Analytics Troubled by Bottlenecks. Impossible.
September 30, 2014
The hyperbole artists have painted themselves into a corner. I am not sure too many folks know this. The idea that one can crank out killer analyses with a couple of swipes or a mouse click are raising expectations. Like so much in content processing, reality is a just little bit different.
You know that the slips twixt cup and lip must be cropping up in numerous organizations. The Harvard Business Review does not write too much science fiction compared to MIT’s Technology Review across the river.
“Beware the Analytics Bottleneck” adopts the same MBA tone that makes Wall Street bankers and lawyers so beloved by the common man and delivers what might be a downside.
The write up states:
“Don’t be overwhelmed. Start slower to go faster.” I think that runs counter to the baloney in the Eric Schmidt Google tome.
Next the HBR wants to keep life simple for the busy one percenters:
Technology doesn’t have to be exposed. Keep the complexity behind the curtain. Definitely good advice if one does not know whether the data are valid and the numerical recipes are configured in an appropriate manner.
Then the golden piece of advice for the go go MBA looking for a payday so he or she can pursue his or her dream of helping people or just spending money:
Make faster decisions for faster rewards.
That’s a sure fire way to break through bottlenecks. Use the outputs to support really fast decisions. Forget that pondering stuff. Just guess.
What’s scary is that when some folks have a tiny bit of knowledge, their deliberations can yield disastrous decisions. Need some examples. Well, do some thinking. How about GM and ignition switches? What about IRS actions and email mysteries? Or multi billion dollar acquisitions that lead to multi billion dollar write offs shortly after handing over the dump trucks filled with cash?
My take on this write up is that the “expert” did not focus on the bottlenecks that Big Data often produce like sex crazed hamsters:
- The time and cost to normalize and validate data
- The complexity of updating indexes so that reports reflect the most recent data, not stale data
- Dealing with the configuration decisions that generate outputs that are just plain wrong
- The money spent to get a system back online when it crashes either an old fashioned on premises flame out or one of the nifty new cloud systems that are virtual and allegedly fool proof.
In short, Big Data and analytics pose some very significant challenges for vendors, licensees, and those who use the systems. The good news is that guessing will probably produce better results than reasoning through a decision based on flawed information. The bad news is that fancy content processing systems are likely to gobble budgets and increase certain operational costs.
The HBR obviously does not agree. Well, the fellows around the cast iron stove in Harrod’s Creek, Kentucky, find my observations directly on point.
Stephen E Arnold, September 30, 2014
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