How to make data scientists shine

#artificialintelligence 

The effort to take advantage of emergent new business innovations, of advances in digitization, analytics, artificial intelligence, machine learning, internet of things or robotics, is leading to an increasing demand for people with related skills. Being a data scientist may be considered as the sexiest job within the data related jobs, but it has its challenges, specially when it comes to demonstrate the value created by their work. In this article, let us look at some of those challenges, and how they can be overcome when organizations take on a systematic approach on how to manage their data. This is often a communication problem, turning a business problem into a technical problem, when there is a gap in the language and concepts used by the business stakeholders and the data scientists. However, the causes run deeper, and can be related also with a lack of data literacy on the business side and business literacy on the data side, and with the lack of organization wide business concepts that can be clearly mapped into data.

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