Real analysis requires more than just classifying data into a simple hierarchy, rolling up some numbers, and generating a report or two. It requires more than traditional BI tools which, when all is said and done, don’t do much more than that.

Going back to the beginning when we first defined the New Horizons (Part 1 and Part 2), we find that modern AI still doesn’t always support even the foundations that the Spend Master Eric Strovink defined two decades ago.

But that was just the beginning. Today, we also need:

Cube Reverse Engineering and Easy Rule Construction

If the analyst has an existing cube, then the spend analysis system should be capable of extracting all of the rules flawlessly (as well as detecting the classification errors) and presenting them to the user for easy review. If not, then the system should be able to extract all of the rules used for data organization in the underlying systems.

Moreover, for data that still remains unclassified, it should be easy to use multiple AI techniques to generate suggested rules that can be easily accepted, modified, or rejected.

Reusable Cube Template Definitions

That includes derived, aggregated, and federated dimensions as well as pre-defined views that can be easily mapped to existing data sets, edited, and modified to an analyst’s liking. A user should never have to start from scratch when they did a similar analysis before. And tweaking should be as few clicks as possible.

It should also allow for pre-defined cleansing and categorization rules, easy tweaking thereof, and easy integration of Gen-AI for mapping rule suggestions when the built in capabilities are insufficient.

Reusable Filters

It’s not just cubes that need to be reusable, but filters that allow for customized drill-downs into federated hierarchies — it should be super simple to extract, replicated, modify, and save filters that customized cubes and views to exactly what the user needs to see when they need to see it.

Inheritance

By now, you know the big problem with spend analysis is one-cube with one hierarchy based on one schema. A hierarchy defined (on a schema defined) by consensus satisfies no one. A hierarchy defined by an analyst satisfies only the analyst, and only for the problems she is working on at the time.

As a result, in an average organization, whenever analysis needs to be done, a copy of the data is made, another cube is built, another customization performed, another report printed off, another decision made, and when it comes time to revisit the analysis, or rerun it with updated data, no one knows which of the many data sets is the right one to start with, or even where the most recent data from the source apps got pushed to. Because everything is disconnected, analysis makes a mess.

But if the spend analysis application supports inheritance, where cubes can be derived from existing source cubes, and where data updates are automatically propagated down, but not up, then there only needs to be one copy of the source data, and as long as that source is kept up to date, all of the sub-cubes can be kept up to date as well. They don’t need to be tracked, or even maintained when their usefulness has come to an end, as they are just temporary instances a user can create to answer specific questions, where they can manipulate the data, and hierarchy, to their hearts content without impacting any other user in the organization or corrupting the data.

Because they purport to eliminate the need for these capabilities with their advanced capabilities, most AI systems just don’t provide most of these capabilities. But these are the capabilities that true analysis requires.