Analyzing historical data, such as determining the top-line revenue total from last month’s sales, is typically straightforward as it informs us about past events. However, delving into data to uncover intricate patterns and correlations (referred to as relationships) poses a greater challenge. The complexity escalates when using historical data for predictive purposes or to explore hypothetical scenarios (“what if” analysis), particularly if real-time insights are desired based on incoming data—broadly classified as advanced analysis. In this context, all Consumer Packaged Goods (CPG) and consumer product companies engage in data science, especially at the standard analysis level, and in some instances, at the more intricate advanced analysis level.