Knowledge bank

In this step, you go deeper into the analysis and use all the data science tools you have available. Now it’s time to crunch the data and find all the insights you can. If patterns have been found earlier that are based on certain data, you can try to expand the selection or find new data sources around the interesting data. It is also possible to compare different selections against each other. For example, you can create a predictive model that compares underperforming customer segments with your average customer to find insights. The qualitative insights also play a big role in creating a story together with the quantitative data. This so that insights actually lead to an action plan that can be implemented in the company or organization.