When we think about spatial data, whether in the context of data management or data analytics, we tend to think of it in terms of x's and y's in a table; an afterthought that describes some additional attribute of an thing we are interested in. But spatial data is a powerful abstraction of where a thing exists. It is non-arbitrary, prone to error, and deserving of special treatment. The analysis of it can provide deep insight into how the location of an object can determine its core attributes; not just describe them. This talk will aim to re-map our association with spatial data; from how it is acquired and managed to how it can be analyzed either in isolation or in service to machine learning models. It will also aim to demonstrate that inherent in spatial data are spatial relationships that, when properly acknowledged, can increase ones available information by orders of magnitude leading to valuable business insights.