Twenty years ago, we took a stab at defining analysis with the help of the Grand Master himself in Defining Analysis, the spend slayer Eric Strovink who put pen to paper on what analysis in practice really is.

1. Agility

To this day, a lot of analytics applications are still based on ROLAP (real-time OLAP [online analytical processing]). ROLAP is great for queries that fall within the rigid framework it was defined for, but to ask a question outside of that rigid framework, and to get an answer to that question in a reasonable amount of time, the underlying dataset structure must be changed. That takes time, if it can be accomplished at all as it typically requires the agreement of multiple stakeholders.

But data analysis is an ad-hoc process. To paraphrase Sun Tzu, “no canned report survives
first contact with the analyst.” To perform the queries that support the ad-hoc analysis, the analyst needs to:

• generate new dimensions
• change existing dimensional hierarchies
• map and family new and existing dimensions

and that requires an application that supports real agility.

Especially since the ad-hoc analysis not only requires new dimensions and hierarchies, but new datasets, with new cubes and federations on the fly.

2. Speed

Now, with traditional BI tools, you can theoretically do most of the things a modern spend analysis system can do. Load the data sets, cleanse and categorize with queries, write programs to build reports, and dump data to pivot tables in every Purchaser’s favourite tool (Excel) to get different views But that can take days (or weeks) for even a simple problem.

However, you don’t have days or weeks, and sometimes you don’t even have hours to spare when you need an answer quickly. You need an answer right away, or you need to do the same old, same old or, even worse, trust the AI. So you need a tool that supports speed. Whether that be loading, cleansing and categorization, cube building, derived dimension creation, federation, views, filters, and pivots.

3. Power

Every spend analysis system comes with some built in reporting and claims ad-hoc reporting, which at least allows for built-in report customization, but that is not power. Power is the power you wield as a business user, independent of canned reports supplied by a vendor. The creation of customized complex multi-page analysis, from scratch, should be within your reach with minutes of efforts, without any programming skills or super-human training programs.

In the world of AI, these things still don’t exist.

AIs follow scripts, which can be rather rigid. Even worse, Gen-AI creates a script that may or may not be appropriate before executing it. That’s not agility.

AI is blindingly fast as it’s only limited by the computational speed of the processor(s) you give it, but that’s not speed. To achieve real speed, as per the formal definition, distance must be covered. AI can execute hundreds or thousands of canned analyses and rank potential opportunities, but until they are verified, and the factors AI doesn’t know taken into account, it doesn’t generate real sourcing / procurement opportunities and doesn’t cover the distance. That’s not speed.

AI generates reports as it sees them, not as you want them. Gen-AI interprets your requests based on probabilities, not on realities, and that does not give you the power you need to get the insights you need the way you need them to get the support you need to make real progress. It’s not power, just the illusion thereof.

But even worse, it misses the key point that was implied and now needs to be made explicit.

4. Intelligence

It doesn’t have any. The whole point of agility, speed, and power was to support the intuition and insight of an intelligent human who can figure out new ways of looking at data to find new opportunities. Without intelligence, systems are useless. And in the age of AI, the lack of intelligence reinforces the need, and meaning of, analysis more than ever.