With the development of Oracle Analytics, I thought it was the right time to revisit and refresh the content and pay a bit more attention to three areas I haven't focused in my initial blog: data analysis, data preparation and model training/comparison.
My general impression is that if business users who want to include predictive models into their dashboards and analyses they don't need to be data scientists. They would need to know few basics, but otherwise very simple tools are available to analyse data, prepare data and create machine learning models. The price they will pay are most likely less accurate predictions (comparing to data scientists-built models), but I don't think this could be a show stopper. Therefore I would encourage users to use machine learning in regular analyses. Of course, wherever it makes sense.
This blog series comprises of three posts, as follows: