I keep getting the feeling we're on the brink of another momentous industry shift. Sure, I can think of some big reasons. But sometimes, it's the small stuff that drives the point home.
Last week was a study in contrast between old and new IT. On Monday, I attended a Dell event where the main purpose was to introduce a new line of PowerEdge servers -- and, without irony, convince the world that Dell is actually a services company. As I looked around at the audience of bored analysts and reporters slumped over their black ThinkPads, asking rote questions about speeds and feeds and revenue, I half expected a young female athlete to come running down the aisle and hurl a hammer at Michael Dell.
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The scene was a perfect symbol of "the old IT": We might give it a new spin, but we're going to keep doing what we've been doing because we can't imagine anything better to do.
The next day I took a cab across town with Doug Dineley, head of the InfoWorld Test Center, and visited a startup called Nodeable that offers a SaaS application wrapped in a Twitter metaphor to monitor virtual servers on Amazon Web Services. Nodeable plumbs Amazon's APIs for event data to boil up key performance indicators for dashboards and -- it says -- will supply tools to help developers take control of operations. In the process, programmers can provision their own dev, test, and deploy environments, and operators can single-handedly manage dozens or hundreds of servers.
Nodeable's quest is emblematic of the "new IT": We all know the world is headed to the cloud and we know we have the ingenuity to provide an important part of the tooling, even if we haven't totally hashed out where we fit in.
The day after that, at the O'Reilly Strata conference, a live wire by the name of Jesper Andersen, general manager of Bloom, gave a killer data analysis and visualization presentation contrasting San Francisco's upper and lower Haight Street. As the session description stated, "We'll integrate basic public data from the city, street and mapping data from Open Street Maps, real estate and rental listings data, data from social services like Foursquare, Yelp and Instagram, and analyze photographs of streets from mapping services to create a holistic view of one street and see what we can understand from this."
That we did. Fascinating conclusions about crime, happiness, human behavior, and more came bubbling up from Andersen's patchwork of data sources. It was the bright promise of big data right before your eyes. Did Andersen's exercise have some obvious moneymaking application? Not really -- mostly it seemed to confirm things we already know (such as where on Haight to open a bar and where to open a shoe store). But the potential seemed boundless.