Big data for guide dogs. IBM helps train service dogs

Big data for guide dogs. IBM helps train service dogs

Big data for guide dogs. IBM helps train service dogs

I love the idea of big data. Being able to derive insights from the ever-increasing stream of information out there is an awesome promise. But alas, a good proportion of the case studies we hear about big data end up being about how big data is used to tailor sales and marketing messages and eke out a few cents extra on every transaction. It may be the reality of our commercial world, but it's sad to see a promising technology applied this way.

So, it was nice to receive a pitch from IBM's cloud computing division about how it is partnering with Guiding Eyes for the Blind to bring dogs and data together. While it may sound like an April Fool's joke, IBM is taking structured and unstructured canine data and putting it on the IBM cloud to crunch numbers and generate insights.

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That's not as crazy an idea as it sounds. Guiding Eyes for the Blind, one of the U.S.'s leading breeders and trainers of service dogs, has been collecting and analyzing troves of information about its dogs (half a million health records and 65,000-plus temperament records) continually perfecting its breeding and training processes since 1954. Not surprisingly, the data was sitting trapped within legacy systems. The move into the cloud will not only provide simplified IT and data management, but it will improve data accessibility for greater analysis.

It would be easy to assume (as I did) that breeding guide dogs is a fairly simple process, with everyone settling on the best breed and just getting down to it. Instead, data from medical records and complex genetic mapping is all utilized, as well as unstructured data from feedback collected from trainers and host families reflecting upon their experiences with different dogs.

Guiding Eyes is moving all of its records to the cloud and alongside cost and analytics benefits, aims to provide easier access to colleagues across the U.S.

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Comments 1

  1. Richard M Little

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