The 20th-century art movement revolutionized traditional painting. Instead of limiting the representation of an object to two dimensions, it showed the viewer all the possible viewpoints of an object at once. Okay, so it may not be a perfect comparison, but Spendata’s “cubism” also brings a new perspective to the field of spend analysis.

One Cube vs. Many Cubes

The cube is a perfect metaphor for any multi-dimensional dataset, whether it’s spend data or any other data. It lets you slice, dice, and drill around, but, as any spend analyst will tell you, a single cube has its limitations. Why?

There is little point in lumping P-card data together with AP spending, and no point at all in struggling with the mismatch between AP data and commodity-specific invoice-level data. It’s like trying to solve a Rubik’s cube that only pivots in one direction, which is why Spendata lets its users quickly create cubes, explore them, and discard them.

Bottom Line

The old idea of “one cube to rule them all” never works for the analyst. It just works for the “map and dump” service companies that perform data manipulation and mapping offline, then load the result into a hosted BI tool with pre-formatted dashboards and reports. One-cube solutions frustrate those who want the power to create, modify, explore and throw away cubes at will; or change rules, build new dimensions, and add data whenever they want.