There was a great post on the marketing of big data by John Foreman on his blog. I found it a very enjoyable read for one … and it showed that hype is a self-similar phenomenon. No matter what topic it is in, some people will try to generate and exploit the generated hype, regardless of the true information content associated with it.
I could shake my head, but I’ve seen this, many times over my career. Have a look at some of these posts going back 9+ years.
- On SCSI by any other transport
- On virtualization
- On clouds (wait, what? clouds hyped? no … ya don’t say)
- On Grid/utility computing which, not so curiously, evolved with the help of virtualization, iSCSI, and multi-core into clouds. More here.
Basically hype usually devolves into an “X is going to change the world” without an accurate accounting of what the costs of X will be, or the things that X depends upon. If you read any of the mostly content-free articles in various technology (e)rags these days, you’ll often see fanciful prognostications of something, without an intelligent connecting of the dots, nor an estimation of the cost of this connection, or the actual cost-benefit analysis.
I am becoming jaded in my old age …
Change requires a serious contemplation of the cost of the change. If a technology is cheaper, and better, and faster, and easier to deploy, with less hassle, frees you from mundane tasks you do not need to do as your primary mission or job objective, this sounds like a win, though you do need to consider what is the technology depends upon to make it successful.
In his post, John Foreman points out that the real mission of data analytics is to improve processes, business processes, etc. such that they are more likely to yield a positive outcome. He provides the marketing hype around big data, in an entirely deadpanned manner.
Analytics at the speed of big data
Computing at the speed of innovation
Big data at the speed of thought
Big data….at the speed of big data.
That last one from one of my former employers.
His take on this is dead on
There is this idea endemic to the marketing of data science that big data analysis can happen quickly, supporting an innovative and rapidly changing company. But in my experience and in the experience of many of the analysts I know, this marketing idea bears little resemblance to reality.
And its true far beyond the boundaries of big data, its true in general across technology.
The marketeers promise you silver bullets. There are no silver bullets. I thought I was clear on this.
I wrote this almost 2 years ago
Yet, from all the ?company X now has a hyper optimized, purple colored Hadoop distro, with a pony? announcements, one might think that it was a panacea ? a panopticon with infinite ability to extract the most profound and profitable nuggets from mountains of steaming piles of bits.
At the moment, automated tools still can?t replace skull sweat, intuition, etc.
Its good to see that smart people are also either amused or disgusted by this massive overhyping and silver-bulletizing of a set of technologies. As John points out, if Watson is so great, how come IBM isn’t using it to help, I dunno, IBM?
I remember during my SGI/Cray days when we had this new data mining tool available. It was a very visual explorer for patterns using supervised and unsupervised algorithms. I pulled some simulation data into it and played with it a bit. I thought it was wonderful. When SGI rolled it out, I seem to remember asking or being in a meeting where we asked the product team “are our executives using this?”. We could see the value in it, and thought it would help them make better decisions. The team laughed nervously and said “no.”. I remember asking, “why not?”
Dog-fooding folks … especially useful tools … is good thing.
Which is likely why most of the big data folks out there don’t use their own big data tools in their own businesses. We use our unison systems and cadence systems in house to support our business. We sell what we use. Most others don’t.
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