998 and the Internet as we know it was probably only three years old or so…

There will likely be groundbreaking changes in hardware and software, where computation and decision-making will both become part of the same technology.  This could be where much of the analytics engineering advances come from over the next decade.  Read more from the NYT post here…

Here’s a link to Newsweek’s robot montage – but I’ve listed their picks below (which I can’t argue with!…)

1968 – HAL 9000 – The heuristically programmed algorithmic computer wreaked havoc in Stanley Kubrick’s 2001: A Space Odyssey.  One of my colleagues at work told me that he heard that HAL’s soft male (and now creepy) voice is the reason why all computer voices, from navigation systems to Siri, are female.  I can’t blame them!…

1977 – R2-D2 – The astromech droid was a treasured hero in Star Wars, rolling around with C-3PO and helping Luke Skywalker find the Force.  Star Wars is one of my favorite movies of all time, and I love the whole Star Wars universe (yes, even the prequels…)

1984 – The Terminator – Humanity survived the assault of Skynet’s rampaging artificial-intelligence machine. The state of California was not so lucky.   I had a recent conversation about the iPhone’s new Siri application (about how Siri could still command the iPhone even if the phone was locked), and we eventually got around to connecting this back to Skynet!…

1997 – Deep Blue – IBM’s chess-playing computer beat world champion Garry Kasparov, who accused it of cheating and demanded a rematch.  Tough day for humans who want to win at chess…

2000 – ASIMO – Honda’s 120-pound humanoid robot can recognize faces, follow the movement of objects in its field of vision, and run 3.7mph.  My daugther totally digs ASIMO – I’ve seen it about 3 or 4 times at Disneyland.  There’s kind of a sad video of ASIMO falling down trying to climb stairs – makes you really feel for the guy!…

2008 – WALL-E – Pixar’s beloved Hello, Dolly!–watching garbage collector has raked in more than $535 million in worldwide grosses.  Amazing that Steve Jobs had a role in this great robot as well, being the man who helped make Pixar the company that it is today.

2011 – Watson – The IBM supercomputer trounced two Jeopardy! champions this year, and can access 200 million pages of content in mere seconds.  This could be foundational technology for how computers solve challenging problems – pretty amazing stuff!…

Two days ago, GigaOM reported on the iTunes boss at Apple moving over to head up Apple’s television plans.  Today, they rebroadcast an article by NYT’s Nick Bilton that Apple is planning to launch a Siri-enabled television in 2013.

For those of you who have been vacationing in seclusion over the past month, Siri is part of Apple’s newest iPhone offering, allowing you to operate your iPhone with voice commands.  According to these reports, you’ll soon be able to just talk to your TV set, and it will respond to your commands. 

(Hopefully it’ll be easier than programming VCRs…)

“We’ve all heard the predictions: By 2020, the quantity of electronically stored data will reach 35 trillion gigabytes, a forty-four-fold increase from 2009. We had already reached 1.2 million petabytes, or 1.2 zettabytes, by the end of 2010, according to IDC. That’s enough data to fill a stack of DVDs reaching from the Earth to the moon and back — about 240,000 miles each way.

For alarmists, this is an ominous data storage doomsday forecast. For opportunists, it’s an information gold mine whose riches will be increasingly easy to excavate as technology advances.”

Right now, most companies in the “big data” space seem to be focusing on two things: (1) handling, storing, and moving data, and (2) building tools to visualize and analyze data.  With the explosion of data that is generated, having the technology to store it and move it around is obvious.  Also, since there is a lot of data, tools can be easily developed to grab the low hanging fruit that “big data” can provide.

These technology pushes are absolutely important, and necessary to even get at the easy problems that can be solved with mountains of data.  However, as the problems get harder, there will come a need for engineering the right analytics.  Data science and a rigourous engineering discipline can really push the envelope on what can be done in this “big data” space…

More on Computerworld’s take on the data mountain challenge can be found here…

Steve Jobs and Guy Kawasaki

Guy Kawasaki has written a post on the top lessons he learned from Apple’s co-founder Steve Jobs, who passed away last week.

Why should we pay attention to Guy’s lessons? Well, Guy was the chief evangelist for Apple, working directly with Jobs in the early days of the PC, and was critical in cultivating the market for their first Macintosh computer in the 1980′s.  He knew firsthand about the genius (and perfectionism) of Steve Jobs, and has been witness to Apple’s successes and failures since. 

He went on to become co-founder of Alltop.com, an “online magazine rack” of popular topics on the web, and a founding partner at Garage Technology Ventures.  Most recently, he’s released his book Enchantment:  The Art of Changing Hearts, Minds, and Actions. 

I think Guy is right on for these Jobs lessons – here’s one that I thought was particularly good:

6)  You can’t go wrong with big graphics and big fonts.

Take a look at Steve’s slides. The font is sixty points. There’s usually one big screenshot or graphic. Look at other tech speaker’s slides—even the ones who have seen Steve in action. The font is eight points, and there are no graphics. So many people say that Steve was the world’s greatest product introduction guy..don’t you wonder why more people don’t copy his style?”

I’ve seen so many presentations that cram too many words on a slide, where it’s clear that the presenter thinks that it’s the information that’s important – cram more on and the slide must be better.  Jobs was a master on how to focus people on his message – leading to incredible success for his companies and his innovations. 

Take a read of Guy’s lessons here - it’s worth learning some of these lessons…