But according to the data Coté has assembled, PaaS isn't a home run success quite yet.

According to Coté, it's difficult to track any hard numbers down. Most vendors want to talk about apps deployed and registered users, not about paying customers. Coté says the total number of apps deployed on Google AppEngine, Force.com and Heroku is fewer than 300,000. "Which, is a big number, sure," he writes. "But how many applications are there in the world?"

But this is still an early space. As we pointed out earlier Gartner is seeing an increased number of inquiries, and as you can see above the number of PaaS jobs is growing. So there does seem to be some interest in the enterprise.

Photo by LukeGordon1

That's the question this month for our MacBook Air contest. Best comment on this post wins. You'll need a Disqus account to participate. The question raises all kinds of potential conversations, ranging from the type of server security to remote access and encryption considerations. So, let's hear it!

We'll review the comments and pick a winner at the end of the month. Independent analyst Dan Kusnetzky is our co-judge this month. Thanks to Tom Raftery for his help in the June contest.

The winner will be announced on the first of August. We're still waiting to hear back from the winner we selected for June. We'll announce soon!

Now, let's hear from you. We look forward to the conversation!

Borenstein raises some excellent points about how we think about big data and where the whole concept may be going. Just because we have massive amounts of data doesn't mean we know how to use it or that it will ever be helpful.

Straw Man or Reality Check?

Borenstein writes, "Overall, there seemed to be a pervasive worldview that, if stated reductively, might be expressed thusly: Now, with so much of human behavior taking place over the web, mobile devices, and through other information-producing systems, we are collecting so much data that the only rational way of approaching most decision-making is through rigorous data analysis. And through the kind of thorough data analysis made possible by our new massive cloud computing resources we can finally break through the inherent irrationalities and subjectivities built into our individual observations, mental models, worldviews, and ideologies and into a new more objective data-driven representation of the world that can improve and rationalize our decision making."

Borenstein writes, "I'm not trying to create a straw man," but kind of does anyway. I've followed the business use of big data more than the social or scientific uses, but find it difficult to believe the outlook is as extreme as Borenstein paints it to be. Then again, I wasn't at the conference. But Borenstein himself writes that "These are incredibly smart people who live in the midst of the subtle distinctions and limitations that come up in practice when working on these kinds of problems in real life."

That said, I don't think it's unfair to say that big data is at times over-hyped. In business, "big data will save us all" isn't a common refrain, but "big data will save your company" is. There are problems that having mountains of data won't solve. There are also problems that might be helped with big data, but for which there are no guarantees. So it's well worth a reality check from time to time.

Is Big Data Just Cybernetics All Over Again?

Part of Borenstein's critique of the big data movement is based on the Adam Curtis documentary All Watched Over by Machines of Loving Grace, part of which Matt "Black Belt" Jones screened at FooCamp. Jones showed the second installment which covered the cybernetics movement, which eventually gave way to systems theory and systems thinking.

Cybernetics, the study of feedback and self-regulating systems, was deeply influential in the development of the personal computer in the 60s, as documented by Fred Turner (see this interview with Turner for more). Borenstein notes that showing All Watched Over by Machines of Loving Grace at FooCamp was an act of "epic trolling" on Jones' part because "Cybernetics was the dominant philosophy of the 60s and 70s techno-counterculture within which O'Reilly arose."

I haven't seen the documentary yet, so I can't comment on its accuracy or quality. The film looks at the work Jay Forrester did on World3, a project that, according to Wikipedia, was meant to model and predict the "interactions between population, industrial growth, food production and limits in the ecosystems of the Earth." World3 predicted economic and societal collapse.

Borenstein writes:

As a polemic, Curtis's film does more than present this history in a neutral manner. He constructs a critique of cybernetics. He argues that this emphasis on building ever-more accurate models of the world -- and, especially, automating their results through the supposedly objective computer -- represses any idea of individual agency to change the system while simultaneously causing us to project a false agency onto the system itself. In other words, Curtis focuses on cybernetics' conservative political repercussions. In his account, this faith in the technologically augmented system model becomes a reason to defend the status quo.

(Another interesting case study of this sort of applied social cybernetics is described here.)

