Big Data is Puzzling!
The three Vs — volume, velocity and variety — are the essential characteristics of big data. But isn’t it amazing that “big data” suddenly seems to have happened overnight & is conveniently a great marketing ploy for Technology companies to sell their wares! But do companies need “more data” or “less”. What you actually need is “Big commitment” to data period!
The current data situation for most companies is like having sections of a jigsaw puzzle in different rooms, but the puzzle keeps growing without a “puzzle master” integrating all this. The analyst, like the “ring master”, is really the “puzzle master” here & she needs to think very differently to do this. We don’t need more data; we need the correct interrelationships between data to be established & then we need “Big execution commitment” to make the data matter, by bringing decisions closer to the front end of every business.
I am constantly hearing that Data analysis is becoming a more important component to many businesses. IDC estimates enterprises will spend more than US$120 billion by 2015 on analysis systems. IBM estimates that it will reap $16 billion in business analytics revenue by 2015
There is so much talk about shortage of analytics skills. Research from McKinsey & Co suggests that US organizations are facing a shortage of 200,000 IT staffers with deep analytics skills. I wonder whether we really have a shortage or are we looking for the wrong people?
Do we need more “specialists” who really cannot “integrate” the many parts of a puzzle or do we need people from all sorts of backgrounds who bring fresh perspectives to the data that we already have not some mythical “big data”!!
Here is something interesting: Watching how people put together picture puzzles can reveal "a lot of profound effects that we could bring to big data" analysis, said Jeff Jonas, IBM's chief scientist for entity analytics
Joab Jackson has this very interesting take in the CIO magazine:
Puzzles are about assembling small bits of discrete data into larger pictures. In many ways, this is the goal of data analysis as well, namely finding ways of assembling data such that it reveals a bigger pattern.
A lot of organizations make the mistake of practicing "pixel analytics," Jonas said, in which they try to gather too much information from a single data point. The problem is that if too much analysis is done too soon, "you don't have enough context" to make sense of the data, he said.
Context, Jonas explained, means looking at what is around the bit of data,in addition to the data itself. By doing too much stripping and filtering of seemingly useless data, one can lose valuable context. When you see the word "bat," you look at the surrounding data to see what kind of bat it is, be it a baseball bat, a bat of the eyelids or a nocturnal creature, he said.>
"Low-quality data can be your friend. You'll be glad you didn't over-clean it," Jonas said. Google, for instance, reaps the benefits of this approach. Sloppy typers will often get a "did you mean this?" suggestion after entering into the search engine a misspelled word. Google provides results to what it surmises are the correct word. Google guesses the correct word using a backlog of incorrectly typed queries.
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