| | April 20228IN MY OPINIONBy Erik Poppe, Head of Analytics and Data, GjensidigeIn the last decade the demand for skilled data scientists have exploded. Businesses have realized that to stay competitive, they need to utilize their data with multivariate statistical methods ­ machine learning. The number of skilled data scientists has not been able to meet the business demand, thus being a major bottle neck in becoming more data driven. I predict this will change in the years to come, but many challenges still need focus.The statistical processes data Scientists have been doing by building machine learning models are now quickly being automated with so called Auto-ML tools, as well as being supported by readymade generic models you could buy access to from cloud vendors (speech recognition, feature extraction from pictures and so on). The automation pro-cess is able to build faster and often more accurate models than the data scientist are able to do themselves.In my many years working on machine learning within marketing automation, I have learned some lessons on how to succeed. Here are five key take always, and advices I can give on what I think is important to focus on in the journey to become a more data driven business.LESSONS LEARNED ON HOW TO SUCCEED WITH MACHINE LEARNINGErik Poppe
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