Borenstein ties cybernetics and World3 back to the promise today of big data. From IBM's promise of a "smarter planet" to Heritage Providers Network's attempts to improve health care through a data contest, we're seeing some grand plans to improve the world through data crunching. The comparison to cybernetics is apt.

From Polygraphs to Predictive Analytics

The big question is whether things will be different this time. The tools for collecting and processing data have improved immensely since Forrester's time. And so far it seems that most implementations of big data are focusing on solving one particular problem, rather than trying to model the entire world.

Still, I suspect there will be many failures and that many of these failures will not be acknowledged. One of my predictions for 2011 was that predictive analytics (which as Revolution Analytics CEO and SPSS co-creator Norman H. Nie says, is actually just a new name for statistical modeling) would be applied to more and more areas, even if it doesn't work.

For example, earlier this year Nature reported that the U.S. Department of Homeland Security is testing a system for detecting terrorists at airports called Future Attribute Screening Technology. The journal reported "Like a lie detector, FAST measures a variety of physiological indicators, ranging from heart rate to the steadiness of a person's gaze, to judge a subject's state of mind."

The downside is that polygraphs have never been proven accurate, and I'm doubtful that this technology will be able to accurately predict individual humans' behavior. I consider myself a determinist, but I think the actual ability to predict every possible outcome of human behavior is far beyond our abilities.

Last year Jonah Lehrer wrote about a phenomena called the "decline effect" - the tendency for support for scientific claims to decrease as experiments are repeated. In particular, behavioral science seems to be greatly affected by the decline effect. Part of this is due to publication bias, but according to Lehrer a big part of the problem is sheer randomness.

Human behavior is messy and hard to model. In some cases, that might be fine. If a "other products you might like" widget or targeted advertising system mostly shows users stuff they don't want, it's okay as long as it gets it right enough to boost sales. Trying to predict and prevent terrorism, without unduly targeting innocents, is much more difficult.

This Ad's For You

Companies like Hunch and OK Cupid are learning a lot of interesting stuff by analyzing data - but how useful is it? Even if we could assume beyond a shadow of a doubt that 60% of people who like beer are willing to have sex on a first date, you're always going to have the exceptions.

And in the case of advertising, I'm still not sure we're going to get to a point that all the money being spent on finding ways to turn big data into advertising dollars is going to turn out to be a good investment. It's worked out well for Google, but it hasn't for virtually any other company. For all the data Facebook supposedly has on us, Facebook ads are less effective than banner ads.

Then there's the question of what to actually do with all that data. Last year we asked what you would do with the massive data sets generated by persistent surveillance. We mostly got crickets.

So no, not only will big data not "save us all" - it won't even save all our businesses. That doesn't mean it's not relevant - it doesn't even mean that the ability to cope with massive data sets isn't the most significant technological development of the past decade. But it would be unwise to put too much faith in our ability to crunch numbers.

The problem is doing the math and figuring out exactly what you are replacing with any cloud-based provider, where that provider's data center is, and what kind of efficiency and fuels profile is being used by the provider - and your own data center - to make the calculation meaningful.

"As Cloud Computing providers are not publishing any data around Cloud Computing's energy consumption, then it is impossible to say just how energy efficient Cloud Computing is," he said during his talk.

Just because you moved your apps from your data center to a cloud provider and turned off your old and creaky servers doesn't mean you saved the planet. Yes, you saved your energy bill - if indeed you actually turned off those servers. But your provider may not have any more efficient servers on their end. You assume they do, what with virtualization and running a bunch of VMs on a single machine, but still.

Google, Microsoft, Facebook and Apple have gone to great lengths to build very energy-efficient data centers all over the world. But they are located in places where they can get cheap electricity per kilowatt hour and, more often than not, these are places that are burning coal to produce the juice. Raftery concludes his talk by mentioning Jevon's paradox, which relates to the industrial revolution and how it created more of a demand for coal-fired industries, which were efficient but still consumed more fossil fuels.

N.B. Raftery was our most recent Cloud Contest judge, but this was a volunteer position.

In this case study, learn how Welch's uses technology from VMWare and Intel to help keep the juice flowing.