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Apply data science models effectively, not just for their own sake Posted on Jul 16 - 2017

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Having data scientists build analytical models doesn't do much for an organization if the models don't help generate business benefits. Here are some ways to make sure they do.

Data scientists apply machine learning and artificial intelligence algorithms to ever-increasing mounds of data in an attempt to tease out gold nuggets of information that can be used for business benefits. But, while there may be some case studies in which companies boast about their analytics achievements, one might ask how successful the data science practice is in general.

An interesting perspective on that can be found in a December 2016 Harvard Business Review article titled, "Why You're Not Getting Value from Your Data Science." In the article, author Kalyan Veeramachaneni shared a story that sheds some light on the ability of organizations to take advantage of their data science models and programs.

Veeramachaneni recounted that, during a panel discussion on machine learning, he first asked the 150 audience members if they had built a machine learning model, and about one-third raised their hands. He then asked how many people had used a model to generate business value, and then gone on to quantify the value that was produced. This time, no hands were raised. His interpretation of this was that analytics professionals typically spend most of their time at work building and tuning models, and very little, if any time examining how their analyses could be targeted at solving specific business problems.

In retrospect, that isn't an uncommon operational pattern. If we were to replace references to data science, analytics, machine learning and artificial intelligence with ones to any other emerging technology within recent memory, we'd probably find similar results. There's a predisposition for technologists to focus on using technology for its own sake rather than focusing on how it can be wielded for business value. In many cases, project plans are adopted that focus more on achieving milestones for installing and implementing a technology than on ones for achieving business results. View More

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