Big Data, Brewer, And A Couple Of Webinars

Whenever I think about big data, I can't help but think of beer – I have Dr. Eric Brewer to thank for that. Let me explain.

I've been doing a lot of big data inquiries and advisory consulting recently. For the most part, folks are just trying to figure out what it is. As I said in a previous post, the name is a misnomer – it is not just about big volume. In my upcoming report for CIOs, Expand Your Digital Horizon With Big Data, Boris Evelson and I present a definition of big data:

Big data: techniques and technologies that make handling data at extreme scale economical.

You may be less than impressed with the overly simplistic definition, but there is more than meets the eye. In the figure, Boris and I illustrate the four V's of extreme scale:

The point of this graphic is that if you just have high volume or velocity, then big data may not be appropriate. As characteristics accumulate, however, big data becomes attractive by way of cost. The two main drivers are volume and velocity, while variety and variability shift the curve. In other words, extreme scale is more economical, and more economical means more people do it, leading to more solutions, etc.

So what does this have to do with beer? I've given my four V's spiel to lots of people, but a few aren't satisfied, so I've been resorting to the CAP Theorem, which Dr. Brewer presented at conference back in 2000. I'll let you read the link for the details, but the theorem (proven by MIT) goes something like this:

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Do Not Depend On EA To Innovate

Many organizations expect EAs to be the source of technology innovations. They are broadly knowledgeable, experienced, connect-the-dots kind of people you might naturally expect to come up with reasonable ideas for new approaches and technology. When you think about it a bit, this expectation is misplaced. Here’s why I think this:

The best technology innovators are users who have a problem to solve; motivation to solve a specific problem affecting their lives is the key ingredient. EAs just don’t have these kinds of problems; because they operate as a bridge between business and technology, most often they are attempting to solve things that affect other people’s lives. Please don’t get me wrong: EAs are always looking for new, innovative ways to improve things. But this doesn’t replace the “I gotta fix this now” kind of motivation inspiring most innovations.

So am I saying organizations should take EAs out of the innovator role? Yes and no.

Here at Forrester, we have been writing and talking about topics such as Innovation Networks and new roles for business technology for a while. I think that EAs are better placed at the center of an Innovation Network where they connect innovation suppliers (lead users who are dreaming up new ways to solve their problems) with innovation users (other folks who can benefit from a generalization of the solutions the suppliers come up with). In addition, EAs can bring innovation implementers — the team members who know how to actually make innovations into solutions that work for more than just one individual or group — into the conversation.

So what should you do?

  1. Send EAs on a mission to find people doing innovative things in IT and the business. This has a side effect of connecting EAs to the frontlines, where they might discover all kinds of things.
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