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Tim Kannegieter

McKinsey on Data Analytics

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It's almost self-fulling. From my interactions with the world of big data of the last year I've noticed a willingness to sacrifice quality for quantity. It's almost as if instead of thinking about the value of the data at the record level, the approach now is to gloss over details like accuracy, relevance and valid representation. Just turn the firehose on and let the big machines with their magical AI sort it out. So now anyone trying to extract meaning from little data finds that there's nothing of value because the data is crap. Eventually you might get enough crap pilling up so that one pile of crap is a bit bigger than another pile of crap and eureka - the big data machine has found something!

 

I like to temper my enthusiasm for this amazing big data device with the cautions of Cathy O'Neil:

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Big Data processes codify the past, they do not invent the future. Doing that requires moral imagination, and that's something only humans can provide.

 

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Heath - I've also been alarmed by the same patterns of thought among the Big Data crowd.

The extension of the 'fire hose' mentality is a demand for vanishingly cheap IoT sensors distributed liberally across the countryside under 'the-more-the-merrier' recipe. 

Once measurement quality bottoms out in the drive for low-cost sensors, we're back to the same old garbage-in garbage-out conundrum.

The corollary to this is that the Big Data crowd fervently wish to have nothing to do with the really tough business of keeping IoT systems working in the field. This grubby end of the business is costly and time-consuming and requires enormous engineering effort until mass markets are reached. They seem very prepared to simply write that off as 'someone else's problem'

So we need to understand each others dilemmas - hence the value of this forum.

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Big Data can be very powerful but unless there is a clear business problem to solve, yes, it will always be the case of "garbage in - garbage out".

Data analytics provides the ability to identify possible trends and patterns in the data collected that may not seem otherwise obvious. But only someone which knows the business and its processes very well can understand if any of these trends and patterns are of any business value to the organisation.

IDC recently did a research asking: Is Big Data losing steam or is Australia yet to taste success? They found that capabilities to analyse data not keeping pace with the rate at which data is created. Here is a link to the full media release: http://www.idc.com/getdoc.jsp?containerId=prAP42238817

     

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