Data analytics (DA) in the modern context is often understood as the processes and tools for extracting information out of Big Data. The challenge in DA is on the dimension of the data (i.e., the number of potential variables) rather than on the size (i.e., the number of observations). Considering interaction terms is, for example, natural in many research and application contexts, but that makes the dimension of data much larger without a corresponding increase in the number of observations. Neural Networks and many other machine learning tools are often ill-suited for such kinds of DA problems. I will introduce a new variable selection tool based on a zero-norm penalty, and then demonstrate with examples how it can handle high-dimensional data in finance and beyond where the number of observations is either large or small. The talk will also touch upon the AI work-in-progress assisted with this new analytics at the Credit Research Initiative of the National University of Singapore.