Big data: Buzzword for some, reality for others

I'm looking forward to my talk at WebExpo 2012 in Prague. Among many other things, I will talk about famous Peter Harris of Adpac. Peter Harris had his own personal Silicon Valley before there was anybody at The Peninsula (the story how he found his VC is legendary), and he's an icon for many of us at GoodData as we shared an office with him in 2010.

You might wonder what the connection between Mr Harris and big data is. While doing my research for the talk, I found a copy of Computerworld from 1981. One article is about Peter Harris; it starts with the following amazing line:

PORTLAND, Ore. – “Structured programming,” one of the software buzzwords for the 1980s, has been a reality for more than a decade in the data processing offices of the Georgia-Pacific Corp. (GP) headquarters here.

Mr Harris is the one who invented the term structured programming. That was in sixties. In 1981, he explains in a major  IT periodical that GO TO command is bad.

For me, that's insane. And it shows something unbelievable: What we see today as an important and doubtless trend, was actually a war of many battles. Many of them, apparently, belittling structured programming as yet another buzzword.

This is exactly where we are with big data. We feel the urge of discussing the buzzwordness of big data and we predict it will all settle down. Well, not all of us. Some have started to climb the ladder.

For many, the first rung is to realize big data is all around us. Next rungs are about collecting it and analyzing it.

I believe your first step into the world of big data should be different. First you should understand big data influences your business, and–if you're smart–big data can drive your business.

To analyze big data is futile if you don't take any action. That's why credit card companies work with big data for years without even calling it big data. Yes, the volume is epic but that's just one feature of big data (and definitely not the crucial one).

Dust is swirling, visibility is low. You can wait and read more and more definitions of big data. Or, you can be Peter Harris and do what it takes. His story shows the stakes can be incredibly high.