Where's The Analysis?It seems to us that the user of a "spend analysis" system should be able to — well — analyze spend. Unfortunately, most spend analysis systems are really just remote data warehouses that store spend
- Extracting and downloading raw transactions to your local PC.
- Hacking at them with desktop tools until you've got them organized properly.
- Building a custom report by hand from the re-organized data.
- Repeating steps 1-3 for the next analysis.
Does this sound familiar? It should, because it's exactly what you did before you bought the spend analysis system.
We think this "back to the future" approach is valueless, so with BIQ we do things very differently. Steps 1 and 2 are unnecessary, because you can organize your dataset any way you want — or build many different datasets, each organized differently — with ease. Building a new dimension is easy. Changing an existing hierarchy is instantaneous. Map spend whenever you want. Most importantly, we eliminate step 3 by making it possible to pull BIQ's analysis engine results right into your Excel data models.
What's key is that BIQ analyses can be repeated again and again, like a spreadsheet analysis, except more flexibly. So, run an analysis. Re-map the data. Run the analysis again. Change filter positions. Run it again. It's lather-rinse-repeat, just like a spreadsheet model.
So why pay a six-digit price for a system that lands you right back where you started, analyzing raw transactions at your desktop, with primitive tools? Maybe the value justification is in the cleansed data (see Supplier Familying: Behind the Hype), or in the commodity map (see Mapping Spend: Three Easy Steps), or in building the spend dataset in the first place (see Building Datasets: Experts Need Not Apply), or in tight integration with a sourcing or e-commerce suite (see Suite Silliness) — but we don't think so. Follow the links for our reasoning.
Some years ago, when the ideas behind BIQ were first germinating, our team examined the web logs of an industry-leading spend analysis tool. The usage pattern was not unlike this picture:
When we drilled into the spikes to find out what was going on, the activity was almost all raw transaction downloads.
This amazed us at the time, but on reflection it makes perfect sense — users waited for the monthly refresh, grabbed the updated raw data, and then ignored the tool for the remainder of the month, busying themselves with their own desktop analyses.
This exposes the fallacy behind data warehouse-based "spend analysis" systems — the "analysis" is occurring exactly the way it always has, with desktop tools, on individual analysts' work stations. The "spend analysis" system is simply a storage medium for spend data, not an "analysis" system at all.