It’s amazing how much knowledge goes into selecting cows for milk production.

I love this quote: “[we] ran it through a fairly sophisticated computing gobbledygook. It spit out at the other end predicted transmitting ability, predicted genetic values of whatever sort.”  How would you like to explain your complex analysis to a business user using those terms? In most cases, those might be the best way to describe things to most users.

However that quote comes from a scientist that has spent a lot of time and knowledge refining models and using lots of data to try and predict the bulls whose progeny will result in the best dairy cow performance. The article is very interesting and talks a bit about the complexity of using genome data on cattle and how far it is from the experiments Mendel performed on pea plants. I still remember the dominant/recessive gene explanations from biology, though that explanation seems simplistic now. Comparing it to the work done by scientists predicting cow performance is like comparing horse drawn carriages to Formula 1 race cars.

Big data is in the news, with so many companies building products they want to sell to help you tame, analyze, and use the data your organization has available. However as this article hints, it’s much more than managing all that data. There is a tremendous amount of knowledge and intelligence needed to use those tools and actually generate useful information.

As data professionals, we don’t necessarily need all that knowledge, but we do need to communicate with those individuals that may have the knowledge and work closely with them to ensure their ideas are accurately applied against data in the algorithms we write. In addition to writing code and understanding statistics, strong communication skills are important to moving your career forward.

Steve Jones

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About way0utwest

Editor, SQLServerCentral
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